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input/GEO/Underweight/GSE84954/GSE84954_series_matrix.txt.gz

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+size 6786415

input/GEO/Uterine_Carcinosarcoma/GSE32507/GSE32507_series_matrix.txt.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee991c978414606e81ab4f44291f7f53c20c042e59ee2ea8bea9cd88c52f889c
+size 9200143

input/GEO/Vitamin_D_Levels/GSE35925/GSE35925_family.soft.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:38b7133993cd54ff1066efdd30980771e3955085e5d62f71e92214d4133721c7
+size 23011968

p1/preprocess/Adrenocortical_Cancer/code/TCGA.py

@@ -0,0 +1,112 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for the trait "Obesity"
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+trait_keyword = trait
+target_subdir = None
+
+for sd in subdirectories:
+    if trait_keyword.lower() in sd.lower():
+        target_subdir = sd
+        break
+
+if target_subdir is None:
+    # No suitable data found for this trait; mark as completed
+    print("No TCGA subdirectory found for the trait. Skipping.")
+else:
+    # 2. Locate clinical and genetic data files
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load the data
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print column names of clinical data
+    print(clinical_df.columns)
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis"]
+candidate_gender_cols = []
+
+candidate_demo_cols = candidate_age_cols + candidate_gender_cols
+if candidate_demo_cols:
+    extracted_df = clinical_df[candidate_demo_cols]
+    preview_data = preview_df(extracted_df)
+    print(preview_data)
+# Based on the inspection of the provided dictionaries for age and gender:
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = None
+
+print("Chosen age_col:", age_col)
+print("Chosen gender_col:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# (Optional) Save the selected clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 2. Normalize gene symbols in the genetic data
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link the clinical and genetic data on sample IDs
+linked_data = selected_clinical_df.join(normalized_gene_df.T, how="inner")
+
+# 4. Handle missing values
+cleaned_df = handle_missing_values(linked_data, trait)
+
+# 5. Determine if the trait or demographic features are biased
+is_biased, final_df = judge_and_remove_biased_features(cleaned_df, trait)
+
+# 6. Final quality validation
+is_gene_available = not normalized_gene_df.empty
+is_trait_available = trait in final_df.columns
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_biased,
+    df=final_df,
+    note=""
+)
+
+# 7. If the dataset is usable, save the final dataframe
+if is_usable:
+    final_df.to_csv(out_data_file)

p1/preprocess/Alzheimers_Disease/gene_data/GSE214417.csv

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p1/preprocess/Asthma/code/GSE182797.py

@@ -0,0 +1,189 @@
+# 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/1/Asthma/GSE182797.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE182797.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE182797.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on "Transcriptomic profiling" and "microarray analyses"
+
+# 2) Variable Availability and Data Type Conversion
+#    2.1 Identify rows
+trait_row = 0  # "diagnosis: ..." contains multiple distinct values including "adult-onset asthma"
+age_row = 2    # "age: ..." contains multiple numerical values
+gender_row = None  # Only "gender: Female" found, no variability => not available
+
+#    2.2 Define conversion functions
+def convert_trait(value: str):
+    """
+    Convert diagnosis data to a binary label:
+    adult-onset asthma -> 1, otherwise (healthy/IEI) -> 0, unknown -> None
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'adult-onset asthma' in val:
+        return 1
+    elif 'healthy' in val or 'iei' in val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    """Convert age data to a float. Unknown or invalid entries -> None."""
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender data to binary (female->0, male->1).
+    Not used here because gender_row is None, but defined for completeness.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+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
+)
+
+# 4) Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    # 'clinical_data' is assumed to be the DataFrame containing sample characteristics
+    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 and save the selected clinical data
+    preview = preview_df(selected_clinical_df)
+    print("Preview of extracted clinical data:", preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., 'A_19_P00315452') are microarray probe IDs
+# and do not appear to be standard human gene symbols.
+# Therefore, they need to be mapped to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify columns in gene_annotation for probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# 2. Get the mapping of probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Check the shape or a small preview of the mapped gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+# STEP 7: Data Normalization and Linking
+
+# 1) Normalize gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2) Read the previously saved clinical data (which should have shape (2 rows) x (80 columns)) 
+#    so that it aligns correctly with normalized_gene_data.
+temp_clinical = pd.read_csv(out_clinical_data_file)        # Use the first row as header
+temp_clinical.index = [trait, "Age"]
+temp_clinical.columns = normalized_gene_data.columns       # Match with the 80 sample IDs
+
+# Link the clinical and gene data
+linked_data = geo_link_clinical_genetic_data(temp_clinical, normalized_gene_data)
+
+# 3) Handle missing values
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4) Remove biased demographic features; check whether our trait is overly biased
+trait_biased, final_data = judge_and_remove_biased_features(processed_data, trait=trait)
+
+# 5) Conduct final dataset validation
+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=trait_biased,
+    df=final_data,
+    note="Final processed dataset for trait and gene expression."
+)
+
+# 6) If the dataset is usable, save the final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Dataset not usable. No final linked file was saved.")

p1/preprocess/Asthma/code/GSE182798.py

@@ -0,0 +1,189 @@
+# