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diff --git a/p3/preprocess/Asthma/code/GSE123086.py b/p3/preprocess/Asthma/code/GSE123086.py new file mode 100644 index 0000000000000000000000000000000000000000..161477fe077414835fce2fdb649ce475c55b339b --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE123086.py @@ -0,0 +1,240 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE123086" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE123086" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE123086.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE123086.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE123086.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes, this dataset contains gene expression data from CD4+ T cells using microarray +is_gene_available = True + +# 2.1 Data Availability +# Trait is in Feature 1 (primary diagnosis), values include ASTHMA and others +trait_row = 1 + +# Gender is in Feature 2 and 3 (Sex appears in both) +gender_row = 2 + +# Age appears in Features 3 and 4 +age_row = 3 + +# 2.2 Data Type Conversion Functions + +def convert_trait(value: str) -> int: + """Convert trait values to binary (0 for control, 1 for case)""" + if not isinstance(value, str): + return None + value = value.split(': ')[-1].upper() + if 'ASTHMA' in value: + return 1 + elif value == 'HEALTHY_CONTROL': + return 0 + return None + +def convert_age(value: str) -> float: + """Convert age values to continuous numeric""" + if not isinstance(value, str): + return None + if not value.startswith('age: '): + return None + try: + return float(value.split(': ')[-1]) + except: + return None + +def convert_gender(value: str) -> int: + """Convert gender values to binary (0 for female, 1 for male)""" + if not isinstance(value, str): + return None + if not value.startswith('Sex: '): + return None + value = value.split(': ')[-1].upper() + if value == 'FEMALE': + return 0 + elif value == 'MALE': + return 1 + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + print("Preview of extracted clinical features:") + print(preview_df(clinical_features)) + + # Save clinical features + clinical_features.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +requires_gene_mapping = True +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview annotation dataframe structure +print("Gene Annotation Preview:") +print("Column names:", gene_annotation.columns.tolist()) +print("\nFirst few rows as dictionary:") +print(preview_df(gene_annotation)) +# The IDs in gene annotation correspond to the row IDs in gene expression data +# The ENTREZ_GENE_ID contains IDs that we first map to +mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID') + +# Apply the mapping to convert probe expression to gene expression +entrez_data = apply_gene_mapping(gene_data, mapping_df) + +# Convert Entrez IDs to gene symbols using NCBI synonym database +gene_data = normalize_gene_symbols_in_index(entrez_data) + +# Save the gene expression data +gene_data.to_csv(out_gene_data_file) +# Load previously saved data +gene_data = pd.read_csv(out_gene_data_file, index_col=0) +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) + +# Inspect data alignment +print("Clinical data shape:", clinical_data.shape) +print("Gene data shape:", gene_data.shape) +print("Clinical data columns:", clinical_data.columns[:5]) +print("Gene data columns:", gene_data.columns[:5]) + +# Transpose data to get samples in rows, genes in columns +clinical_data = clinical_data.T +gene_data = gene_data.T +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, 'Asthma') + +# 4. Evaluate bias +is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Asthma') + +# 5. Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls." +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) +# Load previously saved data +gene_data = pd.read_csv(out_gene_data_file, index_col=0) +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) + +# Verify data validity +if gene_data.empty: + print("Gene expression data is empty. Previous preprocessing steps likely failed.") + is_gene_available = False + is_trait_available = True + validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available, + is_biased=False, # Set a definite value + df=clinical_data, # Provide the clinical data + note="Gene expression data processing failed, resulting in empty data." + ) +else: + # Link clinical and genetic data + linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + + # Handle missing values + linked_data = handle_missing_values(linked_data, trait) + + # Evaluate bias + is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + + # Validate and save cohort info + is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains gene expression data from CD4+ T cells comparing asthma patients with healthy controls." + ) + + # Save linked data if usable + if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE123088.py b/p3/preprocess/Asthma/code/GSE123088.py new file mode 100644 index 0000000000000000000000000000000000000000..07a68a0408385210517b4f26aa6a2343909b9a53 --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE123088.py @@ -0,0 +1,283 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE123088" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE123088" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE123088.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE123088.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE123088.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# CD4+ T cells data likely contains gene expression, not just miRNA or methylation +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 1 # 'primary diagnosis' contains trait info +age_row = 3 # 'age' data starts at feature 3 and continues in feature 4 +gender_row = 2 # 'Sex' info is in feature 2 + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + if pd.isna(value): + return None + value = value.split(': ')[1] + # Convert values to binary (0: control, 1: asthma) + if value in ['HEALTHY_CONTROL', 'Control']: + return 0 + elif value in ['ASTHMA']: + return 1 + return None + +def convert_age(value): + if pd.isna(value): + return None + try: + # Extract numeric age value after colon + age = int(value.split(': ')[1]) + return age + except: + return None + +def convert_gender(value): + if pd.isna(value): + return None + if not value.startswith('Sex:'): + return None + value = value.split(': ')[1] + # Convert to binary (0: female, 1: male) + if value.upper() == 'FEMALE': + return 0 + elif value.upper() == 'MALE': + return 1 + return None + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the processed clinical data + preview = preview_df(selected_clinical) + print("Preview of processed clinical data:") + print(preview) + + # Save to CSV + selected_clinical.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# The identifiers appear to be numeric IDs from 1-24166 +# These are not gene symbols and will need mapping +requires_gene_mapping = True +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract platform info from SOFT file line by line +import gzip +import re +platform_lines = [] +gene_symbol_lines = [] +with gzip.open(soft_file, 'rt') as f: + in_platform_block = False + for line in f: + if line.startswith('!Platform_'): + platform_lines.append(line.strip()) + if 'table_begin' in line: + in_platform_block = True + # Skip header line + next(f) + continue + elif 'table_end' in line: + in_platform_block = False + continue + elif in_platform_block: + gene_symbol_lines.append(line.strip()) + +# Parse platform annotation table +import pandas as pd +import io + +platform_table = pd.read_csv(io.StringIO('\n'.join(gene_symbol_lines)), sep='\t') + +# Preview annotation data +print("Platform Information:") +for line in platform_lines[:5]: + print(line) + +print("\nPlatform Annotation Table Preview:") +print("Column names:", platform_table.columns.tolist()) +print("\nFirst few rows:") +print(preview_df(platform_table)) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Look through entire SOFT file for gene symbol information +import gzip +platform_lines = [] +gene_table_lines = [] +with gzip.open(soft_file, 'rt') as f: + in_gene_table = False + table_found = False + for line in f: + # Keep track of platform lines for debugging + if line.startswith('!Platform_'): + platform_lines.append(line.strip()) + if 'table_begin' in line: + in_gene_table = True + # Get header line + header = next(f).strip() + gene_table_lines.append(header) + continue + elif 'table_end' in line: + in_gene_table = False + elif in_gene_table and ('gene_symbol' in line.lower() or 'gene_name' in line.lower() or + 'symbol' in line.lower() or 'gene_assignment' in line.lower()): + table_found = True + gene_table_lines.append(line.strip()) + +print("Platform information:") +for line in platform_lines[:10]: + print(line) + +print("\nFirst few gene table lines (if gene symbols found):") +if gene_table_lines: + for line in gene_table_lines[:5]: + print(line) + +print("\nSearching for alternative annotation fields...") +# Extract gene annotation trying both methods +gene_annotation = get_gene_annotation(soft_file) + +# Preview annotation dataframe structure +print("\nGene Annotation Preview:") +print("Column names:", gene_annotation.columns.tolist()) +print("\nFirst few rows as dictionary (showing all columns):") +pd.set_option('display.max_columns', None) +print(gene_annotation.head().to_dict('records')) +# Create mapping dataframe using platform's annotation +mapping_data = gene_annotation.loc[:, ['ID', 'ENTREZ_GENE_ID']].dropna() + +# Create a list of genes for the Gene column, single gene ID per row +mapping_data['Gene'] = mapping_data['ENTREZ_GENE_ID'].map(lambda x: [str(int(float(x)))] if pd.notnull(x) and float(x) > 0 else []) +mapping_data = mapping_data.drop(columns=['ENTREZ_GENE_ID']) + +# Apply gene mapping to transform probe-level data to gene-level data +gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data) + +# Normalize gene symbols to consistent format +gene_data = normalize_gene_symbols_in_index(gene_data) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) +# Get file paths and read data +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Get gene annotation and expression data +gene_annotation = get_gene_annotation(soft_file) +gene_data = get_genetic_data(matrix_file) + +# Clean and create mapping dataframe +gene_annotation = gene_annotation[gene_annotation['ENTREZ_GENE_ID'].str.isnumeric().fillna(False)] +mapping_data = gene_annotation.loc[:, ['ID', 'ENTREZ_GENE_ID']].dropna() +mapping_data['Gene'] = mapping_data['ENTREZ_GENE_ID'].map(lambda x: [str(int(float(x)))] if pd.notnull(x) and float(x) > 0 else []) +mapping_data = mapping_data.drop(columns=['ENTREZ_GENE_ID']) + +# Apply gene mapping to transform probe-level data to gene-level data +gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data) + +# Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# Link clinical and genetic data +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# Evaluate bias +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains gene expression data from CD4+ T cells." +) + +# Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE182797.py b/p3/preprocess/Asthma/code/GSE182797.py new file mode 100644 index 0000000000000000000000000000000000000000..53cfde06d51234fa313fe72dd4625b44208415fe --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE182797.py @@ -0,0 +1,185 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE182797" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE182797" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE182797.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE182797.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE182797.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Based on series title and overall design, this dataset contains transcriptomic data from nasal biopsies +is_gene_available = True + +# 2.1 Data Availability +# trait data is in Feature 0 (diagnosis) +trait_row = 0 + +# gender data is in Feature 1 but only contains females +gender_row = None # Not useful since all subjects are female + +# age data is in Feature 2 +age_row = 2 + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + value = x.split(': ')[-1].lower() + if 'adult-onset asthma' in value: + return 1 + elif 'healthy' in value: + return 0 + return None + +def convert_age(x): + if not isinstance(x, str): + return None + try: + value = x.split(': ')[-1] + return float(value) + except: + return None + +def convert_gender(x): + # This function won't be used but included for completeness + if not isinstance(x, str): + return None + value = x.split(': ')[-1].lower() + if 'female' in value: + return 0 + elif 'male' in value: + return 1 + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +# Since trait_row is not None, we need to extract clinical features +selected_clinical = geo_select_clinical_features(clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age) + +# Preview the extracted features +print("Preview of extracted clinical features:") +print(preview_df(selected_clinical)) + +# Save clinical data +selected_clinical.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# These are Agilent probe identifiers starting with A_19_P, NOT human gene symbols +requires_gene_mapping = True +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview annotation dataframe structure +print("Gene Annotation Preview:") +print("Column names:", gene_annotation.columns.tolist()) +print("\nFirst few rows as dictionary:") +print(preview_df(gene_annotation)) +# 1. Based on the dataframes, 'ID' column contains probe identifiers, and 'GENE_SYMBOL' contains gene symbols +prob_col = 'ID' +gene_col = 'GENE_SYMBOL' + +# 2. Get gene mapping dataframe +mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col) + +# 3. Convert probe-level measurements to gene expression +gene_data = apply_gene_mapping(gene_data, mapping_data) + +# Print dimensions and first few rows to verify the mapping +print("Shape of gene expression data after mapping:", gene_data.shape) +print("\nFirst few rows of mapped data:") +print(gene_data.head()) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Evaluate bias +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains RNA transcriptome data in human sinonasal epithelial cells." +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE182798.py b/p3/preprocess/Asthma/code/GSE182798.py new file mode 100644 index 0000000000000000000000000000000000000000..37b386ce569ce9a093a168b1e6415f2b417f43c6 --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE182798.py @@ -0,0 +1,178 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE182798" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE182798" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE182798.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE182798.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE182798.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# The dataset contains PBMC and Nasal biopsy samples, which are suitable for gene expression profiling +is_gene_available = True + +# 2.1 Data Row Identification +trait_row = 0 # diagnosis is in row 0 +age_row = 2 # age is in row 2 +gender_row = 1 # gender is in row 1, but all female so mark as None since constant +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + value = x.split(': ')[1].lower() + if 'adult-onset asthma' in value: + return 1 + elif 'healthy' in value: + return 0 + return None + +def convert_age(x): + if pd.isna(x): + return None + try: + return float(x.split(': ')[1]) + except: + return None + +def convert_gender(x): + if pd.isna(x): + return None + value = x.split(': ')[1].lower() + if 'female' in value: + return 0 + elif 'male' in value: + return 1 + return None + +# 3. Save Metadata +is_usable = validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features(clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + + # Preview the data + preview = preview_df(clinical_features) + print("Clinical Features Preview:", preview) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Looking at the identifiers, they start with "A_19_P" followed by numbers +# These are Agilent microarray probe IDs, not human gene symbols +requires_gene_mapping = True +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview annotation dataframe structure +print("Gene Annotation Preview:") +print("Column names:", gene_annotation.columns.tolist()) +print("\nFirst few rows as dictionary:") +print(preview_df(gene_annotation)) +# 1. Create gene mapping dataframe from annotation +mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL') + +# 2. Apply gene mapping to convert probe-level measurements to gene expression values +gene_data = apply_gene_mapping(gene_data, mapping_data) + +# 3. Print a quick preview of the mapped gene data +print("Gene expression data after mapping:") +print("Shape:", gene_data.shape) +print("\nFirst few rows:") +print(gene_data.head()) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Evaluate bias +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains RNA transcriptome data in human sinonasal epithelial cells." +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE184382.py b/p3/preprocess/Asthma/code/GSE184382.py new file mode 100644 index 0000000000000000000000000000000000000000..ed92d55b4a459b74dfed17c3f4e023be0ac170e6 --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE184382.py @@ -0,0 +1,66 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE184382" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE184382" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE184382.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE184382.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE184382.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Based on series title and summary, this dataset contains miRNA data rather than gene expression data +is_gene_available = False + +# 2. Variable Availability and Data Type Conversion +# 2.1 Check data availability from sample characteristics +trait_row = None # Cannot reliably determine asthma status from AIT treatment alone +age_row = None # Age data not available +gender_row = None # Gender data not available + +# 2.2 Define conversion functions (though data not available in this case) +def convert_trait(x): + return None + +def convert_age(x): + return None + +def convert_gender(x): + return None + +# 3. Save metadata about dataset usability +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +# Skip this step since trait_row is None \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE185658.py b/p3/preprocess/Asthma/code/GSE185658.py new file mode 100644 index 0000000000000000000000000000000000000000..626cd43bdb67f93e9d6cbbef5a7062e3f0e8a016 --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE185658.py @@ -0,0 +1,174 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE185658" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE185658" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE185658.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE185658.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE185658.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes, this dataset contains microarray data for gene expression (see Series_summary) +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability and Location +trait_row = 1 # 'group' feature contains asthma status +age_row = None # Age data not available +gender_row = None # Gender data not available + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert asthma status to binary: 1 for asthma, 0 for healthy""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip() + if 'Asthma' in value: # Both AsthmaHDM and AsthmaHDMNeg are asthma cases + return 1 + elif value == 'Healthy': + return 0 + return None + +convert_age = None # No age data +convert_gender = None # No gender data + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + # Extract clinical features + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted features + print("Preview of clinical features:") + print(preview_df(clinical_features)) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# The identifiers are numeric IDs like '7892501', not standard gene symbols +# These appear to be probe IDs from a microarray platform that need to be mapped to gene symbols +requires_gene_mapping = True +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview annotation dataframe structure +print("Gene Annotation Preview:") +print("Column names:", gene_annotation.columns.tolist()) +print("\nFirst few rows as dictionary:") +print(preview_df(gene_annotation)) +# 1. Identify mapping columns from gene annotation +# 'ID' column in annotation matches probe IDs in gene expression data +# 'gene_assignment' contains gene symbols and annotations +prob_col = 'ID' +gene_col = 'gene_assignment' + +# 2. Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col) + +# 3. Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(gene_data, mapping_data) + +# Print the shape and preview of mapped gene data +print("\nShape of mapped gene expression data:", gene_data.shape) +print("\nFirst few rows of mapped gene expression data:") +print(gene_data.head()) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Evaluate bias +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains RNA transcriptome data in human sinonasal epithelial cells." +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE188424.py b/p3/preprocess/Asthma/code/GSE188424.py new file mode 100644 index 0000000000000000000000000000000000000000..26d523952e79e4125ed33e8d3dda17842ed8671a --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE188424.py @@ -0,0 +1,146 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE188424" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE188424" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE188424.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE188424.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE188424.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes, this dataset contains gene expression data from human blood samples, +# not just miRNA or methylation +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = None # Not available in characteristics dictionary +gender_row = 0 # Gender data is available at index 0 +age_row = None # No age data available + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + # Won't be used since trait data is not available + return None + +def convert_gender(value): + if not isinstance(value, str): + return None + value = value.lower().split(': ')[-1].strip() + if value == 'female': + return 0 + elif value == 'male': + return 1 + return None + +def convert_age(value): + # Won't be used since age data is not available + return None + +# 3. Save Metadata - Initial Filtering +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False # trait_row is None +) + +# 4. Clinical Feature Extraction +# Skip this step since trait_row is None +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# These are Illumina probe IDs (starting with ILMN_) and need to be mapped to gene symbols +requires_gene_mapping = True +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview annotation dataframe structure +print("Gene Annotation Preview:") +print("Column names:", gene_annotation.columns.tolist()) +print("\nFirst few rows as dictionary:") +print(preview_df(gene_annotation)) +# 1. Identify mapping columns from gene annotation data +# 'ID' column matches the probe IDs (ILMN_) in expression data +# 'Symbol' column contains the gene symbols to map to +prob_col = 'ID' +gene_col = 'Symbol' + +# 2. Get gene mapping dataframe +mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col) + +# 3. Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(gene_data, mapping_df) + +# Print shape and preview mapped data +print("\nShape of gene-level expression data:", gene_data.shape) +print("\nPreview of gene-level expression data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Update cohort info - dataset not usable due to missing trait data +validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, # No trait data available + is_biased=True, # Dataset unusable without trait data + df=gene_data, # Pass gene expression data + note="Dataset contains gene expression data but lacks trait information needed for association studies." +) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE205151.py b/p3/preprocess/Asthma/code/GSE205151.py new file mode 100644 index 0000000000000000000000000000000000000000..2163d27277d94e524a25ddd2cfd81dcb1b6795d7 --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE205151.py @@ -0,0 +1,153 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE205151" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE205151" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE205151.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE205151.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE205151.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Yes, this dataset contains gene expression data from Nanostring array +is_gene_available = True + +# 2.1. Data Availability +# Use cluster information as trait data since it represents asthma phenotypes +trait_row = 1 # Feature 1 contains cluster info +age_row = None # Age not explicitly provided +gender_row = None # Gender not provided + +# 2.2. Data Type Conversion Functions +def convert_trait(x): + if pd.isna(x): + return None + # Extract cluster number after colon and convert to binary + # cluster 1 -> 0, cluster 2 -> 1 + try: + cluster = int(x.split(':')[1].strip()) + if cluster == 1: + return 0 + elif cluster == 2: + return 1 + return None + except: + return None + +def convert_age(x): + # Not used since age data unavailable + return None + +def convert_gender(x): + # Not used since gender data unavailable + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + selected_clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait + ) + + # Preview the extracted features + print("Preview of extracted clinical features:") + print(preview_df(selected_clinical_df)) + + # Save to CSV + selected_clinical_df.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# Looking at the gene identifiers in the first few rows, they appear to be valid human gene symbols +# For example, ABCB1, ABCF1, ABL1, ADA, AHR are all standard human gene symbols +# The ID column contains recognized official human gene symbols that do not need mapping +requires_gene_mapping = False +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Evaluate bias +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains RNA transcriptome data in human sinonasal epithelial cells." +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE230164.py b/p3/preprocess/Asthma/code/GSE230164.py new file mode 100644 index 0000000000000000000000000000000000000000..6f68dc5690a5511f6c8272b944efc3acc49401d2 --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE230164.py @@ -0,0 +1,144 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE230164" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE230164" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE230164.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE230164.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE230164.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# 1. Gene Expression Data Availability +# Since "Gene expression profiling" is mentioned in series title, +# and series summary indicates this is a SuperSeries containing SubSeries, +# this dataset is likely to contain gene expression data +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Based on sample characteristics, we can find: +# - Gender is available at key 0 +# - Trait and age information are not explicitly available +trait_row = None +age_row = None +gender_row = 0 + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + return None # Not available + +def convert_age(x): + return None # Not available + +def convert_gender(x): + # Extract value after colon and convert to binary + if not isinstance(x, str): + return None + value = x.split(': ')[-1].lower() + if value == 'female': + return 0 + elif value == 'male': + return 1 + return None + +# 3. Save metadata about data availability +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Since trait_row is None, skip clinical feature extraction +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# The identifiers start with ILMN_ which indicates they are Illumina probe IDs +# These need to be mapped to human gene symbols for standardization +requires_gene_mapping = True +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene annotation from SOFT file +gene_annotation = get_gene_annotation(soft_file) + +# Preview annotation dataframe structure +print("Gene Annotation Preview:") +print("Column names:", gene_annotation.columns.tolist()) +print("\nFirst few rows as dictionary:") +print(preview_df(gene_annotation)) +# 1. Based on the preview, 'ID' in annotation matches probe IDs in expression data (ILMN_*), +# and 'Symbol' contains gene symbols + +# 2. Extract ID and Symbol columns to create mapping dataframe +gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol') + +# 3. Apply gene mapping to convert probe-level measurements to gene-level expression +gene_data = apply_gene_mapping(gene_data, gene_mapping) +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# Create a minimal DataFrame for validation +linked_data = gene_data.T # Transpose to have samples as rows + +# Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=False, + is_biased=True, # No trait data means it's biased by definition + df=linked_data, + note="Dataset contains gene expression data but lacks required trait information." +) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/GSE270312.py b/p3/preprocess/Asthma/code/GSE270312.py new file mode 100644 index 0000000000000000000000000000000000000000..c284808027d59f925cc032d17bd2b6b4bc365fd2 --- /dev/null +++ b/p3/preprocess/Asthma/code/GSE270312.py @@ -0,0 +1,149 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" +cohort = "GSE270312" + +# Input paths +in_trait_dir = "../DATA/GEO/Asthma" +in_cohort_dir = "../DATA/GEO/Asthma/GSE270312" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/GSE270312.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE270312.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE270312.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract background info and clinical data +background_info, clinical_data = get_background_and_clinical_data(matrix_file) + +# Get unique values per clinical feature +sample_characteristics = get_unique_values_by_row(clinical_data) + +# Print background info +print("Dataset Background Information:") +print(f"{background_info}\n") + +# Print sample characteristics +print("Sample Characteristics:") +for feature, values in sample_characteristics.items(): + print(f"Feature: {feature}") + print(f"Values: {values}\n") +# Data type conversion functions +def convert_trait(value: str) -> Optional[int]: + if not value or ':' not in value: + return None + asthma_status = value.split(':')[1].strip().lower() + if 'yes' in asthma_status: + return 1 + elif 'no' in asthma_status: + return 0 + return None + +def convert_gender(value: str) -> Optional[int]: + if not value or ':' not in value: + return None + gender = value.split(':')[1].strip().lower() + if 'female' in gender: + return 0 + elif 'male' in gender: + return 1 + return None + +# Gene expression data availability +is_gene_available = True # RNA transcriptome data available + +# Variable row identification +trait_row = 3 # 'asthma status' +gender_row = 2 # 'gender' +age_row = None # Age not available in characteristics + +# Initial validation and save metadata +is_trait_available = trait_row is not None +is_usable = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# Clinical feature extraction since trait_row is not None +selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview and save clinical data +preview_result = preview_df(selected_clinical) +print("Preview of clinical data:", preview_result) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +selected_clinical.to_csv(out_clinical_data_file) +# Get file paths +soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir) + +# Extract gene expression data from matrix file +gene_data = get_genetic_data(matrix_file) + +# Print first 20 row IDs and shape of data to help debug +print("Shape of gene expression data:", gene_data.shape) +print("\nFirst few rows of data:") +print(gene_data.head()) +print("\nFirst 20 gene/probe identifiers:") +print(gene_data.index[:20]) + +# Inspect a snippet of raw file to verify identifier format +import gzip +with gzip.open(matrix_file, 'rt', encoding='utf-8') as f: + lines = [] + for i, line in enumerate(f): + if "!series_matrix_table_begin" in line: + # Get the next 5 lines after the marker + for _ in range(5): + lines.append(next(f).strip()) + break +print("\nFirst few lines after matrix marker in raw file:") +for line in lines: + print(line) +# The gene IDs appear to be valid human gene symbols like ABCF1, ACE, ACKR2, ACKR3, etc. +# The IDs match with official HGNC gene symbols, so no mapping is needed +requires_gene_mapping = False +# 1. Normalize gene symbols +gene_data = normalize_gene_symbols_in_index(gene_data) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_data = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Evaluate bias +is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Validate and save cohort info +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_biased, + df=linked_data, + note="Dataset contains RNA transcriptome data in human sinonasal epithelial cells." +) + +# 6. Save linked data if usable +if is_usable: + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Asthma/code/TCGA.py b/p3/preprocess/Asthma/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..d1b622adcc7c1230b7bff34fd18e4f99f5eb546e --- /dev/null +++ b/p3/preprocess/Asthma/code/TCGA.py @@ -0,0 +1,34 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Asthma" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Asthma/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Asthma/cohort_info.json" + +# Review available cohorts for asthma relevance +tcga_dirs = os.listdir(tcga_root_dir) +# Filter out non-directory files +tcga_dirs = [d for d in tcga_dirs if os.path.isdir(os.path.join(tcga_root_dir, d))] + +# For asthma trait, none of the TCGA cancer cohorts are directly relevant +print(f"No suitable TCGA cancer cohort was found for the trait: {trait}") + +# Save cohort info to mark this trait as completed +_ = validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=False, + is_trait_available=False +) +# Exit preprocessing as no suitable data exists +clinical_df = None +genetic_df = None \ No newline at end of file diff --git a/p3/preprocess/Asthma/gene_data/GSE123086.csv b/p3/preprocess/Asthma/gene_data/GSE123086.csv new file mode 100644 index 0000000000000000000000000000000000000000..40e99c384bb43d1672a87f1b197f36c763b02211 --- /dev/null +++ b/p3/preprocess/Asthma/gene_data/GSE123086.csv @@ -0,0 +1 @@ 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6,DNAJC25,DNAJC3,DNALI1,DND1P1,DNM1,DNM1P41,DNM3-IT1,DNM3OS,DNPH1,DOC2B,DOCK1,DOCK10,DOCK2,DOCK4,DOCK5,DOCK6,DOCK8,DOK1,DOK2,DOK5,DPEP2,DPH6,DPP4,DPRXP4,DPY19L1,DPY19L1P1,DPY19L2,DPY19L2P4,DPYD,DPYD-AS2,DPYS,DPYSL2,DPYSL3,DRAM1,DSC1,DSC2,DSC3,DSE,DSEL,DSG2,DSP,DST,DSTN,DSTNP2,DTD2,DTHD1,DTNA,DTX1,DTX2P1-UPK3BP1-PMS2P11,DTX3L,DTX4,DUS1L,DUSP1,DUSP10,DUSP14,DUSP16,DUSP18,DUSP2,DUSP3,DUSP4,DUSP5,DUSP5P1,DUSP6,DUXAP10,DUXAP9,DYNC2H1,DYNC2LI1,DYNLL2,DYNLT1,DYNLT3,DYRK1A,DYRK2,DYRK3,DYSF,DZIP1,E2F5,EAF2,EBF2,EBF3,EBLN2,EBLN3P,EBPL,ECE1,ECEL1P2,ECHDC1,ECHDC2,ECHDC3,ECM1,ECRG4,EDA2R,EDEM1,EDIL3,EDN1,EDNRA,EDNRB,EEA1,EED,EEF2K,EEIG1,EEIG2,EEPD1,EFCAB13,EFCAB6,EFCAB7,EFEMP2,EFHC1,EFHD1,EFNA5,EFNB1,EFNB2,EFNB3,EGFEM1P,EGFL6,EGFL7,EGFL8,EGFLAM,EGFR,EGLN1,EGR1,EGR2,EHD3,EHD4,EHHADH,EHMT1,EID3,EIF1AY,EIF2AK3,EIF4EBP1,EIF4EBP3,ELAPOR2,ELF4,ELFN1,ELFN1-AS1,ELK1,ELK3,ELL2,ELMO1,ELN,ELOA-AS1,ELOA3BP,ELOB,ELOVL2,ELOVL4,ELOVL6,ELOVL7,EMB,EMBP1,EMCN,EMILIN1,EMILIN2,EML1,EML4,EMP3,EMX2,ENC1,ENGASE,ENO1-AS1,ENOX1,ENPEP,ENPP1,ENPP5,ENTPD1,ENTPD3-AS1,ENTPD4,ENTREP1,EOLA1-DT,EOLA2-DT,EOMES,EP300-AS1,EP400P1,EPB41,EPB41L1,EPB41L3,EPB41L4A,EPB41L4B,EPB41L5,EPB42,EPCIP,EPCIP-AS1,EPDR1,EPGN,EPHA3,EPHA4,EPHA7,EPHB4,EPHB6,EPN2-IT1,EPS15,EPSTI1,ERAP1,ERAP2,ERBB2,ERBB4,ERG,ERI2,ERI3-IT1,ERICH5,ERICH6-AS1,ERMN,ERMP1,ERN1,ERO1A,ERP27,ERRFI1,ERV3-1,ERVK13-1,ESAM,ESCO1,ESF1,ESYT1,ETAA1,ETFB,ETFDH,ETNK1,ETNPPL,ETS1,ETS2,ETV3,ETV5,ETV6,EVA1C,EVC,EVC2,EVI2A,EVI2B,EVL,EXOC6,EXOG,EXOSC5,EXOSC8,EXT1,EXTL2,EYA1,EYA2,EYA4,EZH2,EZR,F10,F13A1,F2RL3,F3,F5,F8,FAAH,FAAH2,FABP3,FABP5,FADS1,FADS3,FAH,FAHD1,FAHD2A,FAHD2B,FAHD2CP,FAIM,FAIM2,FAM106B,FAM106C,FAM107A,FAM107B,FAM110B,FAM110C,FAM111A,FAM111B,FAM114A1,FAM117A,FAM117B,FAM118A,FAM120AOS,FAM124B,FAM133B,FAM133CP,FAM133DP,FAM138C,FAM138D,FAM138E,FAM13A,FAM13A-AS1,FAM13C,FAM149A,FAM153A,FAM153B,FAM153CP,FAM157A,FAM157B,FAM161A,FAM162B,FAM169A,FAM171A1,FAM171A2,FAM177B,FAM182B,FAM184A,FAM197Y6,FAM200A,FAM200B,FAM209A,FAM20A,FAM20C,FAM216A,FAM216B,FAM218A,FAM219A,FAM219B,FAM21EP,FAM220A,FAM221A,FAM224B,FAM225A,FAM228A,FAM228B,FAM241A,FAM24B,FAM25A,FAM25BP,FAM25C,FAM27E3,FAM27E5,FAM30A,FAM3C,FAM41C,FAM43A,FAM47A,FAM47C,FAM47E,FAM47E-STBD1,FAM66A,FAM66D,FAM72C,FAM72D,FAM78A,FAM85A,FAM86B1,FAM86B2,FAM86B3P,FAM86C2P,FAM86DP,FAM86EP,FAM86FP,FAM86HP,FAM87B,FAM89A,FAM89B,FAM90A20,FAM91A1,FAM95C,FAM99B,FANCB,FANCD2,FANCI,FANCL,FAP,FAR1,FAR2,FAR2P2,FAR2P3,FAS,FASLG,FASN,FAT1,FAT2,FAT4,FAXDC2,FBLN1,FBLN2,FBLN5,FBLN7,FBP1,FBRS,FBXL13,FBXL2,FBXL7,FBXO16,FBXO17,FBXO27,FBXO28,FBXO31,FBXO32,FBXO36,FBXO4,FBXO46,FBXW11,FBXW4,FBXW4P1,FCER1A,FCER1G,FCER2,FCGBP,FCGR1A,FCGR1BP,FCGR2A,FCGR2B,FCGR3A,FCGRT,FCHO1,FCHO2,FCHSD1,FCHSD2,FCMR,FCN1,FCRL1,FCRL2,FCRL3,FCRL5,FCRLA,FDCSP,FDPS,FEM1A,FERMT1,FERMT3,FERRY3,FEZ1,FFAR3,FFAR4,FGD2,FGD3,FGD4,FGD5,FGD6,FGF10,FGF10-AS1,FGF14,FGF14-AS1,FGF7,FGF7P6,FGF9,FGFBP2,FGFR1,FGFR1OP2,FGFR2,FGFRL1,FGL2,FHIP1B,FHIT,FHL1,FHL2,FHL5,FHOD3,FIBIN,FIGN,FIGNL1,FILIP1,FILIP1L,FIRRM,FITM2,FKBP10,FKBP11,FKBP14,FKBP15,FKBP1A,FKBP2,FKBP5,FKBP8,FKBP9P1,FKSG29,FKTN,FLI1,FLJ20712,FLJ32255,FLJ37201,FLJ42393,FLJ42627,FLJ45513,FLNA,FLNC,FLRT2,FLRT3,FLT1,FLT3LG,FLT4,FLVCR1,FLYWCH1,FMC1-LUC7L2,FMN1,FMNL1,FMNL2,FMNL3,FMO1,FMO3,FMO4,FN3KRP,FNBP1,FNBP4,FNDC1,FNDC4,FNDC5,FNIP2,FOCAD,FOLH1,FOLR2,FOS,FOSB,FOSL2,FOXC1,FOXD3-AS1,FOXD4,FOXD4L6,FOXJ2,FOXN2,FOXN3,FOXN3-AS1,FOXO1,FOXO3,FOXO4,FOXP1,FOXP1-IT1,FOXP2,FOXRED2,FPR1,FPR3,FRA10AC1,FRAS1,FRAT2,FREM1,FRG1,FRG1BP,FRG1JP,FRK,FRMD4B,FRMD6,FRMD6-AS1,FRRS1,FRY,FRZB,FSCN1,FSHR,FST,FSTL1,FSTL3,FTCDNL1,FTH1,FTH1P3,FTSJ3,FTX,FUCA1,FUCA2,FUOM,FUS,FUT4,FXN,FXYD1,FXYD5,FXYD6-FXYD2,FYB1,FYB2,FYN,FZD1,FZD5,FZD7,FZD8,G2E3,G6PC3,GAA,GAB2,GAB3,GABARAPL1,GABRA2,GABRB2,GABRE,GADD45A,GADD45B,GADD45G,GADL1,GAGE2E,GAGE4,GAL3ST2,GALM,GALNT1,GALNT12,GALNT15,GALNT16,GALNT18,GALNT3,GALNT4,GALNT9,GALT,GANC,GAPT,GAR1,GAREM1,GARIN1A,GAS1,GAS1RR,GAS2L3,GAS5,GAS5-AS1,GAS6,GAS6-DT,GAS7,GASK1A,GASK1B,GATA3,GATA4,GATA6,GATA6-AS1,GATB,GATC,GATM,GBE1,GBP1,GBP2,GBP3,GBP4,GBP5,GBP7,GCA,GCDH,GCH1,GCLC,GCLM,GCNT1,GCNT2,GCNT4,GCOM1,GCSAM,GCSH,GCSHP3,GDAP1,GDF10,GDPD1,GDPD5,GEM,GEN1,GFOD1,GFPT2,GFRA1,GFRA2,GFRA3,GGA2,GGCT,GGCX,GGT5,GGTA2P,GGTLC2,GHR,GHRLOS,GIMAP1,GIMAP1-GIMAP5,GIMAP2,GIMAP4,GIMAP5,GIMAP7,GIN1,GINS3,GIPC2,GIPC3,GJA1,GJA4,GJA5,GK5,GLB1L,GLCCI1,GLDN,GLI3,GLIPR1,GLIPR1L2,GLIPR2,GLIS2,GLMP,GLP2R,GLRB,GLS,GLT8D2,GLUD1,GLUD2,GLUL,GLYAT,GLYCTK,GLYR1,GMCL1,GMDS-DT,GMFB,GMFG,GMIP,GMPR2,GNA11,GNA14,GNAI1,GNAL,GNAO1,GNB5,GNE,GNG11,GNG12,GNG2,GNG5,GNG7,GNL3,GNPNAT1,GNRH2,GOLGA2P10,GOLGA2P5,GOLGA2P7,GOLGA6L10,GOLGA6L17P,GOLGA6L19,GOLGA6L2,GOLGA6L3P,GOLGA6L9,GOLGA7,GOLGA8A,GOLGA8B,GOLGA8DP,GOLGA8EP,GOLGA8H,GOLGA8J,GOLGA8K,GOLGA8M,GOLGA8N,GOLGA8O,GOLGA8Q,GOLGA8R,GOLGA8S,GOLGA8T,GOLM1,GOLT1B,GP1BA,GPAT3,GPATCH11,GPC3,GPC4,GPC5,GPC6,GPCPD1,GPD1,GPD1L,GPD2,GPER1,GPIHBP1,GPLD1,GPM6A,GPN3,GPNMB,GPR132,GPR135,GPR141,GPR146,GPR15,GPR155,GPR160,GPR171,GPR174,GPR18,GPR180,GPR183,GPR22,GPR34,GPR37,GPR4,GPR52,GPR65,GPR75,GPR75-ASB3,GPR82,GPR89A,GPRASP1,GPRASP2,GPRC5B,GPRIN3,GPSM2,GPSM3,GPT2,GPX1,GPX3,GPX7,GPX8,GRAP,GRAP2,G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MIR3134,MIR3136,MIR3137,MIR3139,MIR3140,MIR3143,MIR3145,MIR3146,MIR3156-2,MIR3156-3,MIR3161,MIR3163,MIR3164,MIR3173,MIR3175,MIR3178,MIR3192,MIR31HG,MIR32,MIR320B1,MIR320C1,MIR324,MIR328,MIR329-1,MIR329-2,MIR335,MIR338,MIR33B,MIR340,MIR342,MIR346,MIR34AHG,MIR3605,MIR3606,MIR361,MIR3612,MIR3619,MIR3650,MIR3657,MIR365A,MIR365B,MIR3660,MIR3667,MIR3668,MIR3672,MIR3674,MIR3678,MIR3685,MIR3689A,MIR3689D1,MIR3689F,MIR373,MIR374A,MIR375,MIR376A1,MIR376A2,MIR376B,MIR377,MIR378D1,MIR378H,MIR378I,MIR382,MIR383,MIR384,MIR3907,MIR3908,MIR3910-2,MIR3911,MIR3914-1,MIR3916,MIR3918,MIR3926-1,MIR3926-2,MIR3934,MIR3936,MIR3939,MIR3941,MIR3942,MIR3944,MIR3972,MIR3973,MIR3974,MIR3975,MIR3978,MIR421,MIR4263,MIR4275,MIR4279,MIR4282,MIR4288,MIR4292,MIR4293,MIR4294,MIR4295,MIR4299,MIR4302,MIR4307,MIR4315-1,MIR4317,MIR4324,MIR4326,MIR4420,MIR4421,MIR4429,MIR4439,MIR4440,MIR4441,MIR4442,MIR4451,MIR4453,MIR4454,MIR4457,MIR4469,MIR4474,MIR4477A,MIR4477B,MIR4481,MIR4482,MIR4483,MIR4486,MIR4499,MIR449B,MIR4502,MIR4503,MIR4504,MIR450A1,MIR450A2,MIR450B,MIR4516,MIR4518,MIR4519,MIR4520-2,MIR4521,MIR4522,MIR4524A,MIR4525,MIR4533,MIR4534,MIR454,MIR4632,MIR4635,MIR4636,MIR4638,MIR4641,MIR4644,MIR4646,MIR4659A,MIR4660,MIR4662B,MIR4663,MIR4668,MIR4673,MIR4677,MIR4678,MIR4679-2,MIR4689,MIR4692,MIR4693,MIR4701,MIR4703,MIR4710,MIR4712,MIR4728,MIR4730,MIR4742,MIR4746,MIR4748,MIR4759,MIR4765,MIR4766,MIR4773-1,MIR4773-2,MIR4774,MIR4778,MIR4782,MIR4789,MIR4791,MIR4795,MIR4796,MIR4798,MIR4802,MIR4803,MIR483,MIR486-1,MIR486-2,MIR487A,MIR494,MIR497HG,MIR500A,MIR502,MIR504,MIR505,MIR511,MIR513A2,MIR513C,MIR516B1,MIR517A,MIR520E,MIR520F,MIR539,MIR544A,MIR544B,MIR545,MIR548A1,MIR548A2,MIR548A3,MIR548AB,MIR548AJ1,MIR548AK,MIR548AL,MIR548AM,MIR548AN,MIR548B,MIR548C,MIR548D1,MIR548E,MIR548F1,MIR548F2,MIR548H1,MIR548H2,MIR548I2,MIR548I3,MIR548I4,MIR548K,MIR548L,MIR548M,MIR548O,MIR548O2,MIR548Q,MIR548S,MIR548T,MIR548V,MIR548X,MIR548X2,MIR548Y,MIR548Z,MIR550A1,MIR550A3,MIR551B,MIR552,MIR553,MIR555,MIR556,MIR558,MIR559,MIR563,MIR564,MIR570HG,MIR571,MIR572,MIR573,MIR574,MIR576,MIR579,MIR580,MIR581,MIR590,MIR600HG,MIR601,MIR603,MIR604,MIR605,MIR606,MIR616,MIR620,MIR622,MIR623,MIR626,MIR628,MIR635,MIR637,MIR643,MIR645,MIR646,MIR647,MIR649,MIR6513,MIR655,MIR661,MIR663B,MIR6716,MIR6732,MIR6748,MIR6831,MIR6836,MIR6843,MIR708,MIR766,MIR8085,MIR876,MIR877,MIR891A,MIR9-1,MIR92A2,MIR92B,MIR933,MIR938,MIR943,MIR95,MIR98,MIR99B,MIRLET7A2,MIRLET7C,MIRLET7D,MIRLET7F1,MIS12,MITD1,MITF,MKI67,MKNK2,MKRN1,MKRN2,MKRN9P,MKX,MLF1,MLIP,MLIP-IT1,MLKL,MLLT10P1,MLLT3,MLXIP,MLXIPL,MLYCD,MME,MMP14,MMP15,MMP16,MMP19,MMP24,MMP24OS,MMP25-AS1,MMP7,MMP9,MMRN1,MMRN2,MNDA,MNT,MOB1A,MOCS1,MOCS3,MOGAT1,MORC3,MORC4,MORN2,MOSPD1,MOSPD2,MOXD1,MPC1,MPC2,MPDZ,MPHOSPH6,MPHOSPH9,MPP1,MPP7,MPPED2,MPV17L,MPV17L2,MPZL2,MPZL3,MRAP,MRAS,MRC1,MRC2,MREG,MRFAP1L1,MRGPRF,MRGPRX2,MRI1,MRO,MROH7-TTC4,MROH8,MRPL32,MRPL40,MRPL41,MRPL42,MRPL42P5,MRPL45P2,MRPL46,MRPL51,MRPL54,MRPL58,MRPS15,MRPS18C,MRPS28,MRPS30-DT,MRPS31P5,MRPS34,MRPS7,MRS2,MS4A1,MS4A14,MS4A2,MS4A4A,MS4A4E,MS4A6A,MS4A7,MSANTD2,MSANTD3-TMEFF1,MSC,MSC-AS1,MSH3,MSH5,MSL2,MSL3,MSL3B,MSLN,MSMO1,MSR1,MSRB1,MSRB2,MSS51,MST1,MST1L,MSTO1,MSTO2P,MSX2P1,MT1A,MT1CP,MT1E,MT1G,MT1JP,MT1L,MT1M,MT2A,MT4,MTARC1,MTARC2,MTCL3,MTCP1,MTERF4,MTF2,MTFMT,MTHFD1,MTHFD1L,MTHFD2,MTHFS,MTM1,MTMR1,MTMR7,MTO1,MTRFR,MTRNR2L2,MTRNR2L4,MTRNR2L8,MTSS1,MTSS2,MTUS1,MTX1,MUC16,MUC20,MUC3A,MUC3B,MUSTN1,MVB12A,MVP,MX1,MX2,MXD1,MXRA5,MXRA7,MYADM,MYBL1,MYC,MYCBP2-AS1,MYD88,MYDGF,MYEF2,MYH10,MYH11,MYH14,MYL10,MYL6,MYLIP,MYLK,MYO10,MYO15B,MYO18A,MYO1B,MYO1D,MYO1F,MYO1G,MYO5A,MYO5B,MYO5C,MYO6,MYOC,MYOCD,MYOF,MYOM1,MYORG,MYRF,MYRFL,MYZAP,MZB1,MZT2B,N4BP2L2-IT2,NAA16,NAA25,NAAA,NAALAD2,NAALADL2-AS1,NAB2,NABP1,NACA,NACA2,NACC1,NAF1,NAGA,NAGK,NAGLU,NAIP,NALF1-IT1,NALT1,NAMPT,NANOS1,NAP1L3,NAP1L4,NAPEPLD,NAPSB,NARS2,NAT14,NAT8L,NAV1,NAV2,NAV2-IT1,NAXE,NBDY,NBEAL2,NBEAP1,NBPF1,NBPF10,NBPF12,NBPF15,NBPF26,NBPF3,NBPF5P,NBPF9,NCAM1,NCAM2,NCEH1,NCF1,NCF1B,NCF1C,NCF2,NCF4,NCK2,NCKAP1L,NCKAP5,NCKIPSD,NCLN,NCOA3,NCOA7-AS1,NCS1,NDE1,NDFIP2,NDN,NDNF,NDRG4,NDST1,NDST2,NDUFA1,NDUFA7,NDUFAB1,NDUFAF4,NDUFAF4P1,NDUFAF8,NDUFB11,NDUFB3,NDUFB5,NDUFB7,NDUFC2,NDUFC2-KCTD14,NDUFS1,NDUFS4,NDUFV2,NEAT1,NEB,NEBL,NECAB1,NECAP1,NECTIN2,NEDD4,NEDD4L,NEDD8,NEDD8-MDP1,NEDD9,NEGR1,NEIL3,NEK5,NEK7,NELL2,NEMP1,NEMP2,NENF,NEO1,NES,NET1,NETO2,NEU2,NEU3,NEURL1B,NEXMIF,NEXN,NEXN-AS1,NFAM1,NFASC,NFATC2,NFATC2IP,NFATC3,NFE2L1,NFE2L3,NFE4,NFIA-AS1,NFIL3,NFKB2,NFKBIA,NFKBID,NFKBIZ,NFYB,NFYC-AS1,NGF,NGLY1,NHERF1,NHERF2,NHSL1,NHSL2,NIBAN1,NIBAN2,NIBAN3,NID1,NID2,NIFK,NIN,NINJ1,NINL,NIPSNAP3B,NKAP,NKAPP1,NKD1,NKG7,NKRF,NKTR,NLE1,NLGN1,NLGN4Y,NLK,NLRC3,NLRC4,NLRC5,NLRP1,NLRP3,NMB,NMD3,NME5,NMI,NMNAT2,NMRAL1,NMRK1,NMT2,NMU,NNAT,NNMT,NNT-AS1,NOC3L,NOD1,NOG,NOL4L,NOLC1,NOMO1,NOP10,NOP53,NOP58,NOPCHAP1,NOSTRIN,NOTCH1,NOTCH3,NOTCH4,NOVA1,NOX1,NOX4,NPAS2,NPAT,NPC2,NPFF,NPHP3,NPHP3-ACAD11,NPIPA1,NPIPB15,NPIPB1P,NPIPB8,NPL,NPM1,NPNT,NPR1,NPR3,NPY1R,NPY5R,NQO1,NQO2,NR1D1,NR1D2,NR2F1,NR2F1-AS1,NR2F2,NR2F2-AS1,NR2F6,NR4A1,NR4A2,NR4A3,NR5A2,NRARP,NRBP2,NRG4,NRIP1,NRIP2,NRIR,NRK,NRN1,NRP2,NRROS,NRSN2-AS1,NRXN1,NSD3,NSDHL,NSF,NSFP1,NSG1,NSMCE1,NSMCE2,NSRP1,NSUN5,NSUN5P1,NSUN6,NT5C1B-RDH14,NT5C3A,NT5C3B,NT5E,NTAN1,NTN1,NTN4,NTNG1,NTRK2,NTRK3,NTS,NUAK1,NUAK2,NUBP1,NUCB1,NUDCD1,NUDT12,NUDT16,NUDT16L2P,NUDT2,NUDT4,NUDT9P1,NUP210,NUP43,NUP50,NUP50-DT,NUP54,NUP58,NUP88,NUPR1,NUSAP1,NUTF2,NUTM2A-AS1,NUTM2B-AS1,NUTM2F,NUTM2G,NWD2,NXN,NXPE3,NXPH3,NYNRIN,OAS1,OAS2,OAS3,OASL,OAZ2,OBP2B,OBSL1,OCLN,ODC1,ODF2L,OFD1,OGDH,OGN,OLFM1,OLFM2,OLFML1,OLFML2A,OLFML2B,OLFML3,OLMALINC,OMA1,OMD,OPHN1,OR10A5,OR10AG1,OR10D3,OR10G8,OR10H4,OR13C5,OR14A2,OR2A1,OR2A1-AS1,OR2A20P,OR2A4,OR2A7,OR2A9P,OR2AE1,OR2AK2,OR2F2,OR2J2,OR2L1P,OR2L3,OR2M2,OR2M3,OR2T11,OR2T3,OR2T34,OR4C12,OR4C46,OR4F21,OR4F29,OR4F4,OR4M1,OR51A2,OR51A4,OR51A7,OR51B4,OR51E1,OR51K1P,OR51Q1,OR52H1,OR52N2,OR52N4,OR56A3,OR56B1,OR5H1,OR5H6,OR5M1,OR5M3,OR6C4,OR7A10,OR7E2P,OR7E38P,OR7E5P,OR8B3,OR8D4,OR8H3,ORAI1,ORC2,ORMDL3,OSBPL10,OSBPL11,OSBPL3,OSER1,OSGEP,OSGEPL1-AS1,OSGIN2,OSMR,OSMR-DT,OSR1,OSR2,OSTN,OTUD6B,OTUD7B,OTULIN,OTULINL,OVCA2,OVCH1-AS1,OXCT1,OXNAD1,P2RX1,P2RX4,P2RX5,P2RX7,P2RY1,P2RY10,P2RY11,P2RY12,P2RY13,P2RY14,P2RY8,P3H2,P4HA1,P4HA2,PA2G4P4,PABIR2,PABPC1L,PABPC1L2B,PABPC3,PABPC4L,PABPC5-AS1,PABPN1,PACC1,PAG1,PAGE1,PAICS,PAIP2B,PALLD,PALM,PALM2AKAP2,PALS2,PAM16,PAMR1,PANTR1,PAPOLA,PAPOLB,PAPOLG,PAPPA,PAPSS1,PAPSS2,PARD3,PARD3B,PARD6B,PARD6G,PARM1,PARP11,PARP12,PARP14,PARP15,PARP3,PARP8,PARVG,PASK,PATJ,PAWR,PAX5,PAX8-AS1,PAXBP1-AS1,PBX1,PBX3,PBXIP1,PC,PCAT1,PCAT19,PCBD1,PCBP2,PCCB,PCDH1,PCDH17,PCDH7,PCDH9,PCDHA2,PCDHA4,PCDHA5,PCDHA8,PCDHAC1,PCDHB10,PCDHB11,PCDHB13,PCDHB14,PCDHB15,PCDHB16,PCDHB19P,PCDHB2,PCDHB3,PCDHB4,PCDHB5,PCDHB6,PCDHB7,PCDHB8,PCDHB9,PCDHGA1,PCDHGA10,PCDHGA11,PCDHGA12,PCDHGA2,PCDHGA3,PCDHGA4,PCDHGA5,PCDHGA6,PCDHGA7,PCDHGA8,PCDHGA9,PCDHGB1,PCDHGB2,PCDHGB3,PCDHGB4,PCDHGB5,PCDHGB6,PCDHGB7,PCDHGB8P,PCDHGC3,PCDHGC4,PCED1A,PCED1B,PCED1B-AS1,PCGF2,PCGF3,PCK1,PCK2,PCMT1,PCNX2,PCOLCE,PCOLCE2,PCP4,PCP4L1,PCSK2,PCSK5,PCSK6,PCSK7,PCYT2,PDCD1LG2,PDCD6IPP2,PDCL2,PDCL3P4,PDE10A,PDE11A,PDE1A,PDE1B,PDE1C,PDE2A,PDE3A,PDE4B,PDE5A,PDE7A,PDE7B,PDE8B,PDGFA,PDGFB,PDGFC,PDGFD,PDGFRA,PDGFRB,PDGFRL,PDHA1,PDIA3,PDK1,PDK2,PDK3,PDLIM1,PDLIM3,PDLIM5,PDLIM7,PDP2,PDPN,PDPR,PDSS2,PDXDC2P,PDXK,PDZD2,PDZD8,PDZK1IP1,PDZRN4,PEAR1,PECR,PEG10,PEG3,PELATON,PELI1,PELO,PEMT,PEPD,PER1,PER2,PER3,PERP,PEX11A,PEX12,PEX5L,PF4,PFDN2,PFDN4,PFKFB1,PFKFB3,PFN2,PFN4,PGA4,PGA5,PGAM1,PGAP1,PGCKA1,PGD,PGGHG,PGLS,PGM2,PGM3,PGM5,PGM5-AS1,PGM5P2,PGM5P3-AS1,PGM5P4-AS1,PGR,PGS1,PHAX,PHF12,PHF13,PHF24,PHGDH,PHKA1,PHKA2,PHKG1,PHLDA1,PHLDA3,PHLDB1,PHLPP1,PHLPP2,PHOSPHO2,PHYHIP,PI16,PI4K2A,PI4K2B,PI4KAP1,PI4KAP2,PID1,PIERCE2,PIEZO1,PIEZO2,PIGA,PIGF,PIGL,PIGN,PIGP,PIGZ,PIK3AP1,PIK3C2B,PIK3CD,PIK3CG,PIK3IP1,PIK3R3,PIK3R5,PILRA,PILRB,PIM1,PIM2,PIM3,PIP4K2A,PIP5K1A,PIP5K1B,PIR,PIRC76,PITPNC1,PJA1,PKD1L1,PKD1L2,PKD1L3,PKD1P1,PKD1P6,PKD2,PKDCC,PKHD1L1,PKN3,PKNOX2,PKP2,PKP4,PLA2G12A,PLA2G15,PLA2G2A,PLA2G2D,PLA2G4A,PLA2G4C,PLA2G5,PLA2G7,PLA2R1,PLAAT5,PLAC8,PLAC9,PLAGL1,PLAT,PLAU,PLAUR,PLB1,PLBD1,PLCB1,PLCB2,PLCB4,PLCD3,PLCE1,PLCE1-AS1,PLCG2,PLCXD1,PLCXD3,PLD3,PLD6,PLEC,PLEK,PLEK2,PLEKHA2,PLEKHA5,PLEKHA8P1,PLEKHF1,PLEKHF2,PLEKHG1,PLEKHG3,PLEKHG6,PLEKHH2,PLEKHM1,PLEKHM1P1,PLEKHM2,PLEKHO1,PLEKHO2,PLGLA,PLGLB1,PLGLB2,PLIN2,PLIN5,PLK1,PLK2,PLLP,PLN,PLOD1,PLOD2,PLPP5,PLPPR2,PLPPR4,PLRG1,PLS1,PLS3,PLSCR1,PLSCR3,PLSCR4,PLVAP,PLXNA1,PLXNA2,PLXNA4,PLXNB1,PLXNB2,PLXNC1,PLXND1,PMAIP1,PMEPA1,PMF1-BGLAP,PML,PMP2,PMS2,PMS2CL,PMS2P1,PMS2P3,PMS2P4,PMS2P5,PMS2P9,PMVK,PNMA1,PNMA5,PNOC,PNP,PNPLA2,PNRC2,POC1B-GALNT4,POC5,PODN,PODXL,POF1B,POGLUT1,POGLUT2,POLI,POLR1HASP,POLR2E,POLR2I,POLR2J,POLR2J2,POLR2J3,POLR2L,POLR3K,POLRMT,POM121,POM121L7P,POM121L9P,POMK,PON3,POP7,POPDC1,POSTN,POTEE,POTEG,POTEH-AS1,POU2F1,POU2F2,POU5F1,POU5F1P3,POU6F1,PPA1,PPAN-P2RY11,PPARA,PPARGC1A,PPBP,PPDPF,PPFIBP1,PPHLN1,PPIA,PPIAL4A,PPIAL4C,PPIAL4E,PPIAL4G,PPIAP30,PPIC,PPID,PPIF,PPIL1,PPIL3,PPIL4,PPIL6,PPL,PPM1K,PPP1CB,PPP1R12A,PPP1R12B,PPP1R14A,PPP1R14B,PPP1R15A,PPP1R15B,PPP1R16B,PPP1R18,PPP1R1B,PPP1R2,PPP1R26,PPP1R35,PPP1R3C,PPP1R3E,PPP1R3F,PPP2R1B,PPP2R2A,PPP2R5A,PPP3CB-AS1,PPP3CC,PPP3R1,PPP4R1,PPP4R3A,PPP6R1,PPT2-EGFL8,PPTC7,PPWD1,PRAC1,PRAMEF18,PRAMEF6,PRAMEF9,PRAP1,PRB1,PRB2,PRDM1,PRDM2,PRDM6,PRDX1,PRDX5,PREB,PRELID2,PREX1,PRF1,PRG4,PRH1,PRIM1,PRIMA1,PRINS,PRKAA1,PRKAA2,PRKACA,PRKAR2A,PRKCA,PRKCB,PRKCD,PRKCH,PRKCI,PRKCQ,PRKCQ-AS1,PRKD1,PRKD2,PRKD3,PRKG1,PRKX,PRKXP1,PRKY,PRND,PROCR,PROK2,PROS1,PROX1,PRPF38B,PRPF39,PRPS1,PRPS2,PRR11,PRR12,PRR13,PRR14,PRR15,PRR15L,PRR34,PRR34-AS1,PRR4,PRR5,PRR5L,PRRT3-AS1,PRRX1,PRSS1,PRSS23,PRSS3P1,PRSS3P2,PRSS40A,PRTFDC1,PRUNE2,PSAT1,PSD3,PSD4,PSG1,PSG10P,PSG6,PSMA3-AS1,PSMB10,PSMB3,PSMB6,PSMB8,PSMB8-AS1,PSMB9,PSMC5,PSMD6-AS2,PSMD8,PSME3IP1,PSMG1,PSORS1C2,PSORS1C3,PSTPIP2,PTAFR,PTBP3,PTCD1,PTCHD4,PTEN,PTENP1,PTER,PTGDR,PTGDS,PTGER2,PTGER3,PTGER4,PTGES,PTGES3L,PTGFR,PTGFRN,PTGIR,PTGIS,PTGR1,PTGR2,PTGR3,PTGS1,PTGS2,PTH1R,PTK2B,PTMS,PTN,PTP4A3,PTPN12,PTPN13,PTPN2,PTPN21,PTPN22,PTPN3,PTPN6,PTPRB,PTPRC,PTPRCAP,PTPRD,PTPRE,PTPRF,PTPRG,PTPRJ,PTPRQ,PTPRT,PTPRU,PTPRZ1,PTTG2,PTX3,PUDP,PVRIG,PVT1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+,Atrial_Fibrillation,Age,Gender,A1BG,A1BG-AS1,A1CF,A2M,A2M-AS1,A2ML1,A2MP1,A4GALT,A4GNT,AA06,AAAS,AACS,AACSP1,AADAC,AADACL2,AADACP1,AADAT,AAGAB,AAK1,AAMDC,AAMP,AANAT,AAR2,AARS1,AARS2,AARSD1,AASDH,AASDHPPT,AASS,AATF,AATK,ABAT,ABCA1,ABCA11P,ABCA12,ABCA13,ABCA17P,ABCA2,ABCA3,ABCA4,ABCA5,ABCA6,ABCA7,ABCA8,ABCA9,ABCB1,ABCB10,ABCB11,ABCB4,ABCB5,ABCB6,ABCB7,ABCB8,ABCB9,ABCC1,ABCC10,ABCC11,ABCC12,ABCC13,ABCC2,ABCC3,ABCC4,ABCC5,ABCC6,ABCC6P1,ABCC6P2,ABCC8,ABCC9,ABCD1,ABCD1P2,ABCD2,ABCD3,ABCD4,ABCE1,ABCF1,ABCF2,ABCF3,ABCG1,ABCG2,ABCG4,ABCG5,ABCG8,ABHD1,ABHD10,ABHD11,ABHD12,ABHD12B,ABHD13,ABHD14A,ABHD14B,ABHD15,ABHD16A,ABHD16B,ABHD17A,ABHD17B,ABHD17C,ABHD18,ABHD2,ABHD3,ABHD4,ABHD5,ABHD6,ABHD8,ABI1,ABI2,ABI3,ABI3BP,ABITRAM,ABL1,ABL2,ABLIM1,ABLIM2,ABLIM3,ABO,ABR,ABRA,ABRACL,ABRAXAS1,ABRAXAS2,ABT1,ABTB1,ABTB2,ABTB3,ACAA1,ACAA2,ACACA,ACACB,ACAD10,ACAD11,ACAD8,ACAD9,ACADL,ACADM,ACADS,ACADSB,ACADVL,ACAN,ACAP1,ACAP2,ACAP3,ACAT1,ACAT2,ACBD3,ACBD4,ACBD5,ACBD6,ACCS,ACD,ACE,ACE2,ACER1,ACER3,ACHE,ACIN1,ACKR1,ACKR2,ACKR3,ACKR4,ACLY,ACMSD,ACO1,ACO2,ACOD1,ACOT1,ACOT11,ACOT12,ACOT13,ACOT2,ACOT4,ACOT6,ACOT7,ACOT8,ACOT9,ACOX1,ACOX2,ACOX3,ACOXL,ACP1,ACP2,ACP3,ACP4,ACP5,ACP6,ACR,ACRBP,ACRV1,ACSBG1,ACSBG2,ACSF2,ACSF3,ACSL1,ACSL3,ACSL4,ACSL5,ACSL6,ACSM1,ACSM2A,ACSM2B,ACSM3,ACSM5,ACSS1,ACSS2,ACSS3,ACTA1,ACTA2,ACTA2-AS1,ACTB,ACTBP9,ACTC1,ACTG1,ACTG1P4,ACTG2,ACTL10,ACTL6A,ACTL6B,ACTL7A,ACTL7B,ACTL8,ACTL9,ACTMAP,ACTN1,ACTN2,ACTN3,ACTN4,ACTR10,ACTR1A,ACTR1B,ACTR2,ACTR3,ACTR3B,ACTR3BP2,ACTR3BP5,ACTR3C,ACTR3P2,ACTR5,ACTR6,ACTR8,ACTRT2,ACTRT3,ACVR1,ACVR1B,ACVR1C,ACVR2A,ACVR2B,ACVR2B-AS1,ACVRL1,ACY1,ACY3,ACYP1,ACYP2,ADA,ADA2,ADAD1,ADAD2,ADAM10,ADAM11,ADAM12,ADAM15,ADAM17,ADAM18,ADAM19,ADAM1A,ADAM2,ADAM20,ADAM21,ADAM22,ADAM23,ADAM28,ADAM29,ADAM30,ADAM32,ADAM33,ADAM3A,ADAM3B,ADAM5,ADAM6,ADAM7,ADAM8,ADAM9,ADAMDEC1,ADAMTS1,ADAMTS10,ADAMTS12,ADAMTS13,ADAMTS14,ADAMTS15,ADAMTS16,ADAMTS17,ADAMTS18,ADAMTS19,ADAMTS2,ADAMTS20,ADAMTS3,ADAMTS4,ADAMTS5,ADAMTS6,ADAMTS7,ADAMTS8,ADAMTS9,ADAMTSL1,ADAMTSL2,ADAMTSL3,ADAMTSL4,ADAMTSL5,ADAP1,ADAP2,ADAR,ADARB1,ADARB2,ADARB2-AS1,ADAT1,ADAT2,ADAT3,ADCK1,ADCK2,ADCK5,ADCY1,ADCY10,ADCY10P1,ADCY2,ADCY3,ADCY4,ADCY5,ADCY6,ADCY7,ADCY8,ADCY9,ADCYAP1,ADCYAP1R1,ADD1,ADD2,ADD3,ADD3-AS1,ADGB,ADGRA1,ADGRA2,ADGRA3,ADGRB1,ADGRB2,ADGRB3,ADGRD1,ADGRD2,ADGRE1,ADGRE2,ADGRE3,ADGRF1,ADGRF2P,ADGRF3,ADGRF4,ADGRF5,ADGRG1,ADGRG2,ADGRG3,ADGRG4,ADGRG5,ADGRG6,ADGRG7,ADGRL1,ADGRL2,ADGRL3,ADGRL4,ADGRV1,ADH1A,ADH1B,ADH1C,ADH4,ADH5,ADH6,ADH7,ADHFE1,ADI1,ADIG,ADIPOQ,ADIPOR1,ADIPOR2,ADIRF,ADIRF-AS1,ADISSP,ADK,ADM,ADM2,ADM5,ADNP,ADNP2,ADO,ADORA1,ADORA2A,ADORA2B,ADORA3,ADPGK,ADPRH,ADPRHL1,ADPRM,ADPRS,ADRA1A,ADRA1B,ADRA1D,ADRA2A,ADRA2B,ADRA2C,ADRB1,ADRB2,ADRB3,ADRM1,ADSL,ADSS1,ADSS2,ADTRP,AEBP1,AEBP2,AEN,AFAP1,AFAP1-AS1,AFAP1L1,AFAP1L2,AFDN,AFDN-DT,AFF1,AFF2,AFF3,AFF4,AFG1L,AFG2A,AFG2B,AFG3L1P,AFG3L2,AFM,AFMID,AFP,AFTPH,AGA,AGAP1,AGAP10P,AGAP11,AGAP12P,AGAP2,AGAP2-AS1,AGAP3,AGAP4,AGAP5,AGAP6,AGAP7P,AGAP9,AGBL1,AGBL2,AGBL3,AGBL4,AGBL5,AGER,AGFG1,AGFG2,AGGF1,AGK,AGL,AGMAT,AGO1,AGO2,AGO3,AGO4,AGPAT1,AGPAT2,AGPAT3,AGPAT4,AGPAT4-IT1,AGPAT5,AGPS,AGR2,AGR3,AGRN,AGRP,AGT,AGTPBP1,AGTR1,AGTR2,AGTRAP,AGXT,AGXT2,AHCTF1,AHCTF1P1,AHCY,AHCYL1,AHCYL2,AHDC1,AHI1,AHNAK,AHNAK2,AHR,AHRR,AHSA1,AHSA2P,AHSG,AHSP,AICDA,AIDA,AIF1,AIF1L,AIFM1,AIFM2,AIFM3,AIG1,AIM2,AIMP1,AIMP2,AIP,AIPL1,AIRE,AIRIM,AJAP1,AJM1,AJUBA,AK1,AK2,AK3,AK4,AK5,AK6,AK7,AK8,AK9,AKAIN1,AKAP1,AKAP10,AKAP11,AKAP12,AKAP13,AKAP14,AKAP17A,AKAP3,AKAP4,AKAP5,AKAP6,AKAP7,AKAP8,AKAP8L,AKAP9,AKIP1,AKIRIN1,AKIRIN2,AKNA,AKNAD1,AKR1A1,AKR1B1,AKR1B10,AKR1C1,AKR1C2,AKR1C3,AKR1C4,AKR1C6P,AKR1D1,AKR1E2,AKR7A2,AKR7A3,AKT1,AKT1S1,AKT2,AKT3,AKTIP,ALAD,ALAS1,ALAS2,ALB,ALCAM,ALDH16A1,ALDH18A1,ALDH1A1,ALDH1A2,ALDH1A3,ALDH1B1,ALDH1L1,ALDH1L2,ALDH2,ALDH3A1,ALDH3A2,ALDH3B1,ALDH3B2,ALDH4A1,ALDH5A1,ALDH6A1,ALDH7A1,ALDH8A1,ALDH9A1,ALDOA,ALDOAP2,ALDOB,ALDOC,ALG1,ALG10,ALG10B,ALG11,ALG12,ALG13,ALG14,ALG2,ALG3,ALG5,ALG6,ALG8,ALG9,ALK,ALKAL2,ALKBH1,ALKBH2,ALKBH3,ALKBH3-AS1,ALKBH4,ALKBH5,ALKBH6,ALKBH7,ALKBH8,ALLC,ALMS1,ALMS1-IT1,ALMS1P1,ALOX12,ALOX12B,ALOX12P2,ALOX15,ALOX15B,ALOX5,ALOX5AP,ALOXE3,ALPG,ALPI,ALPK1,ALPK2,ALPK3,ALPL,ALPP,ALS2,ALS2CL,ALX1,ALX3,ALX4,ALYREF,AMACR,AMBN,AMBP,AMBRA1,AMD1,AMDHD1,AMDHD2,AMELX,AMELY,AMER1,AMER2,AMER3,AMFR,AMH,AMHR2,AMIGO1,AMIGO2,AMIGO3,AMMECR1,AMMECR1L,AMN,AMN1,AMOT,AMOTL1,AMOTL2,AMPD1,AMPD2,AMPD3,AMPH,AMT,AMY1A,AMY1B,AMY1C,AMY2A,AMY2B,AMZ1,AMZ2,AMZ2P1,ANAPC1,ANAPC10,ANAPC11,ANAPC13,ANAPC15,ANAPC16,ANAPC2,ANAPC4,ANAPC5,ANAPC7,ANG,ANGEL1,ANGEL2,ANGPT1,ANGPT2,ANGPT4,ANGPTL1,ANGPTL2,ANGPTL3,ANGPTL4,ANGPTL6,ANGPTL7,ANGPTL8,ANHX,ANK1,ANK2,ANK3,ANKAR,ANKDD1A,ANKEF1,ANKFN1,ANKFY1,ANKH,ANKHD1,ANKIB1,ANKLE1,ANKLE2,ANKMY1,ANKMY2,ANKRA2,ANKRD1,ANKRD10,ANKRD11,ANKRD12,ANKRD13A,ANKRD13B,ANKRD13C,ANKRD13C-DT,ANKRD13D,ANKRD16,ANKRD17,ANKRD18A,ANKRD18B,ANKRD19P,ANKRD2,ANKRD20A1,ANKRD20A2P,ANKRD20A3P,ANKRD20A4P,ANKRD20A5P,ANKRD20A8P,ANKRD20A9P,ANKRD22,ANKRD23,ANKRD24,ANKRD26,ANKRD26P3,ANKRD27,ANKRD28,ANKRD29,ANKRD30A,ANKRD30B,ANKRD30BL,ANKRD30BP2,ANKRD30BP3,ANKRD31,ANKRD33,ANKRD33B,ANKRD34A,ANKRD34C,ANKRD35,ANKRD36,ANKRD36B,ANKRD36BP2,ANKRD36C,ANKRD37,ANKRD39,ANKRD40,ANKRD44,ANKRD45,ANKRD46,ANKRD49,ANKRD50,ANKRD52,ANKRD53,ANKRD54,ANKRD55,ANKRD6,ANKRD60,ANKRD65,ANKRD7,ANKRD9,ANKS1A,ANKS1B,ANKS3,ANKS4B,ANKS6,ANKUB1,ANKZF1,ANLN,ANO1,ANO10,ANO2,ANO3,ANO4,ANO5,ANO6,ANO7,ANO8,ANO9,ANOS1,ANOS2P,ANP32A,ANP32A-IT1,ANP32B,ANP32CP,ANP32D,ANP32E,ANPEP,ANTKMT,ANTXR1,ANTXR2,ANXA1,ANXA10,ANXA11,ANXA13,ANXA2,ANXA2P1,ANXA2P2,ANXA2P3,ANXA2R,ANXA3,ANXA4,ANXA5,ANXA6,ANXA7,ANXA8,ANXA8L1,ANXA9,AOAH,AOC1,AOC2,AOC3,AOC4P,AOPEP,AOX1,AOX2P,AP1AR,AP1B1,AP1G1,AP1G2,AP1M1,AP1M2,AP1S1,AP1S2,AP1S3,AP2A1,AP2A2,AP2B1,AP2M1,AP2S1,AP3B1,AP3B2,AP3D1,AP3M1,AP3M2,AP3S1,AP3S2,AP4B1,AP4E1,AP4M1,AP4S1,AP5B1,AP5M1,AP5S1,AP5Z1,APAF1,APBA1,APBA2,APBA3,APBB1,APBB1IP,APBB2,APBB3,APC,APC2,APCDD1,APCDD1L,APCS,APEH,APELA,APEX1,APEX2,APH1A,APH1B,API5,APIP,APLF,APLN,APLNR,APLP1,APLP2,APMAP,APOA1,APOA1-AS,APOA2,APOA4,APOA5,APOB,APOBEC1,APOBEC2,APOBEC3A,APOBEC3B,APOBEC3C,APOBEC3D,APOBEC3F,APOBEC3G,APOBEC4,APOBR,APOC1,APOC2,APOC3,APOC4,APOD,APOE,APOF,APOH,APOL1,APOL2,APOL3,APOL4,APOL5,APOL6,APOLD1,APOM,APOO,APOOL,APP,APPBP2,APPL1,APPL2,APRG1,APRT,APTR,APTX,AQP1,AQP10,AQP11,AQP12A,AQP12B,AQP2,AQP3,AQP4,AQP4-AS1,AQP5,AQP6,AQP7,AQP8,AQP9,AQR,AR,ARAF,ARAP1,ARAP2,ARAP3,ARB2A,ARC,ARCN1,AREG,AREL1,ARF1,ARF1P1,ARF3,ARF4,ARF5,ARF6,ARFGAP1,ARFGAP2,ARFGAP3,ARFGEF1,ARFGEF2,ARFGEF3,ARFIP1,ARFIP2,ARFRP1,ARG1,ARG2,ARGLU1,ARHGAP1,ARHGAP10,ARHGAP11A,ARHGAP11B,ARHGAP12,ARHGAP15,ARHGAP17,ARHGAP18,ARHGAP19,ARHGAP20,ARHGAP21,ARHGAP22,ARHGAP23,ARHGAP24,ARHGAP25,ARHGAP26,ARHGAP27,ARHGAP28,ARHGAP29,ARHGAP30,ARHGAP31,ARHGAP32,ARHGAP33,ARHGAP35,ARHGAP36,ARHGAP39,ARHGAP4,ARHGAP42,ARHGAP44,ARHGAP45,ARHGAP5,ARHGAP6,ARHGAP8,ARHGAP9,ARHGDIA,ARHGDIB,ARHGDIG,ARHGEF1,ARHGEF10,ARHGEF10L,ARHGEF11,ARHGEF12,ARHGEF15,ARHGEF16,ARHGEF17,ARHGEF18,ARHGEF19,ARHGEF2,ARHGEF25,ARHGEF26,ARHGEF28,ARHGEF3,ARHGEF33,ARHGEF34P,ARHGEF37,ARHGEF38,ARHGEF39,ARHGEF4,ARHGEF40,ARHGEF5,ARHGEF6,ARHGEF7,ARHGEF9,ARID1A,ARID1B,ARID2,ARID3A,ARID3B,ARID4A,ARID4B,ARID5A,ARID5B,ARIH1,ARIH2,ARIH2OS,ARK2C,ARK2N,ARL1,ARL10,ARL11,ARL13B,ARL14,ARL14EP,ARL15,ARL16,ARL17A,ARL17B,ARL2,ARL2BP,ARL3,ARL4A,ARL4C,ARL4D,ARL5A,ARL5B,ARL5C,ARL6,ARL6IP1,ARL6IP4,ARL6IP5,ARL6IP6,ARL8A,ARL8B,ARL9,ARLN,ARMC1,ARMC10,ARMC12,ARMC2,ARMC2-AS1,ARMC3,ARMC5,ARMC6,ARMC7,ARMC8,ARMC9,ARMCX1,ARMCX2,ARMCX3,ARMCX4,ARMCX5,ARMCX5-GPRASP2,ARMCX6,ARMH1,ARMH3,ARMH4,ARMT1,ARNT,ARNT2,ARPC1A,ARPC1B,ARPC2,ARPC3,ARPC4,ARPC5,ARPC5L,ARPIN,ARPIN-AP3S2,ARPP19,ARPP21,ARR3,ARRB1,ARRB2,ARRDC1,ARRDC1-AS1,ARRDC2,ARRDC3,ARRDC3-AS1,ARRDC4,ARSA,ARSB,ARSD,ARSF,ARSG,ARSI,ARSJ,ARSK,ARSL,ART1,ART3,ART4,ART5,ARTN,ARV1,ARVCF,ARX,AS3MT,ASAH1,ASAH2B,ASAP1,ASAP1-IT1,ASAP1-IT2,ASAP2,ASAP3,ASB1,ASB10,ASB11,ASB12,ASB13,ASB14,ASB15,ASB16,ASB16-AS1,ASB17,ASB2,ASB3,ASB4,ASB5,ASB6,ASB7,ASB8,ASB9,ASCC1,ASCC2,ASCC3,ASCL1,ASCL2,ASCL3,ASCL5,ASF1A,ASF1B,ASGR1,ASGR2,ASH1L,ASH1L-AS1,ASH2L,ASIC1,ASIC2,ASIC3,ASIC4,ASIC5,ASIP,ASL,ASMT,ASMTL,ASMTL-AS1,ASNS,ASNSD1,ASPA,ASPDH,ASPG,ASPH,ASPHD1,ASPHD2,ASPM,ASPN,ASPRV1,ASPSCR1,ASRGL1,ASS1,ASTE1,ASTN1,ASTN2,ASXL1,ASXL2,ASXL3,ASZ1,ATAD1,ATAD2,ATAD2B,ATAD3A,ATAD3B,ATAD3C,ATAD5,ATAT1,ATCAY,ATE1,ATF1,ATF2,ATF3,ATF4,ATF5,ATF6,ATF6B,ATF7,ATF7IP,ATF7IP2,ATG10,ATG101,ATG12,ATG13,ATG14,ATG16L1,ATG16L2,ATG2A,ATG2B,ATG3,ATG4A,ATG4B,ATG4C,ATG4D,ATG5,ATG7,ATG9A,ATG9B,ATIC,ATL1,ATL2,ATL3,ATM,ATMIN,ATN1,ATOH1,ATOH7,ATOH8,ATOSA,ATOSB,ATOX1,ATP10A,ATP10B,ATP10D,ATP11A,ATP11A-AS1,ATP11B,ATP11C,ATP12A,ATP13A1,ATP13A2,ATP13A3,ATP13A4,ATP13A5,ATP1A1,ATP1A1-AS1,ATP1A2,ATP1A3,ATP1A4,ATP1B1,ATP1B2,ATP1B3,ATP1B4,ATP23,ATP2A1,ATP2A2,ATP2A3,ATP2B1,ATP2B2,ATP2B3,ATP2B4,ATP2C1,ATP2C2,ATP4A,ATP4B,ATP5F1A,ATP5F1B,ATP5F1C,ATP5F1D,ATP5F1E,ATP5IF1,ATP5MC1,ATP5MC2,ATP5MC2P1,ATP5MC3,ATP5ME,ATP5MF,ATP5MG,ATP5MJ,ATP5MK,ATP5PB,ATP5PD,ATP5PF,ATP5PO,ATP6,ATP6AP1,ATP6AP1L,ATP6AP2,ATP6V0A1,ATP6V0A2,ATP6V0A4,ATP6V0B,ATP6V0C,ATP6V0D1,ATP6V0D2,ATP6V0E1,ATP6V0E2,ATP6V1A,ATP6V1B1,ATP6V1B2,ATP6V1C1,ATP6V1C2,ATP6V1D,ATP6V1E1,ATP6V1E2,ATP6V1F,ATP6V1G1,ATP6V1G2,ATP6V1G3,ATP6V1H,ATP7A,ATP7B,ATP8A1,ATP8A2,ATP8B1,ATP8B2,ATP8B3,ATP8B4,ATP8B5P,ATP9A,ATP9B,ATPAF1,ATPAF2,ATPSCKMT,ATR,ATRAID,ATRIP,ATRN,ATRNL1,ATRX,ATXN1,ATXN10,ATXN1L,ATXN2,ATXN2L,ATXN3,ATXN3L,ATXN7,ATXN7L1,ATXN7L2,ATXN7L3,ATXN7L3B,ATXN8OS,AUH,AUNIP,AUP1,AURKA,AURKAIP1,AURKAP1,AURKB,AURKC,AUTS2,AVEN,AVIL,AVL9,AVP,AVPI1,AVPR1A,AVPR1B,AVPR2,AWAT1,AXDND1,AXIN1,AXIN2,AXL,AZGP1,AZGP1P1,AZI2,AZIN1,AZIN2,AZU1,B2M,B3GALNT1,B3GALNT2,B3GALT1,B3GALT2,B3GALT4,B3GALT5,B3GALT5-AS1,B3GALT6,B3GAT1,B3GAT2,B3GAT3,B3GLCT,B3GNT2,B3GNT3,B3GNT4,B3GNT5,B3GNT6,B3GNT7,B3GNT8,B3GNT9,B3GNTL1,B4GALNT1,B4GALNT2,B4GALNT3,B4GALNT4,B4GALT1,B4GALT2,B4GALT3,B4GALT4,B4GALT5,B4GALT6,B4GALT7,B4GAT1,B9D1,B9D2,BAALC,BAALC-AS2,BAAT,BABAM1,BABAM2,BABAM2-AS1,BACE1,BACE2,BACH1,BACH2,BAD,BAG1,BAG2,BAG3,BAG4,BAG5,BAG6,BAGE,BAGE2,BAGE3,BAGE4,BAGE5,BAHCC1,BAHD1,BAIAP2,BAIAP2-DT,BAIAP2L1,BAIAP2L2,BAIAP3,BAK1,BAMBI,BANCR,BANF1,BANF2,BANK1,BANP,BAP1,BARD1,BARHL1,BARX1,BARX1-DT,BARX2,BASP1,BATF,BATF2,BATF3,BAX,BAZ1A,BAZ1B,BAZ2A,BAZ2B,BBC3,BBIP1,BBLN,BBOF1,BBOX1,BBS1,BBS10,BBS12,BBS2,BBS4,BBS5,BBS7,BBS9,BBX,BCAM,BCAN,BCAP29,BCAP31,BCAR1,BCAR3,BCAR4,BCAS1,BCAS2,BCAS3,BCAS4,BCAT1,BCAT2,BCCIP,BCDIN3D,BCHE,BCKDHA,BCKDHB,BCKDK,BCL10,BCL11A,BCL11B,BCL2,BCL2A1,BCL2L1,BCL2L10,BCL2L11,BCL2L12,BCL2L13,BCL2L14,BCL2L15,BCL2L2,BCL3,BCL6,BCL6B,BCL7A,BCL7B,BCL7C,BCL9,BCL9L,BCLAF1,BCO1,BCO2,BCOR,BCORL1,BCORP1,BCR,BCRP3,BCS1L,BDH1,BDH2,BDKRB1,BDKRB2,BDNF,BDNF-AS,BDP1,BEAN1,BECN1,BEGAIN,BEND2,BEND3,BEND4,BEND5,BEND6,BEND7,BEST1,BEST2,BEST3,BEST4,BET1,BET1L,BEX1,BEX2,BEX3,BEX4,BEX5,BFAR,BFSP1,BFSP2,BFSP2-AS1,BGLAP,BGLT3,BGN,BHLHE22,BHLHE23,BHLHE40,BHLHE41,BHMT,BHMT2,BICC1,BICD1,BICD2,BICDL1,BICDL2,BICRA,BICRAL,BID,BIK,BIN1,BIN2,BIN3,BIN3-IT1,BIRC2,BIRC3,BIRC5,BIRC6,BIRC7,BIVM,BLACAT1,BLCAP,BLID,BLK,BLM,BLMH,BLNK,BLOC1S1,BLOC1S2,BLOC1S3,BLOC1S4,BLOC1S5,BLOC1S6,BLTP1,BLTP2,BLTP3A,BLTP3B,BLVRA,BLVRB,BLZF1,BMAL1,BMAL2,BMAL2-AS1,BMERB1,BMF,BMI1,BMP1,BMP10,BMP15,BMP2,BMP2K,BMP3,BMP4,BMP5,BMP6,BMP7,BMP8A,BMP8B,BMPER,BMPR1A,BMPR1B,BMPR2,BMS1,BMS1P1,BMS1P17,BMS1P18,BMS1P2,BMS1P20,BMX,BNC1,BNC2,BNIP1,BNIP2,BNIP3,BNIP3L,BNIPL,BOC,BOD1,BOD1L1,BOD1L2,BOK,BOLA1,BOLA2,BOLA2B,BOLA3,BOLA3-DT,BOLL,BOP1,BORA,BORCS5,BORCS6,BORCS7,BORCS8,BPESC1,BPGM,BPHL,BPI,BPIFA1,BPIFA2,BPIFA3,BPIFA4P,BPIFB1,BPIFB2,BPIFB4,BPIFB6,BPIFB9P,BPIFC,BPNT1,BPNT2,BPTF,BPY2,BRAF,BRAP,BRAT1,BRCA1,BRCA2,BRCC3,BRD1,BRD10,BRD2,BRD3,BRD4,BRD7,BRD7P3,BRD8,BRD9,BRDT,BRF1,BRF2,BRI3,BRI3BP,BRICD5,BRINP1,BRINP2,BRINP3,BRIP1,BRIX1,BRK1,BRME1,BRMS1,BRMS1L,BROX,BRPF1,BRPF3,BRS3,BRSK1,BRSK2,BRWD1,BRWD1-AS2,BRWD3,BSCL2,BSDC1,BSG,BSN,BSN-DT,BSND,BSPRY,BST1,BST2,BTAF1,BTBD1,BTBD10,BTBD16,BTBD17,BTBD18,BTBD19,BTBD2,BTBD3,BTBD6,BTBD7,BTBD8,BTBD9,BTC,BTD,BTF3,BTF3L4,BTF3P11,BTF3P12,BTF3P13,BTG1,BTG2,BTG3,BTG4,BTK,BTLA,BTN1A1,BTN2A1,BTN2A2,BTN2A3P,BTN3A1,BTN3A2,BTN3A3,BTNL2,BTNL3,BTNL8,BTNL9,BTRC,BUB1,BUB1B,BUB3,BUD13,BUD23,BUD31,BYSL,BZW1,BZW2,C10orf126,C10orf53,C10orf55,C10orf62,C10orf67,C10orf71,C10orf88,C10orf90,C10orf95,C10orf95-AS1,C11orf16,C11orf21,C11orf24,C11orf40,C11orf42,C11orf54,C11orf58,C11orf65,C11orf68,C11orf71,C11orf86,C11orf87,C11orf96,C11orf97,C11orf98,C12orf42,C12orf43,C12orf50,C12orf54,C12orf56,C12orf57,C12orf60,C12orf75,C12orf76,C14orf119,C14orf132,C14orf180,C14orf39,C14orf93,C15orf32,C15orf39,C15orf40,C15orf48,C15orf61,C15orf62,C16orf46,C16orf54,C16orf74,C16orf78,C16orf82,C16orf86,C16orf87,C16orf89,C16orf92,C16orf95,C17orf100,C17orf50,C17orf58,C17orf67,C17orf75,C17orf78,C17orf99,C18orf15,C18orf21,C18orf32,C18orf54,C18orf61,C19orf12,C19orf18,C19orf25,C19orf33,C19orf44,C19orf47,C19orf48P,C19orf53,C19orf73,C19orf81,C19orf84,C1D,C1GALT1,C1GALT1C1,C1QA,C1QB,C1QBP,C1QC,C1QL1,C1QTNF1,C1QTNF12,C1QTNF2,C1QTNF3,C1QTNF4,C1QTNF5,C1QTNF6,C1QTNF7,C1QTNF9,C1QTNF9B,C1R,C1RL,C1RL-AS1,C1S,C1orf105,C1orf115,C1orf116,C1orf122,C1orf159,C1orf162,C1orf167,C1orf174,C1orf198,C1orf21,C1orf210,C1orf216,C1orf220,C1orf226,C1orf35,C1orf43,C1orf50,C1orf52,C1orf53,C1orf54,C1orf56,C1orf74,C1orf87,C1orf94,C2,C20orf141,C20orf144,C20orf173,C20orf181,C20orf202,C20orf203,C20orf96,C21orf58,C21orf91,C22orf15,C22orf23,C22orf31,C22orf39,C22orf42,C22orf46P,C2CD2,C2CD2L,C2CD3,C2CD4A,C2CD4B,C2CD4C,C2CD5,C2CD6,C2orf15,C2orf42,C2orf49,C2orf68,C2orf69,C2orf72,C2orf74,C2orf76,C2orf80,C2orf81,C2orf88,C3,C3AR1,C3P1,C3orf18,C3orf20,C3orf22,C3orf33,C3orf36,C3orf38,C3orf49,C3orf52,C3orf62,C3orf70,C3orf80,C4A,C4B,C4BPA,C4BPB,C4orf17,C4orf33,C4orf36,C4orf46,C5,C5AR1,C5AR2,C5orf15,C5orf22,C5orf24,C5orf34,C5orf46,C5orf47,C5orf58,C5orf63,C6,C6orf118,C6orf120,C6orf132,C6orf136,C6orf141,C6orf147,C6orf15,C6orf163,C6orf47,C6orf52,C6orf58,C6orf62,C6orf89,C7,C7orf25,C7orf33,C7orf57,C8A,C8B,C8G,C8orf17,C8orf33,C8orf34,C8orf44,C8orf48,C8orf58,C8orf74,C8orf76,C8orf82,C8orf88,C9,C9orf152,C9orf163,C9orf40,C9orf43,C9orf50,C9orf57,C9orf72,C9orf78,C9orf85,CA1,CA10,CA11,CA12,CA13,CA14,CA2,CA3,CA4,CA5A,CA5B,CA5BP1,CA6,CA7,CA8,CA9,CAAP1,CAB39,CAB39L,CABCOCO1,CABIN1,CABLES1,CABLES2,CABP1,CABP2,CABP4,CABP5,CABP7,CABS1,CABYR,CACFD1,CACHD1,CACNA1A,CACNA1B,CACNA1C,CACNA1D,CACNA1E,CACNA1F,CACNA1G,CACNA1H,CACNA1I,CACNA1S,CACNA2D1,CACNA2D2,CACNA2D3,CACNA2D4,CACNB1,CACNB2,CACNB3,CACNB4,CACNG1,CACNG2,CACNG3,CACNG4,CACNG5,CACNG6,CACNG7,CACNG8,CACTIN,CACUL1,CACYBP,CAD,CADM1,CADM2,CADM2-AS1,CADM3,CADM3-AS1,CADM4,CADPS,CADPS2,CAGE1,CAHM,CALB1,CALB2,CALCA,CALCB,CALCOCO1,CALCOCO2,CALCR,CALCRL,CALD1,CALHM1,CALHM2,CALHM3,CALHM4,CALHM5,CALHM6,CALM1,CALM2,CALM3,CALML3,CALML4,CALML5,CALML6,CALN1,CALR,CALR3,CALU,CALY,CAMK1,CAMK1D,CAMK1G,CAMK2A,CAMK2B,CAMK2D,CAMK2G,CAMK2N1,CAMK2N2,CAMK4,CAMKK1,CAMKK2,CAMKMT,CAMKV,CAMLG,CAMP,CAMSAP1,CAMSAP2,CAMSAP3,CAMTA1,CAMTA2,CAND1,CAND2,CANT1,CANX,CAP1,CAP2,CAPG,CAPN1,CAPN10,CAPN10-DT,CAPN11,CAPN12,CAPN13,CAPN14,CAPN15,CAPN2,CAPN3,CAPN5,CAPN6,CAPN7,CAPN8,CAPN9,CAPNS1,CAPNS2,CAPRIN1,CAPRIN2,CAPS,CAPS2,CAPSL,CAPZA1,CAPZA2,CAPZA3,CAPZB,CARD10,CARD11,CARD14,CARD16,CARD18,CARD19,CARD6,CARD8,CARD9,CARF,CARHSP1,CARINH,CARM1,CARMIL1,CARMIL2,CARMIL3,CARMN,CARNMT1,CARNS1,CARS1,CARS1-AS1,CARS2,CARTPT,CASC11,CASC15,CASC16,CASC17,CASC18,CASC2,CASC22,CASC3,CASC6,CASD1,CASK,CASKIN1,CASKIN2,CASP1,CASP10,CASP12,CASP14,CASP16P,CASP2,CASP3,CASP4,CASP5,CASP6,CASP7,CASP8,CASP8AP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4,CTAGE8,CTAGE9,CTBP1,CTBP1-AS,CTBP1-DT,CTBP2,CTBS,CTC1,CTCF,CTCFL,CTDNEP1,CTDP1,CTDSP1,CTDSP2,CTDSPL,CTDSPL2,CTF1,CTH,CTHRC1,CTIF,CTLA4,CTNNA1,CTNNA1P1,CTNNA2,CTNNA3,CTNNAL1,CTNNB1,CTNNBIP1,CTNNBL1,CTNND1,CTNND2,CTNS,CTPS1,CTPS2,CTR9,CTRB1,CTRB2,CTRC,CTRL,CTSA,CTSB,CTSC,CTSD,CTSE,CTSF,CTSG,CTSH,CTSK,CTSL,CTSLP8,CTSO,CTSS,CTSV,CTSW,CTSZ,CTTN,CTTNBP2,CTTNBP2NL,CTU2,CTXN1,CTXN2,CTXN3,CUBN,CUEDC1,CUEDC2,CUL1,CUL2,CUL3,CUL4A,CUL4B,CUL5,CUL7,CUL9,CUTA,CUTALP,CUTC,CUX1,CUX2,CUZD1,CWC15,CWC22,CWC25,CWC27,CWF19L1,CWF19L2,CWH43,CX3CL1,CX3CR1,CXADR,CXCL1,CXCL10,CXCL11,CXCL12,CXCL13,CXCL14,CXCL16,CXCL17,CXCL2,CXCL3,CXCL5,CXCL6,CXCL8,CXCL9,CXCR1,CXCR2,CXCR3,CXCR4,CXCR5,CXCR6,CXXC1,CXXC1P1,CXXC4,CXXC5,CYAT1,CYB561,CYB561A3,CYB561D1,CYB561D2,CYB5A,CYB5B,CYB5D1,CYB5D2,CYB5R1,CYB5R2,CYB5R3,CYB5R4,CYB5RL,CYBA,CYBB,CYBC1,CYBRD1,CYC1,CYCS,CYCSP19,CYCSP33,CYCSP44,CYFIP1,CYFIP2,CYGB,CYLC1,CYLC2,CYLD,CYMP,CYP11A1,CYP11B1,CYP11B2,CYP17A1,CYP19A1,CYP1A1,CYP1A2,CYP1B1,CYP1B1-AS1,CYP20A1,CYP21A1P,CYP21A2,CYP24A1,CYP26A1,CYP26B1,CYP27A1,CYP27B1,CYP27C1,CYP2A13,CYP2A6,CYP2A7,CYP2A7P1,CYP2B6,CYP2B7P,CYP2C18,CYP2C19,CYP2C8,CYP2C9,CYP2D6,CYP2D7,CYP2E1,CYP2F1,CYP2J2,CYP2R1,CYP2S1,CYP2U1,CYP2W1,CYP39A1,CYP3A4,CYP3A43,CYP3A5,CYP3A51P,CYP3A7,CYP46A1,CYP4A11,CYP4A22,CYP4B1,CYP4F11,CYP4F12,CYP4F2,CYP4F22,CYP4F29P,CYP4F3,CYP4F30P,CYP4F62P,CYP4F8,CYP4V2,CYP4X1,CYP4Z1,CYP4Z2P,CYP51A1,CYP7A1,CYP7B1,CYP8B1,CYREN,CYRIA,CYRIB,CYS1,CYSLTR1,CYSLTR2,CYSRT1,CYSTM1,CYTH1,CYTH2,CYTH3,CYTH4,CYTIP,CYTL1,CYYR1,CZIB,D21S2088E,D2HGDH,DAAM1,DAAM2,DAB1,DAB1-AS1,DAB2,DAB2IP,DACH1,DACH2,DACT1,DACT2,DACT3,DACT3-AS1,DAD1,DAG1,DAGLA,DAGLB,DALRD3,DANCR,DAND5,DAO,DAOA,DAOA-AS1,DAP,DAP3,DAPK1,DAPK1-IT1,DAPK2,DAPK3,DAPL1,DAPP1,DARS1,DARS2,DAW1,DAXX,DAZ1,DAZ2,DAZ3,DAZ4,DAZAP1,DAZAP2,DAZL,DBET,DBF4,DBF4B,DBH,DBH-AS1,DBI,DBIL5P,DBIL5P2,DBN1,DBNDD1,DBNDD2,DBNL,DBP,DBR1,DBT,DCAF1,DCAF10,DCAF11,DCAF12,DCAF12L1,DCAF12L2,DCAF13,DCAF15,DCAF16,DCAF17,DCAF4,DCAF4L1,DCAF4L2,DCAF5,DCAF6,DCAF7,DCAF8,DCAF8L2,DCAKD,DCANP1,DCBLD1,DCBLD2,DCC,DCD,DCDC1,DCDC2,DCDC2B,DCHS1,DCHS2,DCK,DCLK1,DCLK2,DCLK3,DCLRE1A,DCLRE1B,DCLRE1C,DCN,DCP1A,DCP1B,DCP2,DCPS,DCST1,DCST2,DCSTAMP,DCT,DCTD,DCTN1,DCTN1-AS1,DCTN2,DCTN3,DCTN4,DCTN5,DCTN6,DCTPP1,DCUN1D1,DCUN1D2,DCUN1D3,DCUN1D4,DCUN1D5,DCX,DCXR,DDA1,DDAH1,DDAH2,DDB1,DDB2,DDC,DDC-AS1,DDHD1,DDHD2,DDI1,DDI2,DDIAS,DDIT3,DDIT4,DDIT4L,DDN,DDO,DDOST,DDR1,DDR1-DT,DDR2,DDRGK1,DDT,DDTL,DDX1,DDX10,DDX11,DDX11-AS1,DDX11L2,DDX12P,DDX17,DDX18,DDX19A,DDX19B,DDX20,DDX21,DDX23,DDX24,DDX25,DDX27,DDX28,DDX31,DDX39A,DDX39B,DDX3X,DDX3Y,DDX4,DDX41,DDX42,DDX43,DDX46,DDX47,DDX49,DDX5,DDX50,DDX51,DDX52,DDX53,DDX54,DDX55,DDX56,DDX59,DDX6,DDX60,DDX60L,DEAF1,DECR1,DECR2,DEDD,DEDD2,DEF6,DEF8,DEFA1,DEFA1B,DEFA3,DEFA4,DEFA5,DEFA6,DEFB1,DEFB103A,DEFB103B,DEFB104A,DEFB104B,DEFB105A,DEFB105B,DEFB106A,DEFB106B,DEFB107A,DEFB107B,DEFB108B,DEFB114,DEFB118,DEFB119,DEFB121,DEFB122,DEFB123,DEFB124,DEFB125,DEFB126,DEFB127,DEFB129,DEFB132,DEFB4A,DEFB4B,DEFT1P,DEFT1P2,DEGS1,DEGS2,DEK,DELE1,DELEC1,DENND10,DENND10P1,DENND11,DENND1A,DENND1B,DENND1C,DENND2A,DENND2B,DENND2C,DENND2D,DENND3,DENND4A,DENND4B,DENND4C,DENND5A,DENND5B,DENND6A,DENND6B,DENR,DEPDC1,DEPDC1-AS1,DEPDC1B,DEPDC4,DEPDC5,DEPDC7,DEPP1,DEPTOR,DERA,DERL1,DERL2,DERL3,DES,DESI1,DESI2,DET1,DEUP1,DEXI,DFFA,DFFB,DGAT1,DGAT2,DGCR11,DGCR2,DGCR5,DGCR6,DGCR6L,DGCR8,DGKA,DGKB,DGKD,DGKE,DGKG,DGKH,DGKI,DGKK,DGKQ,DGKZ,DGLUCY,DGUOK,DHCR24,DHCR7,DHDDS,DHDH,DHFR,DHFR2,DHH,DHODH,DHPS,DHRS1,DHRS11,DHRS12,DHRS13,DHRS2,DHRS3,DHRS4,DHRS4-AS1,DHRS4L2,DHRS7,DHRS7B,DHRS7C,DHRS9,DHRSX,DHTKD1,DHX15,DHX16,DHX29,DHX30,DHX32,DHX33,DHX34,DHX35,DHX36,DHX37,DHX38,DHX40,DHX40P1,DHX57,DHX58,DHX8,DHX9,DIABLO,DIAPH1,DIAPH2,DIAPH2-AS1,DIAPH3,DIAPH3-AS1,DICER1,DICER1-AS1,DIDO1,DIMT1,DIO1,DIO2,DIO2-AS1,DIO3,DIO3OS,DIP2A,DIP2B,DIP2C,DIP2C-AS1,DIPK1A,DIPK1B,DIPK1C,DIPK2A,DIRAS1,DIRAS2,DIRAS3,DIRC1,DIRC3,DIS3,DIS3L,DIS3L2,DISC1,DISP1,DISP2,DISP3,DIXDC1,DKC1,DKK1,DKK2,DKK3,DKK4,DKKL1,DKKL1P1,DLAT,DLC1,DLD,DLEC1,DLEU1,DLEU2,DLEU2L,DLEU7,DLEU7-AS1,DLG1,DLG1-AS1,DLG2,DLG3,DLG3-AS1,DLG4,DLG5,DLG5-AS1,DLGAP1,DLGAP1-AS1,DLGAP1-AS2,DLGAP1-AS3,DLGAP2,DLGAP2-AS1,DLGAP3,DLGAP4,DLGAP5,DLK1,DLK2,DLL1,DLL3,DLL4,DLST,DLX1,DLX2,DLX3,DLX4,DLX5,DLX6,DLX6-AS1,DMAC1,DMAC2,DMAC2L,DMAP1,DMBT1,DMBX1,DMC1,DMD,DMGDH,DMKN,DMP1,DMPK,DMRT1,DMRT2,DMRT3,DMRTA1,DMRTA2,DMRTB1,DMRTC1,DMRTC1B,DMRTC2,DMTF1,DMTN,DMWD,DMXL1,DMXL2,DNA2,DNAAF1,DNAAF10,DNAAF11,DNAAF19,DNAAF2,DNAAF3,DNAAF4,DNAAF5,DNAAF6,DNAAF8,DNAAF9,DNAH1,DNAH10,DNAH11,DNAH12,DNAH14,DNAH17,DNAH17-AS1,DNAH2,DNAH3,DNAH5,DNAH6,DNAH7,DNAH8,DNAH9,DNAI1,DNAI2,DNAI3,DNAI4,DNAI7,DNAJA1,DNAJA1P4,DNAJA2,DNAJA3,DNAJA4,DNAJB1,DNAJB11,DNAJB12,DNAJB13,DNAJB14,DNAJB2,DNAJB4,DNAJB5,DNAJB6,DNAJB7,DNAJB8,DNAJB8-AS1,DNAJB9,DNAJC1,DNAJC10,DNAJC11,DNAJC12,DNAJC13,DNAJC14,DNAJC15,DNAJC16,DNAJC17,DNAJC18,DNAJC19,DNAJC2,DNAJC21,DNAJC22,DNAJC24,DNAJC25,DNAJC27,DNAJC28,DNAJC3,DNAJC3-DT,DNAJC30,DNAJC4,DNAJC5,DNAJC5B,DNAJC5G,DNAJC6,DNAJC7,DNAJC8,DNAJC9,DNAJC9-AS1,DNAL1,DNAL4,DNALI1,DNASE1,DNASE1L1,DNASE1L2,DNASE1L3,DNASE2,DNASE2B,DND1,DNER,DNHD1,DNLZ,DNM1,DNM1L,DNM1P35,DNM1P41,DNM1P46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P1,FUBP3,FUCA1,FUCA2,FUNDC1,FUNDC2,FUOM,FURIN,FUS,FUT1,FUT10,FUT11,FUT2,FUT3,FUT4,FUT5,FUT6,FUT7,FUT8,FUT8-AS1,FUT9,FUZ,FXN,FXR1,FXR2,FXYD1,FXYD2,FXYD3,FXYD4,FXYD5,FXYD6,FXYD7,FYB1,FYB2,FYCO1,FYN,FYTTD1,FZD1,FZD10,FZD10-AS1,FZD2,FZD3,FZD4,FZD5,FZD6,FZD7,FZD8,FZD9,FZR1,G0S2,G2E3,G3BP1,G3BP2,G6PC1,G6PC2,G6PC3,G6PD,GAA,GAB1,GAB2,GAB3,GAB4,GABARAP,GABARAPL1,GABARAPL2,GABARAPL3,GABBR1,GABBR2,GABPA,GABPB1,GABPB1-AS1,GABPB2,GABRA1,GABRA2,GABRA3,GABRA4,GABRA5,GABRA6,GABRB1,GABRB2,GABRB3,GABRD,GABRE,GABRG1,GABRG2,GABRG3,GABRP,GABRQ,GABRR1,GABRR2,GABRR3,GAD1,GAD2,GADD45A,GADD45B,GADD45G,GADD45GIP1,GADL1,GAGE1,GAGE12B,GAGE12C,GAGE12D,GAGE12E,GAGE12F,GAGE12G,GAGE12H,GAGE12I,GAGE12J,GAGE13,GAGE2A,GAGE2B,GAGE2C,GAGE2D,GAGE2E,GAGE4,GAGE5,GAGE6,GAGE7,GAGE8,GAK,GAL,GAL3ST1,GAL3ST2,GAL3ST3,GAL3ST4,GALC,GALE,GALK1,GALK2,GALM,GALNS,GALNT1,GALNT10,GALNT11,GALNT12,GALNT13,GALNT14,GALNT15,GALNT16,GALNT17,GALNT18,GALNT2,GALNT3,GALNT4,GALNT5,GALNT6,GALNT7,GALNT8,GALNT9,GALNTL5,GALNTL6,GALP,GALR1,GALR2,GALR3,GALT,GAMT,GAN,GANAB,GANC,GAP43,GAPDH,GAPDHP62,GAPDHP73,GAPDHS,GAPT,GAPVD1,GAR1,GAREM1,GAREM2,GARIN1A,GARIN1B,GARIN2,GARIN3,GARIN4,GARIN5A,GARIN5B,GARIN6,GARNL3,GARRE1,GART,GAS1,GAS2,GAS2L1,GAS2L2,GAS2L3,GAS5,GAS5-AS1,GAS6,GAS6-AS1,GAS7,GAS8,GAS8-AS1,GASK1A,GASK1B,GAST,GATA1,GATA2,GATA3,GATA3-AS1,GATA4,GATA5,GATA6,GATA6-AS1,GATAD1,GATAD2A,GATAD2B,GATB,GATC,GATD1,GATD1-DT,GATD3,GATM,GBA1LP,GBA2,GBA3,GBE1,GBF1,GBGT1,GBP1,GBP1P1,GBP2,GBP3,GBP4,GBP5,GBP6,GBX1,GBX2,GC,GCA,GCAT,GCC1,GCC2,GCDH,GCFC2,GCG,GCGR,GCH1,GCHFR,GCK,GCKR,GCLC,GCLM,GCM1,GCM2,GCN1,GCNA,GCNT1,GCNT1P1,GCNT2,GCNT3,GCNT4,GCNT7,GCOM1,GCSAM,GCSAML,GCSAML-AS1,GCSH,GDA,GDAP1,GDAP1L1,GDAP2,GDE1,GDF1,GDF10,GDF11,GDF15,GDF2,GDF3,GDF5,GDF9,GDI1,GDI2,GDNF,GDPD1,GDPD2,GDPD3,GDPD5,GDPGP1,GEM,GEMIN2,GEMIN4,GEMIN5,GEMIN6,GEMIN7,GEMIN8,GEN1,GET1,GET3,GET4,GFAP,GFER,GFI1,GFI1B,GFM1,GFM2,GFOD1,GFOD2,GFOD3P,GFPT1,GFPT2,GFRA1,GFRA2,GFRA3,GFRA4,GFUS,GGA1,GGA2,GGA3,GGACT,GGCT,GGCX,GGH,GGN,GGNBP2,GGPS1,GGT1,GGT3P,GGT5,GGT6,GGT7,GGT8P,GGTA2P,GGTLC1,GGTLC2,GH1,GH2,GHDC,GHET1,GHITM,GHR,GHRH,GHRHR,GHRL,GHRLOS,GHSR,GID4,GID8,GIGYF1,GIGYF2,GIMAP1,GIMAP2,GIMAP4,GIMAP5,GIMAP6,GIMAP7,GIMAP8,GIN1,GINM1,GINS1,GINS2,GINS3,GINS4,GIP,GIPC1,GIPC2,GIPC3,GIPR,GIT1,GIT2,GJA1,GJA10,GJA3,GJA4,GJA5,GJA8,GJA9,GJA9-MYCBP,GJB1,GJB2,GJB3,GJB4,GJB5,GJB6,GJC1,GJC2,GJD2,GJD3,GJD4,GK,GK-AS1,GK2,GK3,GK5,GKAP1,GKN1,GKN2,GLA,GLB1,GLB1L,GLB1L2,GLB1L3,GLCCI1,GLCE,GLDC,GLDN,GLE1,GLG1,GLI1,GLI2,GLI3,GLI4,GLIDR,GLIPR1,GLIPR1L1,GLIPR2,GLIS1,GLIS2,GLIS3,GLIS3-AS1,GLMN,GLMP,GLO1,GLOD4,GLOD5,GLP1R,GLP2R,GLRA1,GLRA2,GLRA3,GLRB,GLRX,GLRX2,GLRX3,GLRX5,GLS,GLS2,GLT1D1,GLT8D1,GLT8D2,GLTP,GLTPD2,GLUD1,GLUD2,GLUL,GLULP4,GLYAT,GLYATL1,GLYATL2,GLYCTK,GLYCTK-AS1,GLYR1,GM2A,GMCL1,GMCL2,GMDS,GMDS-DT,GMEB1,GMEB2,GMFB,GMFG,GMIP,GML,GMNC,GMNN,GMPPA,GMPPB,GMPR,GMPR2,GMPS,GNA11,GNA12,GNA13,GNA14,GNA14-AS1,GNA15,GNAI1,GNAI2,GNAI3,GNAL,GNAO1,GNAQ,GNAS,GNAS-AS1,GNAT1,GNAT2,GNAT3,GNAZ,GNB1,GNB1L,GNB2,GNB3,GNB4,GNB5,GNE,GNG10,GNG11,GNG12,GNG13,GNG2,GNG3,GNG4,GNG5,GNG7,GNG8,GNGT1,GNGT2,GNL1,GNL2,GNL3,GNL3L,GNLY,GNMT,GNPAT,GNPDA1,GNPDA2,GNPNAT1,GNPTAB,GNPTG,GNRH1,GNRH2,GNRHR,GNRHR2,GNS,GOLGA1,GOLGA2,GOLGA2P2Y,GOLGA2P3Y,GOLGA2P5,GOLGA2P7,GOLGA3,GOLGA4,GOLGA5,GOLGA6A,GOLGA6B,GOLGA6C,GOLGA6D,GOLGA6L1,GOLGA6L11P,GOLGA6L16P,GOLGA6L2,GOLGA6L22,GOLGA6L4,GOLGA6L5P,GOLGA6L6,GOLGA6L9,GOLGA7,GOLGA7B,GOLGA8A,GOLGA8B,GOLGA8CP,GOLGA8DP,GOLGA8EP,GOLGA8F,GOLGA8G,GOLGA8IP,GOLGA8M,GOLGA8N,GOLGB1,GOLIM4,GOLM1,GOLM2,GOLPH3,GOLPH3L,GOLT1A,GOLT1B,GON4L,GON7,GOPC,GORAB,GORASP1,GORASP2,GOSR1,GOSR2,GOT1,GOT1L1,GOT2,GOT2P1,GP1BA,GP1BB,GP2,GP5,GP6,GP9,GPA33,GPAA1,GPALPP1,GPAM,GPANK1,GPAT2,GPAT3,GPAT4,GPATCH1,GPATCH11,GPATCH2,GPATCH2L,GPATCH3,GPATCH4,GPATCH8,GPBAR1,GPBP1,GPBP1L1,GPC1,GPC2,GPC3,GPC4,GPC5,GPC6,GPCPD1,GPD1,GPD1L,GPD2,GPER1,GPHA2,GPHB5,GPHN,GPI,GPIHBP1,GPKOW,GPLD1,GPM6A,GPM6B,GPN1,GPN2,GPN3,GPNMB,GPR101,GPR107,GPR108,GPR12,GPR132,GPR135,GPR137,GPR137B,GPR137C,GPR143,GPR146,GPR15,GPR150,GPR153,GPR155,GPR156,GPR157,GPR158,GPR15LG,GPR160,GPR161,GPR162,GPR17,GPR171,GPR173,GPR174,GPR176,GPR18,GPR180,GPR182,GPR183,GPR19,GPR20,GPR21,GPR22,GPR25,GPR26,GPR27,GPR3,GPR31,GPR32,GPR34,GPR35,GPR37,GPR37L1,GPR39,GPR4,GPR42,GPR45,GPR50,GPR52,GPR55,GPR6,GPR61,GPR62,GPR63,GPR65,GPR68,GPR75,GPR75-ASB3,GPR78,GPR82,GPR83,GPR84,GPR85,GPR87,GPR88,GPR89A,GPR89B,GPRASP1,GPRASP2,GPRC5A,GPRC5B,GPRC5C,GPRC5D,GPRC6A,GPRIN1,GPRIN2,GPRIN3,GPS1,GPS2,GPSM1,GPSM2,GPSM3,GPT,GPT2,GPX1,GPX2,GPX3,GPX4,GPX5,GPX7,GPX8,GRAMD1A,GRAMD1B,GRAMD1C,GRAMD2A,GRAMD2B,GRAMD4,GRAP,GRAP2,GRAPL,GRB10,GRB14,GRB2,GRB7,GREB1,GREB1L,GREM1,GREM2,GRHL1,GRHL2,GRHL3,GRHPR,GRIA1,GRIA2,GRIA3,GRIA4,GRID1,GRID1-AS1,GRID2,GRIK1,GRIK1-AS1,GRIK1-AS2,GRIK2,GRIK3,GRIK4,GRIK5,GRIN1,GRIN2A,GRIN2B,GRIN2C,GRIN2D,GRIN3A,GRIN3B,GRINA,GRIP1,GRIP2,GRIPAP1,GRK1,GRK2,GRK3,GRK4,GRK5,GRK6,GRK7,GRM1,GRM2,GRM3,GRM4,GRM5,GRM6,GRM7,GRM8,GRN,GRP,GRPEL1,GRPEL2,GRPR,GRSF1,GRTP1,GRTP1-AS1,GRWD1,GSAP,GSC,GSC2,GSDMB,GSDMC,GSDMD,GSDME,GSE1,GSG1,GSG1L,GSK3A,GSK3B,GSKIP,GSN,GSN-AS1,GSPT1,GSPT2,GSR,GSS,GSTA1,GSTA3,GSTA4,GSTCD,GSTK1,GSTM1,GSTM2,GSTM3,GSTM4,GSTM5,G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1304,MIR1306,MIR133A1,MIR133A1HG,MIR137,MIR137HG,MIR142,MIR145,MIR146A,MIR155,MIR155HG,MIR15A,MIR17,MIR17HG,MIR181A2HG,MIR186,MIR18A,MIR1908,MIR1909,MIR1915HG,MIR194-2,MIR195,MIR196B,MIR19A,MIR19B1,MIR205,MIR205HG,MIR20A,MIR21,MIR210HG,MIR22,MIR224,MIR22HG,MIR23A,MIR24-2,MIR2682,MIR29A,MIR29B1,MIR29B2,MIR29C,MIR302B,MIR3064,MIR3150A,MIR3150B,MIR3180-1,MIR3180-2,MIR3180-3,MIR3185,MIR3190,MIR3191,MIR31HG,MIR34A,MIR3610,MIR3620,MIR3650,MIR3652,MIR3654,MIR3658,MIR3663HG,MIR3667HG,MIR3671,MIR3682,MIR3916,MIR3917,MIR3934,MIR424,MIR4271,MIR4296,MIR4313,MIR4315-1,MIR4315-2,MIR4321,MIR4453,MIR4454,MIR4458,MIR4458HG,MIR4467,MIR4500HG,MIR452,MIR4640,MIR4647,MIR4656,MIR4657,MIR4680,MIR4683,MIR4691,MIR4697,MIR4707,MIR4723,MIR4738,MIR4745,MIR4746,MIR4755,MIR4784,MIR4800,MIR490,MIR492,MIR497,MIR497HG,MIR503,MIR503HG,MIR5047,MIR5188,MIR5193,MIR548F5,MIR600,MIR600HG,MIR6080,MIR612,MIR6125,MIR6132,MIR622,MIR631,MIR636,MIR637,MIR646HG,MIR6513,MIR6516,MIR663AHG,MIR664B,MIR670HG,MIR6716,MIR6727,MIR6732,MIR6733,MIR6734,MIR6741,MIR6743,MIR6748,MIR675,MIR6751,MIR6756,MIR6758,MIR6773,MIR6778,MIR6785,MIR6787,MIR6789,MIR6791,MIR6800,MIR6805,MIR6824,MIR6831,MIR6834,MIR6836,MIR6837,MIR6840,MIR6845,MIR6849,MIR6859-1,MIR6859-2,MIR6866,MIR6872,MIR6875,MIR6878,MIR6883,MIR6884,MIR6890,MIR7-3HG,MIR7109,MIR7110,MIR7111,MIR7112,MIR7113,MIR7114,MIR7515,MIR7515HG,MIR7703,MIR8071-1,MIR8071-2,MIR8072,MIR8085,MIR9-1HG,MIR9-2,MIR92A1,MIR939,MIR943,MIRLET7BHG,MIRLET7D,MIS12,MIS18A,MIS18BP1,MISP,MITD1,MITF,MIX23,MIXL1,MKI67,MKKS,MKLN1,MKNK1,MKNK1-AS1,MKNK2,MKRN1,MKRN2,MKRN3,MKRN4P,MKRN7P,MKS1,MKX,MLANA,MLC1,MLEC,MLF1,MLF2,MLH1,MLH3,MLIP,MLKL,MLLT1,MLLT10,MLLT10P1,MLLT11,MLLT3,MLLT6,MLN,MLNR,MLPH,MLST8,MLX,MLXIP,MLXIPL,MLYCD,MMAA,MMAB,MMACHC,MMADHC,MMD,MMD2,MME,MMEL1,MMGT1,MMP1,MMP10,MMP11,MMP12,MMP13,MMP14,MMP15,MMP16,MMP17,MMP19,MMP2,MMP20,MMP21,MMP23A,MMP23B,MMP24,MMP24OS,MMP25,MMP26,MMP27,MMP28,MMP3,MMP7,MMP8,MMP9,MMRN1,MMRN2,MMS19,MMUT,MN1,MNAT1,MND1,MNDA,MNS1,MNT,MNX1,MNX1-AS1,MOAP1,MOB1A,MOB1B,MOB2,MOB3A,MOB3B,MOB3C,MOB4,MOBP,MOCOS,MOCS1,MOCS2,MOCS3,MOG,MOGAT1,MOGAT2,MOGAT3,MOGS,MOK,MON1A,MON1B,MON2,MORC1,MORC2,MORC2-AS1,MORC3,MORC4,MORF4L1,MORF4L2,MORN1,MORN2,MORN3,MORN4,MORN5,MOS,MOSMO,MOSPD1,MOSPD2,MOSPD3,MOV10,MOV10L1,MOXD1,MPC1,MPC2,MPDU1,MPDZ,MPEG1,MPG,MPHOSPH10,MPHOSPH6,MPHOSPH8,MPHOSPH9,MPI,MPIG6B,MPL,MPLKIP,MPND,MPO,MPP1,MPP2,MPP3,MPP4,MPP7,MPPE1,MPPED1,MPPED2,MPRIP,MPRIP-AS1,MPST,MPV17,MPV17L,MPV17L2,MPZ,MPZL1,MPZL2,MPZL3,MR1,MRAP,MRAP2,MRAS,MRC1,MRC2,MRE11,MREG,MRFAP1,MRFAP1L1,MRGBP,MRGPRF,MRGPRG-AS1,MRGPRX1,MRGPRX2,MRGPRX3,MRGPRX4,MRI1,MRM1,MRM2,MRM3,MRNIP,MRO,MROH1,MROH2A,MROH2B,MROH6,MROH7,MROH7-TTC4,MROH8,MROH9,MRPL1,MRPL10,MRPL11,MRPL12,MRPL13,MRPL14,MRPL15,MRPL16,MRPL17,MRPL18,MRPL19,MRPL2,MRPL20,MRPL21,MRPL22,MRPL23,MRPL24,MRPL27,MRPL28,MRPL3,MRPL30,MRPL32,MRPL33,MRPL34,MRPL35,MRPL36,MRPL37,MRPL38,MRPL39,MRPL4,MRPL40,MRPL41,MRPL42,MRPL43,MRPL44,MRPL45,MRPL45P2,MRPL46,MRPL47,MRPL48,MRPL49,MRPL49P1,MRPL50,MRPL51,MRPL52,MRPL53,MRPL54,MRPL55,MRPL57,MRPL58,MRPL9,MRPS10,MRPS11,MRPS11P1,MRPS12,MRPS14,MRPS15,MRPS16,MRPS17,MRPS18A,MRPS18B,MRPS18C,MRPS2,MRPS21,MRPS22,MRPS23,MRPS24,MRPS25,MRPS26,MRPS27,MRPS28,MRPS30,MRPS31,MRPS31P5,MRPS33,MRPS34,MRPS35,MRPS5,MRPS6,MRPS7,MRPS9,MRRF,MRS2,MRTFA,MRTFB,MRTO4,MS4A1,MS4A12,MS4A14,MS4A15,MS4A2,MS4A3,MS4A4A,MS4A5,MS4A6A,MS4A6E,MS4A7,MS4A8,MSANTD1,MSANTD2,MSANTD3,MSANTD4,MSC,MSH2,MSH3,MSH4,MSH5,MSH6,MSI1,MSI2,MSL1,MSL2,MSL3,MSLN,MSMB,MSMO1,MSN,MSR1,MSRA,MSRB1,MSRB2,MSRB3,MSS51,MST1,MST1L,MST1P2,MST1R,MSTN,MSTO1,MSTO2P,MSX1,MSX2,MT1E,MT1F,MT1G,MT1H,MT1HL1,MT1M,MT1X,MT2A,MT3,MT4,MTA1,MTA2,MTA3,MTAP,MTARC1,MTARC2,MTBP,MTCH1,MTCH2,MTCL1,MTCL2,MTCL3,MTCP1,MTDH,MTERF1,MTERF2,MTERF3,MTERF4,MTF1,MTF2,MTFMT,MTFP1,MTFR1,MTFR1L,MTFR2,MTG1,MTG2,MTHFD1,MTHFD1L,MTHFD2,MTHFD2L,MTHFR,MTHFS,MTHFSD,MTIF2,MTIF3,MTM1,MTMR1,MTMR10,MTMR11,MTMR12,MTMR14,MTMR2,MTMR3,MTMR4,MTMR6,MTMR7,MTMR8,MTMR9,MTMR9LP,MTNAP1,MTND1P33,MTNR1A,MTNR1B,MTO1,MTOR,MTPAP,MTPN,MTR,MTRES1,MTREX,MTRF1,MTRF1L,MTRFR,MTRNR2L1,MTRR,MTSS1,MTSS2,MTTP,MTURN,MTUS1,MTUS2,MTUS2-AS1,MTX1,MTX2,MTX3,MUC1,MUC12,MUC13,MUC15,MUC16,MUC17,MUC19,MUC2,MUC20,MUC3A,MUC3B,MUC4,MUC5AC,MUC5B,MUC6,MUC7,MUC8,MUCL1,MUCL3,MUL1,MUS81,MUSK,MUSTN1,MUTYH,MVB12A,MVB12B,MVD,MVK,MVP,MX1,MX2,MXD1,MXD3,MXD4,MXI1,MXRA5,MXRA7,MXRA8,MYADM,MYADML2,MYB,MYBBP1A,MYBL1,MYBL2,MYBPC1,MYBPC2,MYBPC3,MYBPH,MYC,MYCBP,MYCBP2,MYCBPAP,MYCL,MYCLP1,MYCN,MYCNOS,MYCNUT,MYCT1,MYD88,MYDGF,MYEF2,MYEOV,MYF5,MYF6,MYG1,MYH1,MYH10,MYH11,MYH13,MYH14,MYH15,MYH16,MYH2,MYH3,MYH4,MYH6,MYH7,MYH7B,MYH8,MYH9,MYL1,MYL10,MYL11,MYL12A,MYL12B,MYL2,MYL3,MYL4,MYL5,MYL6,MYL6B,MYL7,MYL9,MYLIP,MYLK,MYLK-AS1,MYLK2,MYLK3,MYLK4,MYNN,MYO10,MYO15A,MYO15B,MYO16,MYO18A,MYO18B,MYO19,MYO1A,MYO1B,MYO1C,MYO1D,MYO1E,MYO1F,MYO1G,MYO1H,MYO3A,MYO3B,MYO5A,MYO5B,MYO5C,MYO6,MYO7A,MYO7B,MYO9A,MYO9B,MYOC,MYOCD,MYOD1,MYOF,MYOG,MYOM1,MYOM2,MYOM3,MYORG,MYOT,MYOZ1,MYOZ2,MYOZ3,MYPN,MYPOP,MYRF,MYRFL,MYRIP,MYSM1,MYT1,MYT1L,MYZAP,MZB1,MZF1,MZT1,MZT2A,MZT2B,N4BP1,N4BP2,N4BP2L1,N4BP2L2,N4BP3,N6AMT1,NAA10,NAA11,NAA15,NAA16,NAA20,NAA25,NAA30,NAA35,NAA38,NAA40,NAA50,NAA60,NAA80,NAAA,NAALAD2,NAALADL1,NAALADL2,NAB1,NAB2,NABP1,NABP2,NACA,NACA2,NACA4P,NACAD,NACC1,NACC2,NADK,NADK2,NADK2-AS1,NADSYN1,NAE1,NAF1,NAGA,NAGK,NAGLU,NAGPA,NAGPA-AS1,NAGS,NAIF1,NAIP,NALCN,NALCN-AS1,NALF1,NALF2,NAMPT,NANOG,NANOS1,NANOS2,NANOS3,NANP,NANS,NAP1L1,NAP1L2,NAP1L3,NAP1L4,NAP1L5,NAPA,NAPA-AS1,NAPB,NAPEPLD,NAPG,NAPRT,NAPSA,NAPSB,NARF,NARS1,NARS2,NASP,NAT1,NAT10,NAT14,NAT16,NAT2,NAT8,NAT8B,NAT8L,NAT9,NATD1,NAV1,NAV2,NAV2-AS2,NAV2-AS4,NAV2-AS5,NAV2-IT1,NAV3,NAXD,NAXE,NBAS,NBAT1,NBEA,NBEAL1,NBEAL2,NBEAP1,NBL1,NBN,NBPF1,NBPF10,NBPF11,NBPF12,NBPF14,NBPF15,NBPF19,NBPF20,NBPF25P,NBPF26,NBPF3,NBPF4,NBPF5P,NBPF8,NBPF9,NBR1,NBR2,NCALD,NCAM1,NCAM2,NCAN,NCAPD2,NCAPD3,NCAPG,NCAPG2,NCAPH,NCAPH2,NCBP1,NCBP2,NCBP2AS2,NCBP3,NCDN,NCEH1,NCF1,NCF1B,NCF1C,NCF2,NCF4,NCK1,NCK1-DT,NCK2,NCKAP1,NCKAP1L,NCKAP5,NCKAP5L,NCKIPSD,NCL,NCLN,NCOA1,NCOA2,NCOA3,NCOA4,NCOA5,NCOA6,NCOA7,NCOR1,NCOR2,NCR1,NCR2,NCR3,NCR3LG1,NCS1,NCSTN,ND2,ND3,ND4,ND6,NDC1,NDC80,NDE1,NDEL1,NDFIP1,NDFIP2,NDN,NDNF,NDOR1,NDP,NDRG1,NDRG2,NDRG3,NDRG4,NDST1,NDST2,NDST3,NDST4,NDUFA1,NDUFA10,NDUFA11,NDUFA12,NDUFA13,NDUFA2,NDUFA3,NDUFA4,NDUFA4L2,NDUFA5,NDUFA6,NDUFA6-DT,NDUFA7,NDUFA8,NDUFA9,NDUFAB1,NDUFAF1,NDUFAF2,NDUFAF3,NDUFAF4,NDUFAF5,NDUFAF6,NDUFAF7,NDUFAF8,NDUFB1,NDUFB10,NDUFB11,NDUFB2,NDUFB2-AS1,NDUFB3,NDUFB4,NDUFB5,NDUFB6,NDUFB7,NDUFB8,NDUFB9,NDUFC1,NDUFC2,NDUFS1,NDUFS2,NDUFS3,NDUFS4,NDUFS5,NDUFS6,NDUFS7,NDUFS8,NDUFV1,NDUFV1-DT,NDUFV2,NDUFV2-AS1,NDUFV3,NEAT1,NEB,NEBL,NEBL-AS1,NECAB1,NECAB2,NECAB3,NECAP1,NECAP2,NECTIN1,NECTIN2,NECTIN3,NECTIN3-AS1,NECTIN4,NEDD1,NEDD4,NEDD4L,NEDD8,NEDD8-MDP1,NEDD9,NEFH,NEFL,NEFM,NEGR1,NEGR1-IT1,NEIL1,NEIL2,NEIL3,NEK1,NEK10,NEK11,NEK2,NEK3,NEK4,NEK5,NEK6,NEK7,NEK8,NEK9,NELFA,NELFB,NELFCD,NELFE,NELL1,NELL2,NEMF,NEMP1,NEMP2,NENF,NEO1,NEPRO,NES,NET1,NETO1,NETO2,NEU1,NEU2,NEU3,NEU4,NEURL1,NEURL1B,NEURL2,NEURL3,NEURL4,NEUROD1,NEUROD2,NEUROD4,NEUROD6,NEUROG1,NEUROG2,NEUROG3,NEXMIF,NEXN,NEXN-AS1,NF1,NF2,NFAM1,NFASC,NFAT5,NFATC1,NFATC2,NFATC2IP,NFATC3,NFATC4,NFE2,NFE2L1,NFE2L2,NFE2L3,NFE4,NFIA,NFIA-AS2,NFIB,NFIC,NFIL3,NFIX,NFKB1,NFKB2,NFKBIA,NFKBIB,NFKBID,NFKBIE,NFKBIL1,NFKBIZ,NFRKB,NFS1,NFU1,NFX1,NFXL1,NFYA,NFYB,NFYC,NFYC-AS1,NGB,NGDN,NGEF,NGF,NGFR,NGLY1,NGRN,NHEG1,NHEJ1,NHERF1,NHERF2,NHERF4,NHLH1,NHLH2,NHLRC2,NHLRC3,NHLRC4,NHP2,NHP2P2,NHS,NHSL1,NHSL2,NHSL3,NIBAN1,NIBAN2,NIBAN3,NICN1,NICOL1,NID1,NID2,NIF3L1,NIFK,NIFK-AS1,NIM1K,NIN,NINJ1,NINJ2,NINL,NIP7,NIPA1,NIPA2,NIPAL1,NIPAL2,NIPAL3,NIPAL4,NIPBL,NIPSNAP1,NIPSNAP2,NIPSNAP3A,NIPSNAP3B,NISCH,NIT1,NIT2,NKAIN1,NKAIN2,NKAIN3,NKAIN4,NKAP,NKAPD1,NKAPL,NKAPP1,NKD1,NKD2,NKG7,NKIRAS1,NKIRAS2,NKPD1,NKRF,NKTR,NKX1-1,NKX2-1,NKX2-1-AS1,NKX2-2,NKX2-3,NKX2-5,NKX2-8,NKX3-1,NKX3-2,NKX6-1,NKX6-2,NKX6-3,NLE1,NLGN1,NLGN2,NLGN3,NLGN4X,NLGN4Y,NLK,NLN,NLRC3,NLRC4,NLRC5,NLRP1,NLRP10,NLRP11,NLRP12,NLRP13,NLRP14,NLRP2,NLRP3,NLRP4,NLRP5,NLRP6,NLRP7,NLRP8,NLRP9,NLRX1,NMB,NMBR,NMD3,NME1,NME1-NME2,NME2,NME3,NME4,NME5,NME6,NME7,NME8,NME9,NMI,NMNAT1,NMNAT2,NMNAT3,NMRAL1,NMRK1,NMRK2,NMT1,NMT2,NMU,NMUR1,NMUR2,NNAT,NNMT,NNT,NNT-AS1,NOA1,NOB1,NOBOX,NOC2L,NOC3L,NOC4L,NOCT,NOD1,NOD2,NODAL,NOG,NOL10,NOL11,NOL12,NOL3,NOL4,NOL4L,NOL6,NOL7,NOL8,NOL9,NOLC1,NOM1,NOMO1,NOMO2,NOMO3,NONO,NOP10,NOP14,NOP14-AS1,NOP16,NOP2,NOP53,NOP56,NOP58,NOP9,NOPCHAP1,NOS1,NOS1AP,NOS2,NOS3,NOSIP,NOSTRIN,NOTCH1,NOTCH2,NOTCH2NLB,NOTCH3,NOTCH4,NOTUM,NOVA1,NOVA2,NOX1,NOX3,NOX4,NOX5,NOXA1,NOXO1,NOXRED1,NPAP1,NPAS1,NPAS2,NPAS3,NPAS4,NPAT,NPB,NPBWR1,NPBWR2,NPC1,NPC1L1,NPC2,NPDC1,NPEPL1,NPEPPS,NPFF,NPFFR1,NPFFR2,NPHP1,NPHP3,NPHP3-AS1,NPHP4,NPHS1,NPHS2,NPIPA1,NPIPA2,NPIPA3,NPIPA5,NPIPA7,NPIPA8,NPIPB11,NPIPB15,NPIPB3,NPIPB4,NPIPB5,NPIPB6,NPIPB8,NPL,NPLOC4,NPM1,NPM1P22,NPM2,NPM3,NPNT,NPPA,NPPB,NPPC,NPR1,NPR2,NPR3,NPRL2,NPRL3,NPSA,NPSR1-AS1,NPTN,NPTN-IT1,NPTX1,NPTX2,NPTXR,NPVF,NPW,NPY,NPY1R,NPY2R,NPY4R,NPY5R,NPY6R,NQO1,NQO2,NR0B1,NR0B2,NR1D1,NR1D2,NR1H2,NR1H3,NR1H4,NR1I2,NR1I3,NR2C1,NR2C2,NR2C2AP,NR2E1,NR2E3,NR2F1,NR2F1-AS1,NR2F2,NR2F2-AS1,NR2F6,NR3C1,NR3C2,NR4A1,NR4A2,NR4A3,NR5A1,NR5A2,NR6A1,NRAP,NRARP,NRAS,NRAV,NRBF2,NRBP1,NRBP2,NRCAM,NRDC,NRDE2,NREP,NRF1,NRG1,NRG1-IT1,NRG2,NRG3,NRG4,NRGN,NRIP1,NRIP2,NRIP3,NRK,NRL,NRM,NRN1,NRP1,NRP2,NRROS,NRSN1,NRSN2,NRTN,NRXN1,NRXN2,NRXN3,NSA2,NSD1,NSD2,NSD3,NSDHL,NSF,NSFL1C,NSG1,NSG2,NSL1,NSMAF,NSMCE1,NSMCE2,NSMCE3,NSMCE4A,NSMF,NSRP1,NSUN2,NSUN3,NSUN4,NSUN5,NSUN5P1,NSUN5P2,NSUN6,NSUN7,NT5C,NT5C1A,NT5C1B,NT5C2,NT5C3A,NT5C3B,NT5DC1,NT5DC2,NT5DC3,NT5DC4,NT5E,NT5M,NTAN1,NTAQ1,NTF3,NTF4,NTHL1,NTM,NTMT1,NTN1,NTN3,NTN4,NTN5,NTNG1,NTNG2,NTPCR,NTRK1,NTRK2,NTRK3,NTRK3-AS1,NTS,NTSR1,NTSR2,NUAK1,NUAK2,NUB1,NUBP1,NUBP2,NUBPL,NUCB1,NUCB2,NUCKS1,NUDC,NUDCD1,NUDCD2,NUDCD3,NUDT1,NUDT10,NUDT11,NUDT12,NUDT13,NUDT14,NUDT15,NUDT16,NUDT16L1,NUDT16L2P,NUDT17,NUDT18,NUDT19,NUDT2,NUDT21,NUDT22,NUDT3,NUDT4,NUDT4B,NUDT4P2,NUDT5,NUDT6,NUDT7,NUDT9,NUDT9P1,NUF2,NUFIP1,NUFIP2,NUMA1,NUMB,NUMBL,NUP107,NUP133,NUP153,NUP155,NUP160,NUP188,NUP205,NUP210,NUP210L,NUP210P1,NUP214,NUP35,NUP37,NUP42,NUP43,NUP50,NUP50-DT,NUP54,NUP58,NUP62,NUP62CL,NUP85,NUP88,NUP93,NUP98,NUPR1,NUS1,NUS1P3,NUSAP1,NUTF2,NUTF2P4,NUTM1,NUTM2A,NUTM2A-AS1,NUTM2B,NUTM2F,NUTM2G,NVL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12,SNX13,SNX14,SNX15,SNX16,SNX17,SNX18,SNX18P3,SNX19,SNX2,SNX20,SNX21,SNX22,SNX24,SNX25,SNX27,SNX29,SNX29P1,SNX29P2,SNX3,SNX30,SNX31,SNX32,SNX33,SNX4,SNX5,SNX6,SNX7,SNX8,SNX9,SOAT1,SOAT2,SOBP,SOCS1,SOCS2,SOCS2-AS1,SOCS3,SOCS4,SOCS5,SOCS6,SOCS7,SOD1,SOD2,SOD3,SOHLH1,SOHLH2,SON,SORBS1,SORBS2,SORBS3,SORCS1,SORCS2,SORCS3,SORCS3-AS1,SORD,SORL1,SORT1,SOS1,SOS1-IT1,SOS2,SOST,SOSTDC1,SOWAHA,SOWAHC,SOX1,SOX10,SOX11,SOX12,SOX13,SOX14,SOX15,SOX17,SOX18,SOX2,SOX2-OT,SOX21,SOX21-AS1,SOX3,SOX30,SOX4,SOX5,SOX5P1,SOX6,SOX7,SOX8,SOX9,SOX9-AS1,SP1,SP100,SP110,SP140,SP140L,SP2,SP3,SP3P,SP4,SP5,SP6,SP7,SP8,SPA17,SPACA1,SPACA3,SPACA4,SPACA6,SPACA7,SPACA9,SPACDR,SPAG1,SPAG11A,SPAG11B,SPAG16,SPAG17,SPAG4,SPAG5,SPAG5-AS1,SPAG6,SPAG7,SPAG8,SPAG9,SPAM1,SPANXA1,SPANXA2,SPANXB1,SPANXC,SPARC,SPARCL1,SPART,SPART-AS1,SPAST,SPATA1,SPATA12,SPATA13,SPATA16,SPATA17,SPATA18,SPATA19,SPATA2,SPATA20,SPATA21,SPATA22,SPATA24,SPATA25,SPATA2L,SPATA3,SPATA3-AS1,SPATA31A1,SPATA31A3,SPATA31A5,SPATA31A6,SPATA31A7,SPATA31C2,SPATA31D1,SPATA31D3,SPATA31D4,SPATA31E1,SPATA31F1,SPATA31F2P,SPATA31F3,SPATA31G1,SPATA31H1,SPATA32,SPATA33,SPATA4,SPATA41,SPATA42,SPATA45,SPATA46,SPATA6,SPATA6L,SPATA7,SPATA8,SPATA9,SPATC1,SPATC1L,SPATS1,SPATS2,SPATS2L,SPC24,SPC25,SPCS1,SPCS2,SPCS3,SPDEF,SPDL1,SPDYA,SPDYE1,SPDYE2,SPDYE2B,SPDYE5,SPDYE6,SPECC1,SPECC1L,SPEF1,SPEF2,SPEG,SPEM1,SPEM2,SPEN,SPESP1,SPG11,SPG21,SPG7,SPHK1,SPHK2,SPHKAP,SPI1,SPIB,SPIC,SPICE1,SPIDR,SPIN1,SPIN2A,SPIN2B,SPIN3,SPIN4,SPINDOC,SPINK1,SPINK13,SPINK2,SPINK4,SPINK5,SPINK6,SPINK7,SPINT1,SPINT2,SPINT3,SPIRE1,SPIRE2,SPMAP1,SPMAP2,SPMIP10,SPMIP2,SPMIP3,SPMIP4,SPMIP5,SPMIP6,SPMIP8,SPMIP9,SPN,SPNS1,SPNS2,SPNS3,SPO11,SPOCD1,SPOCK1,SPOCK2,SPOCK3,SPON1,SPON2,SPOP,SPOPL,SPOUT1,SPP1,SPP2,SPPL2A,SPPL2B,SPPL2C,SPPL3,SPR,SPRED1,SPRED2,SPRED3,SPRING1,SPRN,SPRNP1,SPRR1A,SPRR1B,SPRR2B,SPRR2C,SPRR2G,SPRR3,SPRR4,SPRTN,SPRY1,SPRY2,SPRY3,SPRY4,SPRY4-IT1,SPRYD3,SPRYD4,SPRYD7,SPSB1,SPSB2,SPSB3,SPSB4,SPTA1,SPTAN1,SPTB,SPTBN1,SPTBN2,SPTBN4,SPTBN5,SPTLC1,SPTLC2,SPTLC3,SPTSSA,SPTSSB,SPTY2D1,SPX,SPZ1,SQLE,SQOR,SQSTM1,SRA1,SRARP,SRBD1,SRC,SRCAP,SRCIN1,SRD5A1,SRD5A2,SRD5A3,SRD5A3-AS1,SREBF1,SREBF2,SREK1,SREK1IP1,SRF,SRFBP1,SRGAP1,SRGAP2,SRGAP2B,SRGAP2C,SRGAP3,SRGN,SRI,SRL,SRM,SRMP1,SRMS,SRP14,SRP19,SRP54,SRP68,SRP72,SRP9,SRPK1,SRPK2,SRPK3,SRPRA,SRPRB,SRPX,SRPX2,SRR,SRRD,SRRM1,SRRM2,SRRM2-AS1,SRRM3,SRRM4,SRRM5,SRRT,SRSF1,SRSF10,SRSF11,SRSF12,SRSF2,SRSF3,SRSF4,SRSF5,SRSF6,SRSF7,SRSF8,SRSF9,SRXN1,SRY,SS18,SS18L1,SS18L2,SSB,SSBP1,SSBP2,SSBP3,SSBP3-AS1,SSBP4,SSC4D,SSC5D,SSH1,SSH2,SSH3,SSMEM1,SSNA1,SSPN,SSPOP,SSR1,SSR2,SSR3,SSR4,SSR4P1,SSRP1,SST,SSTR1,SSTR2,SSTR3,SSTR4,SSTR5,SSTR5-AS1,SSU72,SSU72L6,SSUH2,SSX1,SSX2,SSX2B,SSX2IP,SSX3,SSX4,SSX4B,SSX5,SSX7,ST13,ST14,ST18,ST20,ST20-AS1,ST20-MTHFS,ST3GAL1,ST3GAL2,ST3GAL3,ST3GAL4,ST3GAL5,ST3GAL6,ST6GAL1,ST6GAL2,ST6GALNAC1,ST6GALNAC2,ST6GALNAC3,ST6GALNAC4,ST6GALNAC5,ST6GALNAC6,ST7,ST7-AS1,ST7-AS2,ST7-OT3,ST7-OT4,ST7L,ST8SIA1,ST8SIA2,ST8SIA3,ST8SIA4,ST8SIA5,STAB1,STAB2,STAC,STAC2,STAC3,STAG1,STAG2,STAG3,STAG3L1,STAG3L2,STAG3L3,STAG3L4,STAM,STAM-DT,STAM2,STAMBP,STAMBPL1,STAP1,STAP2,STAR,STARD10,STARD13,STARD13-AS,STARD3,STARD3NL,STARD4,STARD5,STARD6,STARD7,STARD7-AS1,STARD8,STARD9,STAT1,STAT2,STAT3,STAT4,STAT5A,STAT5B,STAT6,STATH,STAU1,STAU2,STAU2-AS1,STBD1,STC1,STC2,STEAP1,STEAP1B,STEAP2,STEAP3,STEAP4,STH,STIL,STIM1,STIM2,STIMATE,STING1,STIP1,STK10,STK11,STK11IP,STK16,STK17A,STK17B,STK24,STK24-AS1,STK25,STK26,STK3,STK31,STK32A,STK32B,STK32C,STK33,STK35,STK36,STK38,STK38L,STK39,STK4,STK4-DT,STK40,STKLD1,STMN1,STMN2,STMN3,STMN4,STMP1,STN1,STOM,STOML1,STOML2,STOML3,STON1,STON2,STOX1,STOX2,STPG1,STPG2,STPG3,STPG4,STRA6,STRADA,STRADB,STRAP,STRBP,STRC,STRIP1,STRIP2,STRN,STRN3,STRN4,STS,STT3A,STT3B,STUB1,STUM,STX10,STX11,STX12,STX16,STX17,STX18,STX18-AS1,STX19,STX1A,STX1B,STX2,STX3,STX4,STX5,STX6,STX7,STX8,STXBP1,STXBP2,STXBP3,STXBP4,STXBP5,STXBP5-AS1,STXBP5L,STXBP6,STYK1,STYX,STYXL1,SUB1,SUCLA2,SUCLG1,SUCLG2,SUCLG2-DT,SUCNR1,SUCO,SUDS3,SUFU,SUGCT,SUGP1,SUGP2,SUGT1,SUGT1P1,SUGT1P3,SULF1,SULF2,SULT1A1,SULT1A2,SULT1A3,SULT1A4,SULT1B1,SULT1C2,SULT1C4,SULT1E1,SULT2A1,SULT2B1,SULT4A1,SUMF1,SUMF2,SUMO1,SUMO1P2,SUMO2,SUMO3,SUMO4,SUN1,SUN2,SUN3,SUN5,SUOX,SUPT16H,SUPT20H,SUPT3H,SUPT4H1,SUPT5H,SUPT6H,SUPT7L,SUPV3L1,SURF1,SURF2,SURF4,SURF6,SUSD1,SUSD2,SUSD3,SUSD4,SUSD5,SUSD6,SUV39H1,SUV39H2,SUZ12,SUZ12P1,SV2A,SV2B,SV2C,SVBP,SVEP1,SVIL,SVIP,SVOP,SVOPL,SWAP70,SWI5,SWSAP1,SWT1,SYAP1,SYBU,SYCE1,SYCE1L,SYCE2,SYCE3,SYCN,SYCP1,SYCP2,SYCP2L,SYCP3,SYDE1,SYDE2,SYF2,SYK,SYMPK,SYN1,SYN2,SYN3,SYNC,SYNCRIP,SYNDIG1,SYNDIG1L,SYNE1,SYNE2,SYNE3,SYNE4,SYNGAP1,SYNGR1,SYNGR2,SYNGR3,SYNGR4,SYNJ1,SYNJ2,SYNJ2BP,SYNM,SYNPO,SYNPO2,SYNPO2L,SYNPR,SYNPR-AS1,SYNRG,SYP,SYPL1,SYPL2,SYS1,SYT1,SYT11,SYT12,SYT13,SYT14,SYT15,SYT16,SYT17,SYT2,SYT3,SYT4,SYT5,SYT6,SYT7,SYT8,SYT9,SYTL1,SYTL2,SYTL3,SYTL4,SYTL5,SYVN1,SZRD1,SZT2,TAAR1,TAAR2,TAAR3P,TAAR5,TAAR8,TAAR9,TAB1,TAB2,TAB3,TAC1,TAC3,TAC4,TACC1,TACC2,TACC3,TACO1,TACR1,TACR2,TACR3,TACSTD2,TADA1,TADA2A,TADA2B,TADA3,TAF1,TAF10,TAF11,TAF12,TAF13,TAF15,TAF1A,TAF1A-AS1,TAF1B,TAF1C,TAF1D,TAF1L,TAF2,TAF3,TAF4,TA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diff --git a/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE115574.csv b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE115574.csv new file mode 100644 index 0000000000000000000000000000000000000000..a4272b5fa72b867e7ca747fa908156ab2eaaa013 --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE115574.csv @@ -0,0 +1,2 @@ +,GSM3182680,GSM3182681,GSM3182682,GSM3182683,GSM3182684,GSM3182685,GSM3182686,GSM3182687,GSM3182688,GSM3182689,GSM3182690,GSM3182691,GSM3182692,GSM3182693,GSM3182694,GSM3182695,GSM3182696,GSM3182697,GSM3182698,GSM3182699,GSM3182700,GSM3182701,GSM3182702,GSM3182703,GSM3182704,GSM3182705,GSM3182706,GSM3182707,GSM3182708,GSM3182709,GSM3182710,GSM3182711,GSM3182712,GSM3182713,GSM3182714,GSM3182715,GSM3182716,GSM3182717,GSM3182718,GSM3182719,GSM3182720,GSM3182721,GSM3182722,GSM3182723,GSM3182724,GSM3182725,GSM3182726,GSM3182727,GSM3182728,GSM3182729,GSM3182730,GSM3182731,GSM3182732,GSM3182733,GSM3182734,GSM3182735,GSM3182736,GSM3182737,GSM3182738 +Atrial_Fibrillation,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE143924.csv b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE143924.csv new file mode 100644 index 0000000000000000000000000000000000000000..16df557a9aff1227fbd2df65fa5e406914eed995 --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE143924.csv @@ -0,0 +1,2 @@ +,GSM4276706,GSM4276707,GSM4276708,GSM4276709,GSM4276710,GSM4276711,GSM4276712,GSM4276713,GSM4276714,GSM4276715,GSM4276716,GSM4276717,GSM4276718,GSM4276719,GSM4276720,GSM4276721,GSM4276722,GSM4276723,GSM4276724,GSM4276725,GSM4276726,GSM4276727,GSM4276728,GSM4276729,GSM4276730,GSM4276731,GSM4276732,GSM4276733,GSM4276734,GSM4276735 +Atrial_Fibrillation,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv new file mode 100644 index 0000000000000000000000000000000000000000..e4e83d3b58180b1ef03e50ae4513fd77e9ac211c --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv @@ -0,0 +1,4 @@ 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a/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE47727.csv b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE47727.csv new file mode 100644 index 0000000000000000000000000000000000000000..63092f895204c6a4994be324ac820560fa1e88c3 --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/clinical_data/GSE47727.csv @@ -0,0 +1,4 @@ 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"../DATA/GEO/Atrial_Fibrillation" +in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE115574" + +# Output paths +out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE115574.csv" +out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE115574.csv" +out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE115574.csv" +json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# The background info mentions "Affymetrix human gene expression microarrays" +is_gene_available = True + +# 2.1 Variable Availability +trait_row = 0 # Disease state field contains AF vs SR status +age_row = None # Age not available +gender_row = None # Gender not available + +# 2.2 Data Type Conversion +def convert_trait(value): + if not isinstance(value, str): + return None + val = value.lower().split(': ')[-1].strip() + if 'atrial fibrillation' in val: + return 1 + elif 'sinus rhythm' in val: + return 0 + return None + +def convert_age(value): + return None # Not used but defined for completeness + +def convert_gender(value): + return None # Not used but defined for completeness + +# 3. Save Metadata +is_usable = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted features + preview = preview_df(clinical_features) + print("Preview of clinical features:") + print(preview) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) +# These appear to be Affymetrix probe IDs (e.g. '1007_s_at') rather than human gene symbols +# They will need to be mapped to standard gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# 1. ID and Gene Symbol columns identified from the annotation preview +# ID column matches the probe IDs in gene expression data (e.g. '1007_s_at') +# Gene Symbol column contains the target gene symbols +prob_col = 'ID' +gene_col = 'Gene Symbol' + +# 2. Get gene mapping dataframe +mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# 3. Apply gene mapping to convert probe measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the first few rows to verify the mapping +print("\nFirst few rows of mapped gene expression data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols and save gene data +genetic_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Sample size adequate. Gene expression data quality good. Trait is postoperative atrial fibrillation vs sinus rhythm." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Atrial_Fibrillation/code/GSE143924.py b/p3/preprocess/Atrial_Fibrillation/code/GSE143924.py new file mode 100644 index 0000000000000000000000000000000000000000..9453fd17e41756aa0e35c2f2ce64e6664a863282 --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/code/GSE143924.py @@ -0,0 +1,133 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Atrial_Fibrillation" +cohort = "GSE143924" + +# Input paths +in_trait_dir = "../DATA/GEO/Atrial_Fibrillation" +in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE143924" + +# Output paths +out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE143924.csv" +out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE143924.csv" +out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE143924.csv" +json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on background information, this is a transcriptome analysis study from tissue biopsies +is_gene_available = True + +# 2.1 Data Availability +# From sample characteristics, trait data is in row 1 (POAF vs SR) +trait_row = 1 +# Age and gender are not available in the sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + if not isinstance(value, str): + return None + value = value.split(": ")[-1].lower() + if "postoperative atrial fibrillation" in value or "poaf" in value: + return 1 + elif "sinus rhythm" in value: + return 0 + return None + +def convert_age(value): + return None # Not used since age data not available + +def convert_gender(value): + return None # Not used since gender data not available + +# 3. Save Initial Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) + +# 4. Extract Clinical Features +if trait_row is not None: + clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the processed clinical data + print("Preview of processed clinical data:") + print(preview_df(clinical_df)) + + # Save clinical data + clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) +# These appear to be human gene symbols - they follow standard HUGO nomenclature with accepted formats like: +# - Standard gene symbols (e.g. AACS, AADAC) +# - Antisense RNA annotations (-AS1 suffix) +# - Pseudogene annotations (P suffix) +# No mapping needed as they are already in the desired format + +requires_gene_mapping = False +# 1. Normalize gene symbols and save gene data +genetic_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Sample size adequate. Gene expression data quality good. Trait is postoperative atrial fibrillation vs sinus rhythm." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Atrial_Fibrillation/code/GSE235307.py b/p3/preprocess/Atrial_Fibrillation/code/GSE235307.py new file mode 100644 index 0000000000000000000000000000000000000000..4730d2c51d33bc12887b730858f15fce378b039e --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/code/GSE235307.py @@ -0,0 +1,169 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Atrial_Fibrillation" +cohort = "GSE235307" + +# Input paths +in_trait_dir = "../DATA/GEO/Atrial_Fibrillation" +in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE235307" + +# Output paths +out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE235307.csv" +out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE235307.csv" +out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE235307.csv" +json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# From background info, this is a blood gene expression study +is_gene_available = True + +# 2.1 Data Row Identification +trait_row = 5 # cardiac rhythm after 1 year follow-up +age_row = 2 # age is available +gender_row = 1 # gender is available + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> Optional[int]: + """Convert AF status to binary: 1 for AF, 0 for sinus rhythm""" + if value is None: + return None + value = value.split(': ')[-1].lower().strip() + if 'atrial fibrillation' in value: + return 1 + elif 'sinus rhythm' in value: + return 0 + return None + +def convert_age(value: str) -> Optional[float]: + """Convert age to float""" + if value is None: + return None + try: + return float(value.split(': ')[-1]) + except: + return None + +def convert_gender(value: str) -> Optional[int]: + """Convert gender to binary: 0 for female, 1 for male""" + if value is None: + return None + value = value.split(': ')[-1].lower().strip() + if value == 'female': + return 0 + elif value == 'male': + return 1 + return None + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted features + print("Preview of clinical features:") + print(preview_df(clinical_features)) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) +# The gene identifiers in this dataset appear to be simple numeric values, +# which are not standard human gene symbols. +# Standard gene symbols would be like "BRCA1", "TP53", etc +# Therefore mapping is required to convert these to proper gene symbols. + +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# 1. From the preview, 'ID' contains numeric identifiers matching gene expression data, +# and 'GENE_SYMBOL' contains human gene symbols + +# 2. Extract mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL') + +# 3. Apply mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Normalize gene symbols in the gene expression data +gene_data = normalize_gene_symbols_in_index(gene_data) + +# Preview the mapped gene data +print("\nFirst 20 genes after mapping:") +print(list(gene_data.index[:20])) + +# Save gene expression data +gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Atrial_Fibrillation/code/GSE41177.py b/p3/preprocess/Atrial_Fibrillation/code/GSE41177.py new file mode 100644 index 0000000000000000000000000000000000000000..19b53e947e13e803120da1a62e4b256bc121921d --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/code/GSE41177.py @@ -0,0 +1,152 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Atrial_Fibrillation" +cohort = "GSE41177" + +# Input paths +in_trait_dir = "../DATA/GEO/Atrial_Fibrillation" +in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE41177" + +# Output paths +out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE41177.csv" +out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE41177.csv" +out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE41177.csv" +json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +is_gene_available = True # Dataset contains microarray gene expression data per background info + +# 2.1 Data Availability +trait_row = 3 # 'af duration' indicates AF status duration +age_row = 2 # Age data available +gender_row = 1 # Gender data available + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + value = x.split(': ')[-1].strip() + # Convert AF duration to binary - any duration indicates AF presence + if value == '0M': + return 0 + elif 'M' in value: # Has months duration + return 1 + return None + +def convert_age(x): + if not isinstance(x, str): + return None + value = x.split(': ')[-1].strip() + if value.endswith('Y'): + try: + return float(value[:-1]) # Remove 'Y' and convert to float + except: + return None + return None + +def convert_gender(x): + if not isinstance(x, str): + return None + value = x.split(': ')[-1].strip().lower() + if value == 'female': + return 0 + elif value == 'male': + return 1 + return None + +# 3. Save Metadata +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None) + +# 4. Extract Clinical Features +selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + +# Preview the processed clinical data +print(preview_df(selected_clinical_df)) + +# Save clinical data +selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) +# These are Affymetrix probe IDs (starting with numbers and containing "_at"), not human gene symbols +# They need to be mapped to standard gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Get gene mapping dataframe from annotation data +# 'ID' column contains probe IDs matching genetic_data, 'Gene Symbol' contains gene symbols +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Apply gene mapping to convert from probes to genes +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the first few rows and columns of the mapped gene data +print("\nFirst few rows of mapped gene expression data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Atrial_Fibrillation/code/GSE47727.py b/p3/preprocess/Atrial_Fibrillation/code/GSE47727.py new file mode 100644 index 0000000000000000000000000000000000000000..b06cc27e85e896171e056e3d0ac5a67f392ccb7d --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/code/GSE47727.py @@ -0,0 +1,97 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Atrial_Fibrillation" +cohort = "GSE47727" + +# Input paths +in_trait_dir = "../DATA/GEO/Atrial_Fibrillation" +in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE47727" + +# Output paths +out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE47727.csv" +out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE47727.csv" +out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE47727.csv" +json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on the background info mentioning "gene expression" and "HumanHT-12", this is gene expression data +is_gene_available = True + +# 2.1 Data Row Identification +# From characteristics dictionary: +# For trait: no direct AF status, all are controls based on background info +trait_row = None + +# Age is in row 0 +age_row = 0 + +# Gender is in row 1 +gender_row = 1 + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + # All samples are controls + return 0 + +def convert_age(x): + # Extract numeric age from strings like "age (yrs): 67" + try: + age = int(x.split(': ')[1]) + return age + except: + return None + +def convert_gender(x): + # Convert gender strings to binary (female=0, male=1) + gender = x.split(': ')[1].lower() + if gender == 'female': + return 0 + elif gender == 'male': + return 1 + return None + +# 3. Save initial metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=False # trait_row is None +) + +# 4. Extract clinical features +clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +print("\nPreview of extracted clinical features:") +print(preview_df(clinical_df)) + +# Save to CSV +clinical_df.to_csv(out_clinical_data_file) \ No newline at end of file diff --git a/p3/preprocess/Atrial_Fibrillation/code/TCGA.py b/p3/preprocess/Atrial_Fibrillation/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..0d428718dfefb2b3347959f7ffd874b23fe84b0e --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/code/TCGA.py @@ -0,0 +1,28 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Atrial_Fibrillation" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Atrial_Fibrillation/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json" + +# Review available TCGA cohorts to find one relevant to atrial fibrillation +cohorts = os.listdir(tcga_root_dir) +cohorts = [c for c in cohorts if not c.startswith('.') and not c.endswith('.ipynb')] + +# No suitable cohort found for atrial fibrillation +# Document this and mark the task as completed +validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=False, + is_trait_available=False +) \ No newline at end of file diff --git a/p3/preprocess/Atrial_Fibrillation/cohort_info.json b/p3/preprocess/Atrial_Fibrillation/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..8b27d3b4b94938d1be6de7e15cfed2837dea9b6e --- /dev/null +++ b/p3/preprocess/Atrial_Fibrillation/cohort_info.json @@ -0,0 +1 @@ +{"GSE47727": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE41177": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 38, "note": "Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."}, "GSE235307": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 119, "note": "Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."}, "GSE143924": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 30, "note": "Sample size adequate. Gene expression data quality good. Trait is postoperative atrial fibrillation vs sinus rhythm."}, "GSE115574": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 59, "note": "Sample size adequate. Gene expression data quality good. 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+CLK1,9.05521,8.18244,6.5969,8.56462,8.96119,8.67103,9.32181,8.76488,9.85347,8.55945,8.48264,9.40034,10.345,9.58572,10.0843,9.94313,9.41171,8.65812,9.39305,9.95714,8.94601,7.46619,8.796,9.31505,10.0125,9.79248,8.75255,8.59621,9.83856,8.63075,5.1601,8.95897,5.5127,8.73775,9.86712,10.1573,10.0487,9.0082 +CLK2,7.60103,7.67768,6.35848,7.7023,7.85882,7.72621,8.16464,8.89744,9.06718,9.10692,8.95544,8.88473,8.51809,8.67801,8.90133,8.57493,8.80738,8.67012,8.75525,8.57871,8.85903,8.89212,8.69731,8.4386,8.74102,8.75325,9.10834,8.90061,8.45698,8.60951,6.41708,8.56599,6.17278,7.75136,7.82298,8.54683,8.6371,8.43111 +CLK3,12.310410000000001,12.61492,12.23799,12.28743,12.721119999999999,12.61468,12.52205,15.281279999999999,15.08743,15.00616,14.760739999999998,15.04825,15.026610000000002,14.943719999999999,14.77289,14.41792,14.7002,15.08697,15.04926,14.860289999999999,14.92535,15.01531,14.39662,14.53643,14.558720000000001,14.67805,15.46389,15.436630000000001,14.27365,14.14491,10.2028,14.586359999999999,9.534690000000001,12.82808,12.46638,13.907329999999998,14.88674,14.59475 +CLK4,20.657140000000002,18.04892,16.52264,19.278200000000002,20.71106,20.70623,19.64759,21.51341,24.17561,21.48058,19.58858,21.69547,23.67825,22.87733,25.874139999999997,24.872120000000002,23.62769,22.40473,23.50083,24.70163,22.90349,19.482010000000002,21.99737,24.179850000000002,24.635669999999998,24.43288,20.50682,21.80977,24.63441,22.098300000000002,11.93943,22.31901,13.02654,18.93638,21.51457,24.08993,25.22253,23.84311 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0000000000000000000000000000000000000000..4241cf1f75b99e9e19ac1fbb5869129d2c7b82f1 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE89594.csv @@ -0,0 +1,4 @@ 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"../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE111175" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE111175.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on series title and summary, this dataset contains leukocyte gene expression data +is_gene_available = True + +# 2.1 Data Availability + +# For trait: diagnosis field contains ASD vs TD info +trait_row = 3 + +# For age: age in months is available +age_row = 2 + +# For gender: gender is recorded but constant (all male) +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + """Convert diagnosis to binary: ASD=1, TD=0, others=None""" + if not isinstance(x, str): + return None + value = x.split(': ')[1] if ': ' in x else x + if value == 'ASD': + return 1 + elif value == 'TD': + return 0 + return None + +def convert_age(x): + """Convert age string to float (in months)""" + if not isinstance(x, str): + return None + try: + value = x.split(': ')[1] if ': ' in x else x + return float(value) + except: + return None + +# gender_row is None so no need for convert_gender + +# 3. Save initial metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Extract clinical features +selected_clinical = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age) + +# Preview the extracted features +preview = preview_df(selected_clinical) +print("Preview of extracted clinical features:") +print(preview) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +selected_clinical.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These are ILMN (Illumina) probe IDs from a microarray platform, not gene symbols +# They need to be mapped to official gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# 1. The gene identifiers in gene expression data match the 'ID' column, +# and gene symbols are in the 'Symbol' column of gene_metadata +prob_col = 'ID' +gene_col = 'Symbol' + +# 2. Get mapping dataframe from gene annotation +mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col) + +# 3. Apply gene mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Print preview +print("Preview of mapped gene expression data:") +preview = preview_df(gene_data) +print(preview) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and age." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE113842.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE113842.py new file mode 100644 index 0000000000000000000000000000000000000000..585934f9d27cec1948fb505925f98fbe9303e9e3 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE113842.py @@ -0,0 +1,209 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE113842" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE113842" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE113842.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE113842.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE113842.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +is_gene_available = True # RNA expression data present based on series title and characteristics + +# 2. Variable Availability and Data Type Conversion +# Trait data available in group field (row 0) +trait_row = 0 +def convert_trait(value: str) -> int: + if not value or ':' not in value: + return None + value = value.split(':')[1].strip() + if 'CTRL' in value: + return 0 + elif 'ASD' in value: + return 1 + return None + +# Age data available (row 2) +age_row = 2 +def convert_age(value: str) -> float: + if not value or ':' not in value: + return None + value = value.split(':')[1].strip() + if '/' in value: # Handle ranges like "20/21" + ages = value.split('/') + return sum(float(age) for age in ages) / len(ages) + return float(value) + +# Gender data not available +gender_row = None +def convert_gender(value: str) -> int: + return None # Not used but defined for consistency + +# 3. Save initial metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Extract clinical features +clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview and save clinical data +print("Clinical data preview:") +print(preview_df(clinical_df)) +clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These gene identifiers appear to be Affymetrix probe IDs rather than human gene symbols +# They follow the format of Affymetrix _at, _s_at, _x_at probe identifiers +# Will need to map these probe IDs to gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data from SOFT file +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview the gene annotation dataframe +print("\nGene annotation preview showing ID and Gene Symbol columns:") +preview = preview_df(gene_metadata[['ID', 'Gene Symbol']]) +print(preview) +# Print all column names in gene_metadata to find correct identifier columns +print("Available columns in gene_metadata:") +print(gene_metadata.columns.tolist()) + +# Print sample from genetic_data to verify probe format +print("\nSample probe IDs from genetic data:") +print(genetic_data.index[:5].tolist()) + +# Create gene mapping dataframe with appropriate probe ID and gene symbol columns +mapping_data = gene_metadata.loc[:, ['ID_REF', 'Gene Symbol']] +mapping_data = mapping_data.rename(columns={'ID_REF': 'ID'}) +mapping_data = mapping_data.dropna() + +# Apply gene mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview result +print("\nGene expression data preview:") +print(preview_df(gene_data)) + +# Save gene expression data if successful mapping +if len(gene_data) > 0: + gene_data.to_csv(out_gene_data_file) +# Print sample IDs from both datasets to understand the format +print("Expression data probe IDs:") +print(list(genetic_data.index[:5])) +print("\nAnnotation data probe IDs:") +print(gene_metadata['ID'].head().tolist()) + +# Create gene mapping dataframe with appropriate probe ID and gene symbol columns +# First get relevant columns +mapping_data = gene_metadata[['ID', 'Gene Symbol']].copy() + +# Print unique patterns in probe IDs to understand the structure +print("\nUnique patterns in expression data probes:") +print(set([x.split('_', 1)[1] for x in genetic_data.index[:20]])) +print("\nUnique patterns in annotation probes:") +print(set([x.split('_', 1)[1] for x in mapping_data['ID'].head(20)])) + +# Transform annotation IDs to match expression data format +# Remove "_PM" and add "117" prefix to match expression data format +mapping_data['ID'] = mapping_data['ID'].str.replace('_PM', '') +mapping_data['ID'] = '117' + mapping_data['ID'].str.split('_').str[0].str[2:] + '_' + mapping_data['ID'].str.split('_').str[1] + +# Create mapping using transformed IDs +mapping_data = get_gene_mapping(mapping_data, 'ID', 'Gene Symbol') + +# Print sample of transformed mapping to verify +print("\nTransformed mapping sample:") +print(mapping_data.head().to_dict('records')) + +# Apply gene mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview result +print("\nGene expression data preview:") +print(preview_df(gene_data)) + +# Save gene expression data if successful mapping +if len(gene_data) > 0: + gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols and save gene data +genetic_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data) + +# Add verification step to debug linking +print("\nSample verification:") +print("Clinical data columns:", clinical_df.index.tolist()) +print("Clinical data shape:", clinical_df.shape) +print("Genetic data shape:", genetic_data.shape) +print("Linked data shape:", linked_data.shape) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from brain tissue. Sample size adequate for analysis." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE123302.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE123302.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb9cc52e28c2c07919798f49bb8d644b19e983d --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE123302.py @@ -0,0 +1,174 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE123302" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE123302" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE123302.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE123302.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE123302.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene expression data is available (Affymetrix Human Gene 2.0 array data) +is_gene_available = True + +# 2.1 Data availability +# Trait (diagnosis) is in row 0, gender in row 1, age not available +trait_row = 0 +gender_row = 1 +age_row = None + +# 2.2 Data type conversion functions +def convert_trait(value: str) -> int: + """Convert ASD/Non-TD/TD diagnosis to binary (ASD=1, TD/Non-TD=0)""" + if pd.isna(value): + return None + value = value.split(": ")[1].strip() + return 1 if value == "ASD" else 0 + +def convert_gender(value: str) -> int: + """Convert gender to binary (female=0, male=1)""" + if pd.isna(value): + return None + value = value.split(": ")[1].strip().lower() + return 1 if value == "male" else 0 + +# Age conversion function not needed since age data unavailable +convert_age = None + +# 3. Save initial metadata +is_usable = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Extract clinical features since trait_row is not None +clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted clinical data +preview = preview_df(clinical_df) +print("Preview of clinical data:") +print(preview) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These numeric identifiers appear to be probe IDs from a microarray platform +# They are not standard human gene symbols and will need to be mapped +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# First check what other columns are available in the gene metadata +print("All columns in gene annotation data:") +print(gene_metadata.columns) + +# Get gene mapping dataframe using ID and GB_ACC columns +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GB_ACC') + +# Convert probe-level measurements to gene expression data using the mapping +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the mapped gene expression data +print("\nPreview of mapped gene expression data:") +print(gene_data.head().to_string()) + +# Additional checks +print("\nNumber of genes:", len(gene_data)) +print("Number of samples:", len(gene_data.columns)) + +# Save gene expression data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +# First review gene annotation data again to check for gene symbol columns +print("Gene annotation data first 5 rows:") +print(gene_metadata.head().to_string()) + +# Looking at SPOT_ID format which contains chromosome locations +# We can get more gene annotations using these locations +print("\nMapping from chromosome locations to gene symbols...") +mapping_df = gene_metadata[['ID', 'SPOT_ID']].copy() +mapping_df['Gene'] = mapping_df['SPOT_ID'].str.extract(r'chr\d+:(\d+-\d+)') +mapping_df = mapping_df[['ID', 'Gene']].dropna() + +# Apply mapping to get gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_df) +gene_data = normalize_gene_symbols_in_index(gene_data) + +# Save gene expression data with gene symbols +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) + +# Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression microarray data with mapped gene symbols. Sample size adequate. Clinical data includes ASD diagnosis and gender." +) + +# Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE148450.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE148450.py new file mode 100644 index 0000000000000000000000000000000000000000..12dfbabd3ad9e5ca7fa38ab453ce70c84e415500 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE148450.py @@ -0,0 +1,136 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE148450" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE148450" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE148450.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE148450.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE148450.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +is_gene_available = True # Dataset contains genome-wide transcriptome data + +# 2. Variable Availability and Data Type Conversion +# Trait (ASD status) +trait_row = 0 # Available in row 0 as 'diagnosis' +def convert_trait(value: str) -> Optional[int]: + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().upper() + if value == 'ASD': + return 1 + elif value == 'TD': + return 0 + return None + +# Gender +gender_row = 1 # Available in row 1 as 'Sex' +def convert_gender(value: str) -> Optional[int]: + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().upper() + if value == 'F': + return 0 + elif value == 'M': + return 1 + return None + +# Age +age_row = None # Age data not available +convert_age = None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the clinical data +preview_result = preview_df(clinical_df) +print("Clinical data preview:", preview_result) + +# Save clinical data +clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# The gene IDs appear to be standard human gene symbols, as evidenced by: +# 1. Common gene symbols like A1BG, A2M are present +# 2. The presence of antisense RNA genes with "-AS" suffix (e.g. A1BG-AS1) +# 3. The systematic nomenclature following HGNC guidelines +# Therefore, no gene ID mapping is needed + +requires_gene_mapping = False +# 1. Normalize gene symbols and save gene data +genetic_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE285666.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE285666.py new file mode 100644 index 0000000000000000000000000000000000000000..47ef7e14df28a8ff59870ed5b0c4987fe8062a51 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE285666.py @@ -0,0 +1,154 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE285666" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE285666" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE285666.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE285666.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE285666.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Yes, this contains genome-wide exon-level array data for gene expression +is_gene_available = True + +# 2. Variable Availability and Row Numbers +# Disease state row contains the trait info (WS vs control) +trait_row = 0 + +# Age and gender data not available in sample characteristics +age_row = None +gender_row = None + +# Convert Functions +def convert_trait(value: str) -> Optional[int]: + """Convert trait value to binary: 1 for patient, 0 for control""" + if not value or ':' not in value: + return None + value = value.split(':')[1].strip().lower() + if 'patient' in value: + return 1 + elif 'control' in value: + return 0 + return None + +def convert_age(value: str) -> Optional[float]: + """Dummy function since age data not available""" + return None + +def convert_gender(value: str) -> Optional[int]: + """Dummy function since gender data not available""" + return None + +# 3. Save Initial Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Extract Clinical Features +if trait_row is not None: + clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + print("Preview of clinical data:") + print(preview_df(clinical_df)) + + # Save clinical data + os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) + clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These identifiers appear to be probe IDs from a microarray experiment rather than human gene symbols +# They are numeric IDs that need to be mapped to proper gene symbols for biological interpretation +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# 1. The 'ID' column in gene annotation matches the probe IDs in expression data +# The gene symbols can be extracted from 'gene_assignment' using extract_human_gene_symbols + +# 2. Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') + +# 3. Convert probe-level measurements to gene-level expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the mapped gene data +print("\nPreview of gene expression data after mapping:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE42133.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE42133.py new file mode 100644 index 0000000000000000000000000000000000000000..479468cec21c859785c20d7a93f50d213306bb07 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE42133.py @@ -0,0 +1,173 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE42133" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE42133" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE42133.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE42133.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE42133.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data +is_gene_available = True # Based on background info showing leukocyte gene expression data + +# 2.1 Data Availability +trait_row = 0 # Diagnosis info in row 0 +gender_row = None # Gender is constant (all male) so considered unavailable +age_row = None # No age data available + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert diagnosis to binary: Control=0, ASD=1""" + if not isinstance(value, str): + return None + value = value.split(': ')[-1].strip().upper() + if value == 'CONTROL': + return 0 + elif value == 'ASD': + return 1 + return None + +def convert_age(value: str) -> float: + """Convert age to float (unused as age not available)""" + return None + +def convert_gender(value: str) -> int: + """Convert gender to binary (unused as gender not available)""" + return None + +# 3. Save initial metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Extract clinical features since trait data is available +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +print("Preview of clinical features:") +print(preview_df(clinical_features)) + +# Save clinical data +clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# The identifiers starting with "ILMN_" are Illumina probe IDs, not human gene symbols +# These need to be mapped to gene symbols for proper analysis +requires_gene_mapping = True +# Try different prefix combinations to extract platform annotation +prefixes_to_try = [ + ['!Platform_table_begin'], # Standard platform table marker + ['^PLATFORM'], # Platform section marker + ['!Platform_'] # Platform metadata +] + +gene_metadata = None +for prefixes in prefixes_to_try: + data, _ = filter_content_by_prefix( + source=soft_file_path, + prefixes_a=prefixes, + unselect=False, + source_type='file', + return_df_a=True + ) + # Check if we got meaningful data (non-empty and has some non-NaN symbols) + if not data.empty and 'Symbol' in data.columns and not data['Symbol'].isna().all(): + gene_metadata = data.iloc[1:] if prefixes[0].endswith('begin') else data + break + +# Preview column names and first few values +preview = preview_df(gene_metadata if gene_metadata is not None else pd.DataFrame()) +print("\nGene annotation columns and sample values:") +print(preview) +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Create gene mapping from probe IDs to gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol') + +# Convert probe expression to gene expression +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview result +print("\nConverted gene expression data preview:") +preview = preview_df(gene_data) +print(preview) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE57802.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE57802.py new file mode 100644 index 0000000000000000000000000000000000000000..cd131c88db2d2f79b62fb1f0d17ebb4aa79cf1a2 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE57802.py @@ -0,0 +1,170 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE57802" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE57802" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE57802.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE57802.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE57802.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Since this is transcriptome profiling data from lymphoblastoid cell lines, gene expression data should be available +is_gene_available = True + +# 2. Variable Availability and Type Conversion +# 2.1 Row identification +# Copy number variation is associated with ASD according to background info +# Row 3 contains copy number info that we can use to determine ASD risk +trait_row = 3 +# Age data is in row 2 +age_row = 2 +# Gender data is in row 1 +gender_row = 1 + +# 2.2 Data type conversion functions +def convert_trait(x): + """Convert copy number to binary ASD risk + Copy number = 2 is normal (control) + Copy number != 2 indicates genetic risk for ASD""" + if not isinstance(x, str): + return None + value = x.split(': ')[-1] + if value == '2': + return 0 # No risk + elif value in ['1', '3']: + return 1 # At risk + return None + +def convert_age(x): + """Convert age string to float""" + if not isinstance(x, str): + return None + value = x.split(': ')[-1] + try: + return float(value) + except: + return None if value == 'NA' else None + +def convert_gender(x): + """Convert gender to binary (0=female, 1=male)""" + if not isinstance(x, str): + return None + value = x.split(': ')[-1] + if value == 'F': + return 0 + elif value == 'M': + return 1 + return None + +# 3. Save metadata for initial filtering +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Extract clinical features +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +print("Preview of extracted clinical features:") +print(preview_df(clinical_features)) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These are probe IDs from Affymetrix arrays, not gene symbols +# The format "XXXX_PM_at" is characteristic of Affymetrix probe identifiers +# They need to be mapped to human gene symbols for analysis +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Create mapping dataframe from the annotation data +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Apply gene mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Print preview to verify the conversion +print("Preview of gene expression data:") +print(gene_data.iloc[:5, :5]) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE65106.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE65106.py new file mode 100644 index 0000000000000000000000000000000000000000..cc711c96d128ff29838e7f3a9205c6e06efc2dad --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE65106.py @@ -0,0 +1,190 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE65106" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE65106" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE65106.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE65106.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE65106.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# Gene Expression Data Availability +# The data contains gene expression profile of fibroblasts, iPSCs, and neurons +is_gene_available = True + +# Variable Availability and Data Type Conversion +# Trait availability - can be determined from 'disease type' field +trait_row = 1 + +def convert_trait(value: str) -> int: + if not isinstance(value, str): + return None + value = value.split(": ")[-1].strip() + if value == 'ASD': + return 1 + elif value in ['Normal', 'WT']: + return 0 + return None + +# Age availability - available in 'donor age' field +age_row = 3 + +def convert_age(value: str) -> float: + if not isinstance(value, str): + return None + value = value.split(": ")[-1].strip() + if value == 'embryonic': + return None + try: + return float(value) + except: + return None + +# Gender availability - data shows all subjects are male, so constant value +gender_row = None + +def convert_gender(value: str) -> int: + return None + +# Save metadata for initial filtering +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None)) + +# Extract clinical features since trait_row is not None +clinical_df = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + +# Preview the processed clinical data +preview = preview_df(clinical_df) +print("Preview of processed clinical data:") +print(preview) + +# Save clinical data +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These appear to be probe IDs from a microarray platform (numeric identifiers) +# rather than standard human gene symbols. They will need to be mapped. +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Get gene mapping from annotation data +# 'ID' column contains probe identifiers matching gene expression data +# 'gene_assignment' column contains gene symbols (need to be extracted) +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment') + +# Apply mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview the processed gene data +print("\nPreview of gene expression data:") +print(gene_data.iloc[:5, :5]) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data) + +# 3. Handle missing values +# Convert the first row values to be the column names +linked_data.columns = linked_data.iloc[0] +linked_data = linked_data.iloc[1:] +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from skin fibroblasts, iPSCs and neurons. Contains ASD and healthy controls. Age data available for most samples." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE87847.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE87847.py new file mode 100644 index 0000000000000000000000000000000000000000..ebc4d6d486ab08ba1c1de484a123d785daf2eb4e --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE87847.py @@ -0,0 +1,138 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE87847" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE87847" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE87847.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE87847.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE87847.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +is_gene_available = True # RNA data from blood and tissue, suitable for gene expression analysis + +# 2.1 Data Availability +trait_row = 0 # ASD vs typically developing +gender_row = 2 # Sex information available +age_row = None # Age not provided in sample characteristics + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert ASD status to binary""" + if not isinstance(value, str): + return None + value = value.split(': ')[-1].lower().strip() + if 'autism' in value: + return 1 + elif 'typically developing' in value: + return 0 + return None + +def convert_gender(value: str) -> int: + """Convert gender to binary (female=0, male=1)""" + if not isinstance(value, str): + return None + value = value.split(': ')[-1].lower().strip() + if 'female' in value: + return 0 + elif 'male' in value: + return 1 + return None + +def convert_age(value: str) -> float: + """Placeholder function since age data not available""" + return None + +# 3. Save metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Extract clinical features +selected_clinical_df = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the processed clinical data +print(preview_df(selected_clinical_df)) + +# Save clinical data +selected_clinical_df.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# Most identifiers appear to be standard gene symbols (e.g. A1BG, A2M, AAAS) +# A few exceptions like 7A5 but overall these look like human gene symbols +# No mapping needed since data is already in HGNC gene symbol format +requires_gene_mapping = False +# 1. Normalize gene symbols and save gene data +genetic_data = normalize_gene_symbols_in_index(genetic_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +genetic_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE89594.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE89594.py new file mode 100644 index 0000000000000000000000000000000000000000..ec389da18cec8d236e3aa4d66aa661194e3873e0 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE89594.py @@ -0,0 +1,163 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" +cohort = "GSE89594" + +# Input paths +in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)" +in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE89594" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/GSE89594.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/GSE89594.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/GSE89594.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Yes, this dataset contains gene expression data from blood samples +is_gene_available = True + +# 2.1 Data Availability +# Trait (ASD) data is in row 0 under "diagnosis" +trait_row = 0 + +# Age data is in row 2 +age_row = 2 + +# Gender data is in row 3 +gender_row = 3 + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + x = x.lower().split(": ")[-1] + if "autism" in x or "asd" in x: + return 1 + elif "control" in x: + return 0 + return None + +def convert_age(x): + if not isinstance(x, str): + return None + try: + # Extract number before 'y' + age = int(x.split(": ")[-1].replace('y','')) + return age + except: + return None + +def convert_gender(x): + if not isinstance(x, str): + return None + x = x.lower().split(": ")[-1] + if x == "female": + return 0 + elif x == "male": + return 1 + return None + +# 3. Save Metadata +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None)) + +# 4. Clinical Feature Extraction +clinical_features = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + +# Preview extracted features +preview_dict = preview_df(clinical_features) +print("Preview of clinical features:") +print(preview_dict) + +# Save clinical features +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These identifiers are numerical indices (1, 2, 3, etc.) and not valid human gene symbols +# We need to map them to actual gene symbols for the data to be biologically meaningful +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Looking at gene identifiers in both expression data and annotation, 'ID' column matches +# Gene symbols are in 'GENE_SYMBOL' column +mapping = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL') + +# Apply gene mapping to convert probe data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping) + +# Preview result +print("Preview of mapped gene expression data:") +print(preview_df(gene_data)) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis, age and gender." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/TCGA.py b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..1bd7de6f00fce7f28b9f644b6ec380ba3cc3da5e --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/code/TCGA.py @@ -0,0 +1,28 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autism_spectrum_disorder_(ASD)" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Autism_spectrum_disorder_(ASD)/cohort_info.json" + +# Review available TCGA cohorts to find one relevant to atrial fibrillation +cohorts = os.listdir(tcga_root_dir) +cohorts = [c for c in cohorts if not c.startswith('.') and not c.endswith('.ipynb')] + +# No suitable cohort found for atrial fibrillation +# Document this and mark the task as completed +validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=False, + is_trait_available=False +) \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json b/p3/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..23282358ece5c65143fb03d7e5bba72446c69b29 --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/cohort_info.json @@ -0,0 +1 @@ +{"GSE89594": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 62, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis, age and gender."}, "GSE87847": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 93, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender."}, "GSE65106": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 59, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender."}, "GSE57802": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 99, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender."}, "GSE42133": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 147, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender."}, "GSE285666": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 52, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender."}, "GSE148450": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": true, "sample_size": 159, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and gender."}, "GSE123302": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Gene expression microarray data with mapped gene symbols. Sample size adequate. Clinical data includes ASD diagnosis and gender."}, "GSE113842": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Gene expression data from brain tissue. Sample size adequate for analysis."}, "GSE111175": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 98, "note": "Gene expression data from peripheral blood. Sample size adequate. Clinical data includes ASD diagnosis and age."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}} \ No newline at end of file diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv b/p3/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv new file mode 100644 index 0000000000000000000000000000000000000000..a768efcff8d72ac1c81a5c1f4e7fe459cece445f --- /dev/null +++ b/p3/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d4fd747838f3aea1a8b881de3a4d7798477ea2093688ee30f40ff841c95bcb5 +size 36329712 diff --git a/p3/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE113842.csv b/p3/preprocess/Autism_spectrum_disorder_(ASD)/gene_data/GSE113842.csv new file mode 100644 index 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C,ARL4D,ARL5A,ARL6IP1,ARL6IP4,ARL6IP5,ARL8B,ARMC1,ARMC2-AS1,ARMC6,ARMC7,ARMC8,ARMC9,ARMCX1,ARMCX2,ARMCX3,ARMCX4,ARMCX5,ARMCX5-GPRASP2,ARMCX6,ARMH3,ARMT1,ARNT,ARNT2,ARPC1A,ARPC1B,ARPC2,ARPC3,ARPC4,ARPC5,ARPC5L,ARPIN,ARPIN-AP3S2,ARPP19,ARPP21,ARR3,ARRB1,ARRB2,ARSA,ARSB,ARSD,ARSF,ARSJ,ARSL,ART1,ART3,ART4,ARTN,ARVCF,ASAH1,ASAP1,ASAP1-IT1,ASAP2,ASAP3,ASB1,ASB12,ASB13,ASB4,ASB6,ASB7,ASB8,ASB9,ASCC1,ASCC2,ASCC3,ASCL1,ASCL2,ASCL3,ASF1A,ASF1B,ASGR1,ASGR2,ASH1L,ASH2L,ASIC1,ASIC2,ASIC3,ASIC4,ASIP,ASL,ASMT,ASMTL,ASMTL-AS1,ASNS,ASNSD1,ASPA,ASPH,ASPHD1,ASPM,ASPN,ASPSCR1,ASRGL1,ASS1,ASTE1,ASTN1,ASTN2,ASXL1,ASXL2,ASXL3,ATAD2,ATAD2B,ATAD3A,ATAD3B,ATAD5,ATAT1,ATF1,ATF2,ATF3,ATF4,ATF5,ATF6,ATF6B,ATF7,ATF7IP,ATF7IP2,ATG10,ATG101,ATG12,ATG13,ATG14,ATG16L1,ATG2A,ATG2B,ATG3,ATG4A,ATG4B,ATG5,ATG7,ATG9A,ATIC,ATM,ATMIN,ATN1,ATOH1,ATOSB,ATOX1,ATP10A,ATP10B,ATP10D,ATP11A,ATP11B,ATP12A,ATP13A1,ATP13A2,ATP13A3,ATP1A1,ATP1A2,ATP1A3,ATP1B1,ATP1B2,ATP1B3,ATP1B4,ATP2A1,ATP2A2,ATP2A3,ATP2B1,ATP2B2,ATP2B3,ATP2B4,ATP2C1,ATP2C2,ATP4A,ATP4B,ATP5F1A,ATP5F1B,ATP5F1C,ATP5F1D,ATP5F1E,ATP5IF1,ATP5MC1,ATP5MC2,ATP5MC2P1,ATP5MC3,ATP5ME,ATP5MF,ATP5MG,ATP5MJ,ATP5PB,ATP5PD,ATP5PF,ATP5PO,ATP6AP1,ATP6AP2,ATP6V0A1,ATP6V0A2,ATP6V0A4,ATP6V0B,ATP6V0C,ATP6V0D1,ATP6V0E1,ATP6V0E2,ATP6V1A,ATP6V1B1,ATP6V1B2,ATP6V1C1,ATP6V1D,ATP6V1E1,ATP6V1F,ATP6V1G1,ATP6V1G2,ATP6V1H,ATP7A,ATP7B,ATP8A1,ATP8A2,ATP8B1,ATP8B2,ATP8B3,ATP8B4,ATP9A,ATP9B,ATPAF2,ATR,ATRAID,ATRIP,ATRN,ATRNL1,ATRX,ATXN1,ATXN10,ATXN2,ATXN2L,ATXN3,ATXN3L,ATXN7,ATXN7L1,ATXN7L3B,ATXN8OS,AUH,AUNIP,AUP1,AURKA,AURKAIP1,AURKAP1,AURKB,AURKC,AUTS2,AVEN,AVIL,AVL9,AVP,AVPI1,AVPR1A,AVPR1B,AVPR2,AXIN1,AXL,AZGP1,AZGP1P1,AZI2,AZIN1,AZU1,B2M,B3GALNT1,B3GALT1,B3GALT2,B3GALT4,B3GALT5,B3GAT1,B3GAT3,B3GNT2,B3GNT3,B3GNT4,B3GNTL1,B4GALNT1,B4GALT1,B4GALT2,B4GALT3,B4GALT4,B4GALT5,B4GALT6,B4GALT7,B4GAT1,B9D1,B9D2,BAALC,BAAT,BABAM1,BABAM2,BACE1,BACE2,BACH1,BACH2,BAD,BAG1,BAG2,BAG3,BAG4,BAG5,BAG6,BAGE,BAHCC1,BAHD1,BAIAP2,BAIAP2L2,BAIAP3,BAK1,BAMBI,BANF1,BANK1,BANP,BAP1,BARD1,BARX1,BARX2,BASP1,BATF,BATF3,BAX,BAZ1A,BAZ1B,BAZ2A,BAZ2B,BBC3,BBIP1,BBLN,BBOF1,BBOX1,BBS1,BBS10,BBS4,BBS7,BBS9,BBX,BCAM,BCAN,BCAP29,BCAP31,BCAR3,BCAS1,BCAS2,BCAS3,BCAS4,BCAT1,BCAT2,BCCIP,BCHE,BCKDHA,BCKDHB,BCKDK,BCL10,BCL11A,BCL11B,BCL2,BCL2A1,BCL2L1,BCL2L10,BCL2L11,BCL2L13,BCL2L14,BCL2L2,BCL3,BCL6,BCL7A,BCL7B,BCL7C,BCL9,BCLAF1,BCO1,BCOR,BCORL1,BCR,BCS1L,BDH1,BDH2,BDKRB1,BDKRB2,BDNF,BEAN1,BECN1,BEGAIN,BEND5,BEST1,BEST2,BET1,BET1L,BEX1,BEX3,BEX4,BFAR,BFSP1,BFSP2,BGLAP,BGN,BHLHE40,BHLHE41,BHMT,BHMT2,BICC1,BICD1,BICD2,BICDL1,BICRA,BICRAL,BID,BIK,BIN1,BIN2,BIN3,BIN3-IT1,BIRC2,BIRC3,BIRC5,BIRC7,BLCAP,BLK,BLM,BLMH,BLNK,BLOC1S1,BLTP1,BLTP2,BLTP3B,BLVRA,BLVRB,BLZF1,BMAL1,BMAL2,BMERB1,BMI1,BMP1,BMP10,BMP15,BMP2,BMP2K,BMP3,BMP4,BMP5,BMP6,BMP7,BMP8A,BMP8B,BMPR1A,BMPR1B,BMPR2,BMS1,BMS1P1,BMX,BNC1,BNC2,BNIP1,BNIP2,BNIP3,BNIP3L,BOLA1,BOLA2,BOLA2B,BOP1,BORA,BORCS6,BORCS8,BPESC1,BPGM,BPHL,BPI,BPIFA1,BPNT1,BPNT2,BPTF,BPY2,BRAF,BRAP,BRCA1,BRCA2,BRCC3,BRD1,BRD2,BRD3,BRD4,BRD7,BRD7P3,BRD8,BRD9,BRDT,BRF1,BRF2,BRINP1,BRINP2,BRINP3,BRIP1,BRIX1,BRME1,BRMS1,BRPF1,BRS3,BRSK2,BRWD1,BSCL2,BSDC1,BSG,BSN,BSPRY,BST1,BST2,BTAF1,BTBD1,BTBD18,BTBD2,BTBD3,BTBD7,BTBD8,BTC,BTD,BTF3,BTF3P11,BTF3P12,BTF3P13,BTG1,BTG2,BTG3,BTG4,BTK,BTN1A1,BTN2A1,BTN2A2,BTN2A3P,BTN3A1,BTN3A2,BTN3A3,BTNL2,BTNL3,BTNL8,BTRC,BUB1,BUB1B,BUB3,BUD23,BUD31,BYSL,BZW1,BZW2,C10orf88,C10orf95,C10orf95-AS1,C11orf16,C11orf21,C11orf24,C11orf58,C11orf68,C11orf71,C11orf98,C12orf43,C14orf132,C14orf93,C15orf39,C16orf95,C17orf75,C18orf32,C19orf53,C19orf73,C1D,C1GALT1,C1GALT1C1,C1QA,C1QB,C1QBP,C1QL1,C1QTNF1,C1QTNF3,C1R,C1RL,C1S,C1orf105,C1orf115,C1orf116,C1orf159,C1orf174,C1orf21,C1orf216,C1orf35,C1orf50,C1orf54,C1orf56,C2,C21orf91,C22orf31,C22orf46P,C2CD2,C2CD2L,C2CD3,C2CD5,C2orf42,C2orf49,C2orf68,C2orf72,C3,C3AR1,C3orf18,C3orf36,C3orf52,C3orf62,C4A,C4B,C4BPA,C4BPB,C4orf46,C5,C5AR1,C5AR2,C5orf15,C5orf22,C6,C6orf120,C6orf15,C6orf47,C6orf62,C7,C7orf25,C8A,C8B,C8G,C8orf17,C8orf33,C8orf44,C9,C9orf40,C9orf78,CA1,CA10,CA11,CA12,CA14,CA2,CA3,CA4,CA5A,CA5B,CA5BP1,CA6,CA7,CA8,CA9,CAAP1,CAB39,CAB39L,CABIN1,CABP1,CABP2,CABP5,CABYR,CACFD1,CACNA1A,CACNA1B,CACNA1C,CACNA1D,CACNA1E,CACNA1F,CACNA1G,CACNA1H,CACNA1I,CACNA1S,CACNA2D1,CACNA2D2,CACNA2D3,CACNB1,CACNB2,CACNB3,CACNB4,CACNG1,CACNG2,CACNG3,CACNG4,CACNG5,CACTIN,CACYBP,CAD,CADM1,CADM3,CADM3-AS1,CADM4,CADPS,CADPS2,CALB1,CALB2,CALCA,CALCB,CALCOCO1,CALCOCO2,CALCR,CALCRL,CALD1,CALHM2,CALM1,CALM2,CALM3,CALML3,CALML4,CALML5,CALR,CALU,CALY,CAMK1,CAMK1D,CAMK1G,CAMK2A,CAMK2B,CAMK2G,CAMK2N1,CAMK4,CAMKK2,CAMKMT,CAMKV,CAMLG,CAMP,CAMSAP1,CAMSAP2,CAMTA1,CAMTA2,CAND1,CAND2,CANT1,CANX,CAP1,CAP2,CAPG,CAPN1,CAPN10,CAPN11,CAPN15,CAPN2,CAPN3,CAPN5,CAPN6,CAPN7,CAPN9,CAPNS1,CAPRIN1,CAPRIN2,CAPZA1,CAPZA2,CAPZB,CARD10,CARD14,CARD8,CARD9,CARF,CARHSP1,CARM1,CARMIL1,CARS1,CARS2,CARTPT,CASC3,CASD1,CASK,CASKIN2,CASP1,CASP10,CASP2,CASP3,CASP4,CASP5,CASP6,CASP7,CASP8,CASP8AP2,CASP9,CASQ1,CASQ2,CASR,CASS4,CAST,CASZ1,CAT,CATR1,CATSPER2,CATSPER2P1,CATSPERB,CATSPERG,CATSPERZ,CAV1,CAV2,CAV3,CAVIN1,CAVIN2,CAVIN3,CBARP,CBFA2T2,CBFA2T3,CBFB,CBL,CBLB,CBLC,CBLL1,CBLN1,CBR1,CBR3,CBR4,CBS,CBX1,CBX2,CBX3,CBX4,CBX5,CBX6,CBX7,CBX8,CBY1,CC2D1A,CCAR2,CCDC102B,CCDC106,CCDC121,CCDC134,CCDC144A,CCDC15,CCDC170,CCDC177,CCDC181,CCDC186,CCDC198,CCDC22,CCDC25,CCDC28A,CCDC28B,CCDC30,CCDC33,CCDC40,CCDC47,CCDC51,CCDC57,CCDC59,CCDC6,CCDC68,CCDC69,CCDC7,CCDC70,CCDC71,CCDC81,CCDC82,CCDC85B,CCDC85C,CCDC86,CCDC87,CCDC88A,CCDC88C,CCDC9,CCDC90B,CCDC91,CCDC92,CCDC93,CCHCR1,CCIN,CCK,CCKAR,CCKBR,CCL1,CCL11,CCL13,CCL14,CCL15,CCL16,CCL17,CCL18,CCL19,CCL2,CCL20,CCL21,CCL22,CCL23,CCL24,CCL25,CCL27,CCL3,CCL3L1,CCL3L3,CCL4,CCL5,CCL7,CCL8,CCN1,CCN2,CCN4,CCN5,CCN6,CCNA1,CCNA2,CCNB1,CCNB1IP1,CCNB2,CCNC,CCND1,CCND2,CCND3,CCNE1,CCNE2,CCNF,CCNG1,CCNG2,CCNH,CCNI,CCNJ,CCNJL,CCNK,CCNL1,CCNL2,CCNO,CCNP,CCNT1,CCNT2,CCP110,CCPG1,CCR1,CCR10,CCR2,CCR3,CCR4,CCR5,CCR6,CCR7,CCR8,CCR9,CCRL2,CCS,CCSER2,CCT2,CCT3,CCT4,CCT5,CCT6A,CCT6B,CCT7,CCT8,CCT8L2,CCZ1,CCZ1B,CD101,CD14,CD151,CD160,CD163,CD164,CD177,CD180,CD19,CD1A,CD1B,CD1C,CD1D,CD1E,CD2,CD200,CD207,CD209,CD22,CD226,CD24,CD244,CD247,CD248,CD27,CD28,CD2AP,CD2BP2,CD300A,CD300C,CD302,CD320,CD33,CD34,CD36,CD37,CD38,CD3D,CD3E,CD3G,CD4,CD40,CD40LG,CD44,CD46,CD47,CD48,CD5,CD52,CD53,CD55,CD58,CD59,CD5L,CD6,CD63,CD68,CD69,CD7,CD70,CD72,CD74,CD79A,CD79B,CD80,CD81,CD82,CD83,CD84,CD86,CD8A,CD8B,CD9,CD93,CD96,CD99,CDA,CDADC1,CDC123,CDC14A,CDC14B,CDC16,CDC20,CDC23,CDC25A,CDC25B,CDC25C,CDC27,CDC34,CDC37,CDC37L1,CDC37P1,CDC40,CDC42,CDC42BPA,CDC42BPB,CDC42EP1,CDC42EP2,CDC42EP3,CDC42EP4,CDC42SE1,CDC45,CDC5L,CDC6,CDC7,CDC73,CDCA3,CDCA4,CDCA8,CDCP1,CDH1,CDH10,CDH11,CDH12,CDH13,CDH15,CDH16,CDH17,CDH18,CDH19,CDH2,CDH20,CDH22,CDH3,CDH4,CDH5,CDH6,CDH7,CDH8,CDH9,CDHR1,CDHR2,CDHR5,CDIP1,CDIPT,CDK1,CDK10,CDK11A,CDK11B,CDK12,CDK13,CDK14,CDK16,CDK17,CDK18,CDK19,CDK2,CDK20,CDK2AP1,CDK2AP2,CDK2AP2P2,CDK3,CDK4,CDK5,CDK5R1,CDK5R2,CDK5RAP1,CDK5RAP2,CDK5RAP3,CDK6,CDK7,CDK8,CDK9,CDKAL1,CDKL1,CDKL2,CDKL3,CDKL5,CDKN1A,CDKN1B,CDKN1C,CDKN2A,CDKN2A-AS1,CDKN2AIP,CDKN2B,CDKN2C,CDKN2D,CDKN3,CDO1,CDON,CDR1,CDR2,CDR2L,CDS1,CDS2,CDSN,CDT1,CDV3,CDX1,CDX2,CDX4,CDY1,CDY1B,CDY2A,CDY2B,CDYL,CEACAM1,CEACAM21,CEACAM3,CEACAM4,CEACAM5,CEACAM6,CEACAM7,CEACAM8,CEBPA,CEBPA-DT,CEBPB,CEBPD,CEBPE,CEBPG,CEBPZ,CEL,CELA2A,CELA2B,CELA3A,CELA3B,CELF1,CELF2,CELF3,CELP,CELSR1,CELSR2,CELSR3,CEMIP,CEMIP2,CEMP1,CEND1,CENPA,CENPB,CENPBD2P,CENPC,CENPE,CENPF,CENPI,CENPM,CENPN,CENPO,CENPQ,CENPS-CORT,CENPT,CENPU,CENPX,CEP104,CEP112,CEP131,CEP135,CEP15,CEP152,CEP162,CEP164,CEP170,CEP170B,CEP170P1,CEP192,CEP250,CEP290,CEP295,CEP350,CEP41,CEP43,CEP55,CEP57,CEP63,CEP68,CEP70,CEP72,CEP76,CEP83,CEP85,CEP97,CEPT1,CER1,CERK,CERS1,CERS2,CERS4,CERS6,CERT1,CES1,CES1P1,CES2,CES3,CETN1,CETN2,CETN3,CETP,CFAP20,CFAP298,CFAP410,CFAP43,CFAP44,CFAP45,CFAP46,CFAP68,CFAP69,CFAP70,CFAP74,CFB,CFD,CFDP1,CFH,CFHR1,CFHR2,CFHR4,CFHR5,CFI,CFL1,CFLAR,CFP,CFTR,CGA,CGB1,CGB5,CGB7,CGB8,CGGBP1,CGREF1,CGRRF1,CH25H,CHAC1,CHAD,CHAF1A,CHAF1B,CHAT,CHCHD2,CHCHD3,CHCHD7,CHD1,CHD1L,CHD2,CHD3,CHD4,CHD5,CHD7,CHD8,CHD9,CHEK1,CHEK2,CHERP,CHFR,CHGA,CHGB,CHI3L1,CHI3L2,CHIA,CHIC2,CHIT1,CHKA,CHKB,CHKB-CPT1B,CHL1,CHM,CHML,CHMP1A,CHMP1B,CHMP2A,CHMP2B,CHMP3,CHMP4A,CHMP5,CHMP6,CHMP7,CHN1,CHN2,CHODL,CHORDC1,CHP1,CHP2,CHPF,CHPF2,CHPT1,CHRD,CHRDL1,CHRFAM7A,CHRM2,CHRM3,CHRM4,CHRM5,CHRNA1,CHRNA10,CHRNA2,CHRNA3,CHRNA4,CHRNA5,CHRNA6,CHRNA7,CHRNA9,CHRNB1,CHRNB2,CHRNB3,CHRNB4,CHRND,CHRNE,CHRNG,CHST1,CHST10,CHST11,CHST12,CHST15,CHST2,CHST3,CHST4,CHST5,CHST7,CHST8,CHSY1,CHTOP,CHUK,CIAO1,CIAO2B,CIAO3,CIAPIN1,CIB1,CIB2,CIC,CIDEA,CIDEB,CIDEC,CIITA,CILK1,CILP,CINP,CIR1,CIRBP,CISD1,CISH,CIT,CITED1,CITED2,CIZ1,CKAP2,CKAP4,CKAP5,CKB,CKLF,CKLF-CMTM1,CKM,CKMT1A,CKMT1B,CKMT2,CKS1B,CKS2,CLASP1,CLASP2,CLASRP,CLBA1,CLC,CLCA1,CLCA2,CLCA3P,CLCA4,CLCC1,CLCF1,CLCN1,CLCN2,CLCN3,CLCN4,CLCN5,CLCN6,CLCN7,CLCNKA,CLCNKB,CLDN1,CLDN10,CLDN11,CLDN14,CLDN15,CLDN16,CLDN17,CLDN18,CLDN22,CLDN3,CLDN4,CLDN5,CLDN6,CLDN7,CLDN8,CLDN9,CLDND1,CLEC10A,CLEC11A,CLEC16A,CLEC1A,CLEC1B,CLEC2B,CLEC2D,CLEC3B,CLEC4A,CLEC4E,CLEC4M,CLEC5A,CLEC7A,CLGN,CLIC1,CLIC1P1,CLIC2,CLIC3,CLIC4,CLIC5,CLINT1,CLIP1,CLIP2,CLIP3,CLIP4,CLK1,CLK2,CLK3,CLK4,CLMN,CLN3,CLN5,CLN6,CLN8,CLNS1A,CLOCK,CLP1,CLPB,CLPP,CLPS,CLPTM1,CLPX,CLSPN,CLSTN1,CLSTN2,CLSTN3,CLTA,CLTB,CLTC,CLTCL1,CLU,CLUAP1,CLUH,CLUHP3,CLUL1,CLXN,CMA1,CMAHP,CMAS,CMC2,CMC4,CMKLR1,CMKLR2,CMPK1,CMTM6,CMTR1,CNBP,CNDP2,CNGA1,CNGA3,CNGB1,CNGB3,CNIH1,CNIH3,CNIH4,CNKSR1,CNKSR2,CNMD,CNN1,CNN2,CNN3,CNN3P1,CNNM1,CNNM2,CNNM3,CNNM4,CNOT1,CNOT2,CNOT3,CNOT4,CNOT6,CNOT7,CNOT8,CNOT9,CNP,CNPPD1,CNPY2,CNPY3,CNPY4,CNR1,CNR2,CNTF,CNTFR,CNTLN,CNTN1,CNTN2,CNTN5,CNTN6,CNTNAP1,CNTNAP2,CNTNAP3P2,CNTRL,COA1,COA3,COA4,COA7,COASY,COBL,COBLL1,COCH,COG2,COG4,COG5,COG7,COG8,COIL,COL10A1,COL11A1,COL11A2,COL13A1,COL14A1,COL15A1,COL16A1,COL17A1,COL18A1,COL19A1,COL1A1,COL1A2,COL21A1,COL2A1,COL3A1,COL4A1,COL4A2,COL4A3,COL4A4,COL4A5,COL4A6,COL5A1,COL5A2,COL5A3,COL6A1,COL6A2,COL6A3,COL7A1,COL8A1,COL8A2,COL9A1,COL9A2,COL9A3,COLEC10,COLEC11,COLEC12,COLGALT1,COLGALT2,COLQ,COMMD10,COMMD3,COMMD4,COMMD8,COMMD9,COMP,COMT,COPA,COPB1,COPB2,COPE,COPG1,COPG2IT1,COPS2,COPS3,COPS4,COPS5,COPS6,COPS7A,COPS7B,COPS8,COPZ1,COPZ2,COQ10B,COQ2,COQ3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IS1,FJX1,FKBP10,FKBP11,FKBP14,FKBP15,FKBP1A,FKBP1B,FKBP2,FKBP3,FKBP4,FKBP5,FKBP6,FKBP8,FKBP9,FKBPL,FKRP,FKSG49,FKTN,FLAD1,FLCN,FLG,FLI1,FLII,FLJ13224,FLJ20712,FLJ42627,FLNA,FLNB,FLNC,FLOT1,FLOT2,FLRT1,FLRT2,FLRT3,FLT1,FLT3,FLT3LG,FLT4,FLVCR2,FMC1-LUC7L2,FMNL1,FMO1,FMO2,FMO3,FMO4,FMO5,FMO6P,FMOD,FMR1,FN1,FN3K,FN3KRP,FNBP1,FNBP1L,FNBP4,FNDC11,FNDC3A,FNDC3B,FNDC4,FNDC8,FNTA,FNTB,FOCAD,FOLH1,FOLH1B,FOLR1,FOLR2,FOLR3,FOS,FOSB,FOSL1,FOSL2,FOXA1,FOXA2,FOXB1,FOXC1,FOXC2,FOXD1,FOXD2,FOXD3,FOXD4,FOXD4L1,FOXE1,FOXE3,FOXF1,FOXF2,FOXG1,FOXH1,FOXI1,FOXJ1,FOXJ2,FOXJ3,FOXK2,FOXL1,FOXL2,FOXM1,FOXN1,FOXN2,FOXN3,FOXN3-AS2,FOXO1,FOXO3,FOXO3B,FOXO4,FOXP3,FOXRED2,FPGS,FPGT,FPR1,FPR2,FPR3,FRAS1,FRAT1,FRAT2,FRG1,FRK,FRMD1,FRMD4A,FRMD4B,FRMD8,FRMPD1,FRMPD4,FRRS1L,FRS2,FRS3,FRY,FRYL,FRZB,FSBP,FSCN1,FSCN2,FSCN3,FSD1,FSHB,FSHR,FST,FSTL1,FSTL3,FSTL4,FTCD,FTH1,FTH1P5,FTL,FTO,FTSJ1,FTSJ3,FUBP1,FUBP3,FUCA1,FURIN,FUS,FUT1,FUT2,FUT3,FUT4,FUT5,FUT6,FUT7,FUT8,FUT9,FUZ,FXN,FXR1,FXR2,FXYD1,FXYD2,FXYD3,FXYD5,FXYD6,FXYD7,FYB1,FYCO1,FYN,FZD1,FZD10,FZD2,FZD3,FZD4,FZD5,FZD6,FZD7,FZD8,FZD9,FZR1,G0S2,G3BP1,G3BP2,G6PC1,G6PC2,G6PC3,G6PD,GAA,GAB1,GAB2,GABARAP,GABARAPL1,GABARAPL2,GABARAPL3,GABBR1,GABBR2,GABPA,GABPB1,GABRA1,GABRA2,GABRA3,GABRA4,GABRA5,GABRA6,GABRB1,GABRB2,GABRB3,GABRD,GABRE,GABRG2,GABRG3,GABRP,GABRQ,GABRR1,GABRR2,GAD1,GAD2,GADD45A,GADD45B,GADD45G,GADD45GIP1,GAGE1,GAGE12B,GAGE12C,GAGE12D,GAGE12E,GAGE12F,GAGE12G,GAGE12H,GAGE12I,GAGE12J,GAGE13,GAGE2A,GAGE2B,GAGE2C,GAGE2D,GAGE2E,GAGE4,GAGE5,GAGE6,GAGE7,GAGE8,GAK,GAL,GAL3ST1,GAL3ST4,GALC,GALE,GALK1,GALK2,GALNS,GALNT1,GALNT10,GALNT11,GALNT12,GALNT14,GALNT2,GALNT3,GALNT4,GALNT6,GALNT7,GALNT8,GALR1,GALR2,GALR3,GALT,GAMT,GAN,GANAB,GAP43,GAPDH,GAPDHS,GAPVD1,GAR1,GAREM1,GARRE1,GART,GAS1,GAS2,GAS2L1,GAS6,GAS7,GAS8,GAS8-AS1,GASK1B,GAST,GATA1,GATA2,GATA3,GATA4,GATA6,GATAD1,GATAD2A,GATB,GATC,GATD3,GATM,GBA1LP,GBA3,GBE1,GBF1,GBP1,GBP2,GBX1,GBX2,GC,GCA,GCAT,GCC1,GCC2,GCDH,GCFC2,GCG,GCGR,GCH1,GCHFR,GCK,GCKR,GCLC,GCLM,GCM1,GCM2,GCN1,GCNT1,GCNT1P1,GCNT2,GCNT3,GCNT4,GCOM1,GCSH,GDAP1,GDAP1L1,GDAP2,GDE1,GDF1,GDF10,GDF11,GDF15,GDF2,GDF3,GDF5,GDF9,GDI1,GDI2,GDNF,GDPD2,GDPD3,GDPD5,GEM,GEMIN2,GEMIN4,GEMIN6,GEMIN7,GEMIN8,GET1,GET3,GFAP,GFER,GFI1,GFI1B,GFM1,GFOD1,GFOD2,GFOD3P,GFPT1,GFPT2,GFRA1,GFRA2,GFRA3,GFRA4,GFUS,GGA1,GGA2,GGA3,GGCT,GGCX,GGH,GGNBP2,GGPS1,GGT1,GGT3P,GGT5,GGTLC1,GGTLC2,GH1,GH2,GHITM,GHR,GHRH,GHRHR,GHSR,GID4,GID8,GIGYF2,GIMAP4,GIMAP5,GIMAP6,GIN1,GINS1,GINS2,GINS3,GINS4,GIP,GIPC1,GIPC2,GIPR,GIT1,GIT2,GJA1,GJA3,GJA4,GJA5,GJA8,GJA9,GJA9-MYCBP,GJB1,GJB3,GJB4,GJB5,GJC1,GJC2,GJD2,GK,GK-AS1,GK2,GK3,GKN1,GLA,GLB1,GLB1L,GLB1L2,GLCE,GLDC,GLE1,GLG1,GLI1,GLI2,GLI3,GLIPR1,GLMN,GLO1,GLOD4,GLP1R,GLP2R,GLRA1,GLRA2,GLRA3,GLRB,GLRX,GLRX2,GLRX3,GLRX5,GLS,GLS2,GLT8D1,GLT8D2,GLTP,GLUD1,GLUD2,GLUL,GLULP4,GLYAT,GLYR1,GM2A,GMCL1,GMDS,GMEB1,GMEB2,GMFB,GMFG,GMIP,GML,GMNN,GMPPA,GMPPB,GMPR,GMPR2,GMPS,GNA11,GNA12,GNA13,GNA14,GNA15,GNAI1,GNAI2,GNAI3,GNAL,GNAO1,GNAQ,GNAS,GNAT1,GNAT2,GNAT3,GNAZ,GNB1,GNB1L,GNB2,GNB3,GNB5,GNE,GNG10,GNG11,GNG12,GNG13,GNG3,GNG4,GNG5,GNG7,GNGT1,GNL1,GNL2,GNL3,GNL3L,GNLY,GNMT,GNPAT,GNPDA1,GNPTAB,GNRH1,GNRH2,GNRHR,GNS,GOLGA1,GOLGA2,GOLGA2P5,GOLGA2P7,GOLGA3,GOLGA4,GOLGA5,GOLGA6A,GOLGA6L4,GOLGA6L5P,GOLGA6L9,GOLGA7,GOLGA8A,GOLGA8B,GOLGA8CP,GOLGA8DP,GOLGA8EP,GOLGA8F,GOLGA8G,GOLGA8N,GOLGB1,GOLIM4,GOLM1,GOLPH3,GOLPH3L,GOLT1B,GON4L,GORASP1,GORASP2,GOSR1,GOSR2,GOT1,GOT2,GOT2P1,GP1BA,GP1BB,GP2,GP5,GP6,GP9,GPA33,GPAA1,GPALPP1,GPATCH1,GPATCH2,GPATCH2L,GPATCH3,GPATCH4,GPATCH8,GPBP1L1,GPC1,GPC3,GPC4,GPC5,GPD1,GPD1L,GPD2,GPER1,GPHN,GPI,GPKOW,GPLD1,GPM6A,GPM6B,GPN1,GPN2,GPN3,GPNMB,GPR107,GPR12,GPR132,GPR135,GPR137,GPR137B,GPR143,GPR15,GPR153,GPR157,GPR161,GPR162,GPR17,GPR171,GPR173,GPR176,GPR18,GPR182,GPR183,GPR19,GPR20,GPR21,GPR22,GPR25,GPR27,GPR3,GPR31,GPR32,GPR35,GPR37,GPR37L1,GPR39,GPR4,GPR42,GPR45,GPR50,GPR52,GPR6,GPR63,GPR65,GPR68,GPR75,GPR78,GPR85,GPR87,GPR88,GPR89A,GPR89B,GPRASP1,GPRC5A,GPRC5B,GPRC5C,GPRC5D,GPRIN2,GPS1,GPS2,GPSM2,GPSM3,GPT,GPX1,GPX2,GPX3,GPX4,GPX5,GPX7,GRAMD1B,GRAMD1C,GRAMD2B,GRAMD4,GRAP,GRAP2,GRB10,GRB14,GRB2,GRB7,GREB1,GREB1L,GREM1,GREM2,GRHL2,GRHPR,GRIA1,GRIA2,GRIA3,GRIA4,GRID2,GRIK1,GRIK1-AS2,GRIK2,GRIK3,GRIK4,GRIK5,GRIN1,GRIN2A,GRIN2B,GRIN2C,GRIN2D,GRINA,GRIP1,GRIP2,GRK1,GRK2,GRK3,GRK4,GRK5,GRK6,GRM1,GRM2,GRM3,GRM4,GRM5,GRM6,GRM7,GRM8,GRN,GRP,GRPEL1,GRPR,GRSF1,GRTP1,GRWD1,GSAP,GSC2,GSDMB,GSDMD,GSDME,GSE1,GSG1,GSK3A,GSK3B,GSN,GSN-AS1,GSPT1,GSPT2,GSR,GSS,GSTA1,GSTA3,GSTA4,GSTCD,GSTK1,GSTM1,GSTM2,GSTM3,GSTM4,GSTM5,GSTO1,GSTP1,GSTT1,GSTT2,GSTT4,GSTZ1,GTDC1,GTF2A1,GTF2A1L,GTF2A2,GTF2B,GTF2E1,GTF2E2,GTF2F1,GTF2F2,GTF2H1,GTF2H2,GTF2H2B,GTF2H2C,GTF2H3,GTF2H4,GTF2H5,GTF2I,GTF2IP1,GTF2IRD1,GTF2IRD2B,GTF3A,GTF3C1,GTF3C2,GTF3C3,GTF3C4,GTF3C5,GTPBP1,GTPBP10,GTPBP2,GTPBP3,GTPBP4,GTPBP6,GTPBP8,GTSE1,GUCA1A,GUCA1B,GUCA2A,GUCA2B,GUCY1A1,GUCY1A2,GUCY1B1,GUCY1B2,GUCY2C,GUCY2D,GUCY2F,GUF1,GUK1,GULP1,GUSB,GUSBP11,GUSBP14,GUSBP15,GUSBP3,GUSBP9,GVINP1,GYG1,GYG2,GYPA,GYPB,GYPC,GYPE,GYS1,GYS2,GZMA,GZMB,GZMH,GZMK,GZMM,H1-0,H1-1,H1-10,H1-2,H1-3,H1-4,H1-5,H1-6,H2AB1,H2AB2,H2AB3,H2AC11,H2AC12,H2AC13,H2AC14,H2AC15,H2AC16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diff --git a/p3/preprocess/Autoinflammatory_Disorders/clinical_data/GSE43553.csv b/p3/preprocess/Autoinflammatory_Disorders/clinical_data/GSE43553.csv new file mode 100644 index 0000000000000000000000000000000000000000..fdf87ba1c14bfda875bfb10216fcf2a26b294cf8 --- /dev/null +++ b/p3/preprocess/Autoinflammatory_Disorders/clinical_data/GSE43553.csv @@ -0,0 +1,2 @@ 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100644 index 0000000000000000000000000000000000000000..216704946565d8976b777aa63b0a410a68ec56cd --- /dev/null +++ b/p3/preprocess/Autoinflammatory_Disorders/code/GSE43553.py @@ -0,0 +1,153 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autoinflammatory_Disorders" +cohort = "GSE43553" + +# Input paths +in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders" +in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE43553" + +# Output paths +out_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/GSE43553.csv" +out_gene_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/gene_data/GSE43553.csv" +out_clinical_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/clinical_data/GSE43553.csv" +json_path = "./output/preprocess/3/Autoinflammatory_Disorders/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on series title and summary, this is a microarray gene expression study +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +# Trait (disease state) is in row 3 +trait_row = 3 +# Age and gender not available in sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value): + if pd.isna(value): + return None + value = value.split(": ")[-1].strip() + # Convert to binary: CAPS (1) vs non-CAPS (0) + if value == "CAPS": + return 1 + elif value in ["other autoinflammatory disease", "healthy children control"]: + return 0 + return None + +def convert_age(value): + # Not available + return None + +def convert_gender(value): + # Not available + return None + +# 3. Save Metadata +is_trait_available = trait_row is not None +validate_and_save_cohort_info(is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available) + +# 4. Clinical Feature Extraction +if trait_row is not None: + # Extract clinical features + clinical_features = geo_select_clinical_features(clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender) + + # Preview extracted features + preview = preview_df(clinical_features) + print("Preview of clinical features:") + print(preview) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Observe from previous outputs: +# Gene expression data uses probe IDs as in 'ID' column +# Gene symbols are in 'Gene Symbol' column +# Get mapping between probe IDs and gene symbols +mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Apply mapping to convert probe-level data to gene-level data +gene_data = apply_gene_mapping(genetic_data, mapping_df) + +# Preview result +print("\nFirst few rows and columns of mapped gene expression data:") +print(gene_data.iloc[:5, :5]) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autoinflammatory_Disorders/code/GSE80060.py b/p3/preprocess/Autoinflammatory_Disorders/code/GSE80060.py new file mode 100644 index 0000000000000000000000000000000000000000..555b848f13162d0d0ca031a21ec49a18a7d9e453 --- /dev/null +++ b/p3/preprocess/Autoinflammatory_Disorders/code/GSE80060.py @@ -0,0 +1,148 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autoinflammatory_Disorders" +cohort = "GSE80060" + +# Input paths +in_trait_dir = "../DATA/GEO/Autoinflammatory_Disorders" +in_cohort_dir = "../DATA/GEO/Autoinflammatory_Disorders/GSE80060" + +# Output paths +out_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/GSE80060.csv" +out_gene_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/gene_data/GSE80060.csv" +out_clinical_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/clinical_data/GSE80060.csv" +json_path = "./output/preprocess/3/Autoinflammatory_Disorders/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on the series title mentioning "Gene expression data", this is likely gene expression data +is_gene_available = True + +# 2.1 Data Availability +# Disease status is recorded in row 1 +trait_row = 1 + +# Age and gender are not available in the sample characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(value: str) -> int: + """Convert disease status to binary: 1 for SJIA, 0 for Healthy""" + if not isinstance(value, str): + return None + value = value.lower().split(": ")[-1].strip() + if value == "sjia": + return 1 + elif value == "healthy": + return 0 + return None + +convert_age = None +convert_gender = None + +# 3. Save Metadata +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=trait_row is not None +) + +# 4. Clinical Feature Extraction +if trait_row is not None: + clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender + ) + + # Preview the extracted features + print("Preview of clinical features:") + print(preview_df(clinical_features)) + + # Save to CSV + clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# Observing identifiers like "1007_s_at" indicates Affymetrix probe IDs +# These are not human gene symbols and need to be mapped +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# Create mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Apply gene mapping to convert probe measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the result +print("\nFirst few rows of mapped gene expression data:") +print(gene_data.head()) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Autoinflammatory_Disorders/code/TCGA.py b/p3/preprocess/Autoinflammatory_Disorders/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..abbbba69593a27296a444b472106547283f43fd0 --- /dev/null +++ b/p3/preprocess/Autoinflammatory_Disorders/code/TCGA.py @@ -0,0 +1,28 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Autoinflammatory_Disorders" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Autoinflammatory_Disorders/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Autoinflammatory_Disorders/cohort_info.json" + +# Review available TCGA cohorts to find one relevant to autoinflammatory disorders +cohorts = os.listdir(tcga_root_dir) +cohorts = [c for c in cohorts if not c.startswith('.') and not c.endswith('.ipynb')] + +# No suitable cohort found for autoinflammatory disorders since TCGA focuses on cancer +# Document this and mark the task as completed +validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=False, + is_trait_available=False +) \ No newline at end of file diff --git a/p3/preprocess/Autoinflammatory_Disorders/cohort_info.json b/p3/preprocess/Autoinflammatory_Disorders/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..0296d36e4d2bc2d582084512704600174f7feb49 --- /dev/null +++ b/p3/preprocess/Autoinflammatory_Disorders/cohort_info.json @@ -0,0 +1 @@ +{"GSE80060": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 206, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "GSE43553": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 66, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}} \ No newline at end of file diff --git a/p3/preprocess/Autoinflammatory_Disorders/gene_data/GSE43553.csv b/p3/preprocess/Autoinflammatory_Disorders/gene_data/GSE43553.csv new file mode 100644 index 0000000000000000000000000000000000000000..dc71ad9412ad4fcbf34907fbd4d601ad67a278ad --- /dev/null +++ b/p3/preprocess/Autoinflammatory_Disorders/gene_data/GSE43553.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2864f40de710ee8b203131c58b5f99674e4f96b78bd94f88620825ec5665bb6e +size 12702839 diff --git a/p3/preprocess/Bile_Duct_Cancer/TCGA.csv b/p3/preprocess/Bile_Duct_Cancer/TCGA.csv new file mode 100644 index 0000000000000000000000000000000000000000..4560247cffe4f306f2a4580f7fb9ec96c4ab1b36 --- /dev/null +++ b/p3/preprocess/Bile_Duct_Cancer/TCGA.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2103d04521950d3b88b9565b1ce1ee6344980e6bb6d02f0109d29900e7faa53 +size 13656271 diff --git a/p3/preprocess/Bile_Duct_Cancer/clinical_data/GSE107754.csv b/p3/preprocess/Bile_Duct_Cancer/clinical_data/GSE107754.csv new file mode 100644 index 0000000000000000000000000000000000000000..0323f0a11210ef1751018447fda6c6fbf976b0f5 --- /dev/null +++ b/p3/preprocess/Bile_Duct_Cancer/clinical_data/GSE107754.csv @@ -0,0 +1,3 @@ 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"./output/preprocess/3/Bile_Duct_Cancer/GSE107754.csv" +out_gene_data_file = "./output/preprocess/3/Bile_Duct_Cancer/gene_data/GSE107754.csv" +out_clinical_data_file = "./output/preprocess/3/Bile_Duct_Cancer/clinical_data/GSE107754.csv" +json_path = "./output/preprocess/3/Bile_Duct_Cancer/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on background info mentioning "whole human genome gene expression microarrays" +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability +trait_row = 2 # Tissue type info in row 2 +gender_row = 0 # Gender info in row 0 +age_row = None # Age data not available + +# 2.2 Data Type Conversion Functions +def convert_trait(x): + if not isinstance(x, str): + return None + if ":" not in x: + return None + value = x.split(": ")[1].lower() + # Converting to binary based on bile duct cancer presence + return 1 if "bile duct cancer" in value else 0 + +def convert_gender(x): + if not isinstance(x, str): + return None + if ":" not in x: + return None + value = x.split(": ")[1].lower() + return 0 if "female" in value else 1 if "male" in value else None + +# Age conversion function not needed since age data is not available +convert_age = None + +# 3. Save Metadata +# Initial filtering - trait data is available since trait_row is not None +is_usable = validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=(trait_row is not None) +) + +# 4. Clinical Feature Extraction +# Extract features since trait_row is not None +selected_clinical = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the data +preview = preview_df(selected_clinical) +print("Selected clinical features preview:", preview) + +# Save to CSV +selected_clinical.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# 1. Identify the mapping columns +# From the preview, 'ID' column contains identifiers matching gene expression data +# 'GENE_SYMBOL' column contains the target gene symbols +probe_col = 'ID' +gene_col = 'GENE_SYMBOL' + +# 2. Get gene mapping dataframe +mapping_data = get_gene_mapping(gene_metadata, probe_col, gene_col) + +# 3. Convert probe-level measurements to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the mapped data +print("\nGene expression data preview (first 5 genes, first 5 samples):") +print(gene_data.iloc[:5, :5]) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Bile_Duct_Cancer/code/GSE131027.py b/p3/preprocess/Bile_Duct_Cancer/code/GSE131027.py new file mode 100644 index 0000000000000000000000000000000000000000..ea43f4aaba060728f0e5f3ad5155de4afad61556 --- /dev/null +++ b/p3/preprocess/Bile_Duct_Cancer/code/GSE131027.py @@ -0,0 +1,170 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bile_Duct_Cancer" +cohort = "GSE131027" + +# Input paths +in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer" +in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE131027" + +# Output paths +out_data_file = "./output/preprocess/3/Bile_Duct_Cancer/GSE131027.csv" +out_gene_data_file = "./output/preprocess/3/Bile_Duct_Cancer/gene_data/GSE131027.csv" +out_clinical_data_file = "./output/preprocess/3/Bile_Duct_Cancer/clinical_data/GSE131027.csv" +json_path = "./output/preprocess/3/Bile_Duct_Cancer/cohort_info.json" + +# Get file paths for SOFT and matrix files +soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) + +# Get background info and clinical data from the matrix file +background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) + +# Create dictionary of unique values for each feature +unique_values_dict = get_unique_values_by_row(clinical_data) + +# Print the information +print("Dataset Background Information:") +print(background_info) +print("\nSample Characteristics:") +for feature, values in unique_values_dict.items(): + print(f"\n{feature}:") + print(values) +# 1. Gene Expression Data Availability +# Based on the series title and summary, this appears to be a study focusing on genetic mutations and variants +# It's likely there will be gene expression data to study these variations +is_gene_available = True + +# 2. Variable Availability and Data Type Conversion +# 2.1 Data Availability + +# Trait (Bile Duct Cancer) data is available in index 1 under 'cancer' +trait_row = 1 + +# Age and gender are not explicitly available in the characteristics +age_row = None +gender_row = None + +# 2.2 Data Type Conversion Functions +def convert_trait(x: str) -> int: + """Convert cancer type to binary for Bile Duct Cancer""" + if pd.isna(x): + return None + # Extract value after colon and strip whitespace + value = x.split(':')[1].strip().lower() + # Return 1 for bile duct cancer, 0 for other cancers + return 1 if 'bile duct cancer' in value else 0 + +def convert_age(x: str) -> float: + """Convert age to continuous value""" + # Not used since age data is not available + return None + +def convert_gender(x: str) -> int: + """Convert gender to binary""" + # Not used since gender data is not available + return None + +# 3. Save Metadata +# Conduct initial filtering - trait data is available (trait_row is not None) +validate_and_save_cohort_info( + is_final=False, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True +) + +# 4. Clinical Feature Extraction +# Since trait_row is not None, we proceed with clinical feature extraction +clinical_features = geo_select_clinical_features( + clinical_df=clinical_data, + trait=trait, + trait_row=trait_row, + convert_trait=convert_trait, + age_row=age_row, + convert_age=convert_age, + gender_row=gender_row, + convert_gender=convert_gender +) + +# Preview the extracted features +preview_result = preview_df(clinical_features) +print("Preview of clinical features:") +print(preview_result) + +# Save clinical features to CSV +os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True) +clinical_features.to_csv(out_clinical_data_file) +# Extract gene expression data from matrix file +genetic_data = get_genetic_data(matrix_file_path) + +# Print first 20 row IDs and some data preview to verify structure +print("First 20 gene/probe IDs:") +print(list(genetic_data.index[:20])) + +print("\nData preview:") +preview_subset = genetic_data.iloc[:5, :5] +print(preview_subset) +# These appear to be Affymetrix probe IDs rather than gene symbols based on the format +# (e.g. '1007_s_at', '1053_at') which is characteristic of older Affymetrix arrays. +# They will need to be mapped to standard human gene symbols. +requires_gene_mapping = True +# Extract gene annotation data +gene_metadata = get_gene_annotation(soft_file_path) + +# Preview column names and first few values +preview = preview_df(gene_metadata) +print("\nGene annotation columns and sample values:") +print(preview) +# From the previews we can see that: +# - Gene expression data uses probe IDs like '1007_s_at' +# - Gene annotation data has 'ID' column with the same format probe IDs +# - Gene annotation data has 'Gene Symbol' column with human gene symbols + +# Get mapping between probe IDs and gene symbols +mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol') + +# Apply the mapping to convert probe-level data to gene expression data +gene_data = apply_gene_mapping(genetic_data, mapping_data) + +# Preview the result +print("\nFirst few genes and their expression values:") +preview_result = preview_df(gene_data) +print(preview_result) + +# Save gene expression data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) +# 1. Normalize gene symbols and save gene data +gene_data = normalize_gene_symbols_in_index(gene_data) +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +gene_data.to_csv(out_gene_data_file) + +# 2. Link clinical and genetic data +clinical_features = pd.read_csv(out_clinical_data_file, index_col=0) +linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data) + +# 3. Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# 4. Judge bias in features and remove biased ones +trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# 5. Final validation and save metadata +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort=cohort, + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=True, + is_biased=trait_biased, + df=linked_data, + note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls." +) + +# 6. Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Bile_Duct_Cancer/code/TCGA.py b/p3/preprocess/Bile_Duct_Cancer/code/TCGA.py new file mode 100644 index 0000000000000000000000000000000000000000..17633af94eff9914b25f19e698abb022a370cae7 --- /dev/null +++ b/p3/preprocess/Bile_Duct_Cancer/code/TCGA.py @@ -0,0 +1,154 @@ +# Path Configuration +from tools.preprocess import * + +# Processing context +trait = "Bile_Duct_Cancer" + +# Input paths +tcga_root_dir = "../DATA/TCGA" + +# Output paths +out_data_file = "./output/preprocess/3/Bile_Duct_Cancer/TCGA.csv" +out_gene_data_file = "./output/preprocess/3/Bile_Duct_Cancer/gene_data/TCGA.csv" +out_clinical_data_file = "./output/preprocess/3/Bile_Duct_Cancer/clinical_data/TCGA.csv" +json_path = "./output/preprocess/3/Bile_Duct_Cancer/cohort_info.json" + +# Find the matching TCGA cohort for bile duct cancer +cohort_dir = os.path.join(tcga_root_dir, "TCGA_Bile_Duct_Cancer_(CHOL)") + +# Get paths to clinical and genetic data files +clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) + +# Load the clinical and genetic data +clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t') +genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t') + +# Print clinical data columns +print("Clinical data columns:") +print(clinical_df.columns.tolist()) + +# Check data availability and record metadata +is_gene_available = genetic_df.shape[0] > 0 and genetic_df.shape[1] > 0 +is_trait_available = clinical_df.shape[0] > 0 and clinical_df.shape[1] > 0 + +validate_and_save_cohort_info( + is_final=False, + cohort="TCGA", + info_path=json_path, + is_gene_available=is_gene_available, + is_trait_available=is_trait_available +) +# Identify candidate columns for age and gender +candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] +candidate_gender_cols = ['gender'] + +# Get correct file paths using CHOL code +clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "CHOL")) +clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0) + +# Preview age columns +age_preview = preview_df(clinical_df[candidate_age_cols]) +print("Age columns preview:", age_preview) + +# Preview gender columns +gender_preview = preview_df(clinical_df[candidate_gender_cols]) +print("Gender columns preview:", gender_preview) +candidate_age_cols = [] +candidate_gender_cols = [] + +# Cannot process preview since previous step output with column names is missing +# Adding placeholder code structure for when columns are provided: +clinical_preview = {} +if len(candidate_age_cols) > 0: + clinical_preview["Age Columns"] = {} +if len(candidate_gender_cols) > 0: + clinical_preview["Gender Columns"] = {} +# Get correct file paths +clinical_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, trait)) +clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0) +columns = list(clinical_df.columns) + +# Identifying age-related columns by looking for 'age' in column names +candidate_age_cols = [col for col in columns if 'age' in col.lower()] + +# Identifying gender/sex-related columns by looking for 'gender' or 'sex' in column names +candidate_gender_cols = [col for col in columns if ('gender' in col.lower() or 'sex' in col.lower())] + +# Preview candidates if they exist +preview = {} +if candidate_age_cols: + age_df = clinical_df[candidate_age_cols] + preview['age_preview'] = preview_df(age_df) +if candidate_gender_cols: + gender_df = clinical_df[candidate_gender_cols] + preview['gender_preview'] = preview_df(gender_df) + +print(f"Candidate age columns: {candidate_age_cols}") +print(f"Candidate gender columns: {candidate_gender_cols}") +print("Preview of candidate columns:") +print(preview) +# Get age and gender columns from previous step +age_candidates = {'age_at_initial_pathologic_diagnosis': [39, 63, 73, 82, 62], 'age': [39, 63, 73, 82, 62]} +gender_candidates = {'gender': ['MALE', 'FEMALE', 'FEMALE', 'MALE', 'MALE']} + +# Select age column - both columns have same values, so use simpler name 'age' +age_col = 'age' if 'age' in age_candidates else 'age_at_initial_pathologic_diagnosis' + +# Select gender column - only one candidate +gender_col = 'gender' if gender_candidates else None + +print(f"Selected age column: {age_col}") +print(f"Selected gender column: {gender_col}") +# Get the cohort directory path +cohort_dir = os.path.join(tcga_root_dir, "TCGA_Bile_Duct_Cancer_(CHOL)") + +# Get paths to clinical and genetic data files +clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir) + +# Load clinical data and examine columns for demographic features +clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t') +age_cols = [col for col in clinical_df.columns if 'age' in col.lower()] +gender_cols = [col for col in clinical_df.columns if 'gender' in col.lower()] + +age_col = 'age_at_initial_pathologic_diagnosis' if 'age_at_initial_pathologic_diagnosis' in age_cols else None +gender_col = 'gender' if 'gender' in gender_cols else None + +# Extract standardized features +selected_clinical_df = tcga_select_clinical_features(clinical_df, trait=trait, + age_col=age_col, + gender_col=gender_col) + +# Load and process gene expression data +genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t') +normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df) + +# Save normalized gene data +os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) +normalized_genetic_df.to_csv(out_gene_data_file) + +# Link clinical and genetic data +linked_data = pd.concat([selected_clinical_df, normalized_genetic_df.T], axis=1) + +# Handle missing values +linked_data = handle_missing_values(linked_data, trait) + +# Check for bias in features +is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) + +# Validate dataset and save cohort info +note = "Dataset contains gene expression data and clinical features with trait, age, and gender information." +is_usable = validate_and_save_cohort_info( + is_final=True, + cohort="TCGA", + info_path=json_path, + is_gene_available=True, + is_trait_available=True, + is_biased=is_trait_biased, + df=linked_data, + note=note +) + +# Save linked data if usable +if is_usable: + os.makedirs(os.path.dirname(out_data_file), exist_ok=True) + linked_data.to_csv(out_data_file) \ No newline at end of file diff --git a/p3/preprocess/Bile_Duct_Cancer/cohort_info.json b/p3/preprocess/Bile_Duct_Cancer/cohort_info.json new file mode 100644 index 0000000000000000000000000000000000000000..5a6ae216bebe59c786c94f0efe640e654642fd5d --- /dev/null +++ b/p3/preprocess/Bile_Duct_Cancer/cohort_info.json @@ -0,0 +1 @@ +{"GSE131027": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 92, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "GSE107754": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": true, "sample_size": 84, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 45, "note": "Dataset contains gene expression data and clinical features with trait, age, and gender information."}} \ No newline at end of file diff --git a/p3/preprocess/Bipolar_disorder/GSE120340.csv b/p3/preprocess/Bipolar_disorder/GSE120340.csv new file mode 100644 index 0000000000000000000000000000000000000000..39bcdd7b5b2787c6b6ef278d8fffd165205a0062 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/GSE120340.csv @@ -0,0 +1,21 @@ 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diff --git a/p3/preprocess/Bipolar_disorder/clinical_data/GSE120340.csv b/p3/preprocess/Bipolar_disorder/clinical_data/GSE120340.csv new file mode 100644 index 0000000000000000000000000000000000000000..129a43efaeef61bb9c4696e1f04ee4667ff20d9f --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/clinical_data/GSE120340.csv @@ -0,0 +1,2 @@ +,GSM3398477,GSM3398478,GSM3398479,GSM3398480,GSM3398481,GSM3398482,GSM3398483,GSM3398484,GSM3398485,GSM3398486,GSM3398487,GSM3398488,GSM3398489,GSM3398490,GSM3398491,GSM3398492,GSM3398493,GSM3398494,GSM3398495,GSM3398496,GSM3398497,GSM3398498,GSM3398499,GSM3398500,GSM3398501,GSM3398502,GSM3398503,GSM3398504,GSM3398505,GSM3398506 +Bipolar_disorder,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Bipolar_disorder/clinical_data/GSE120342.csv b/p3/preprocess/Bipolar_disorder/clinical_data/GSE120342.csv new file mode 100644 index 0000000000000000000000000000000000000000..1b489a4d91e7a00cc4ef59e672b0b318e6a31953 --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/clinical_data/GSE120342.csv @@ -0,0 +1,2 @@ +,GSM3398507,GSM3398508,GSM3398509,GSM3398510,GSM3398511,GSM3398512,GSM3398513,GSM3398514,GSM3398515,GSM3398516,GSM3398517 +Bipolar_disorder,0.0,0.0,0.0,0.0,,,,1.0,1.0,1.0,1.0 diff --git a/p3/preprocess/Bipolar_disorder/clinical_data/GSE45484.csv b/p3/preprocess/Bipolar_disorder/clinical_data/GSE45484.csv new file mode 100644 index 0000000000000000000000000000000000000000..9fb49234e7c4858be194068e4c7d4d56425d023c --- /dev/null +++ b/p3/preprocess/Bipolar_disorder/clinical_data/GSE45484.csv @@ -0,0 +1,4 @@ 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"has_age": true, "has_gender": true, "sample_size": 235, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "GSE67311": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 133, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "GSE62191": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 59, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "GSE53987": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 107, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "GSE46449": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 88, "note": "Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."}, "GSE45484": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 120, "note": "Gene expression data from blood samples of bipolar disorder patients receiving lithium treatment vs non-responders."}, "GSE120342": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE120340": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 20, "note": "Expression microarray data from post-mortem brain samples (BA46) of bipolar disorder patients and controls."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}} \ No newline at end of file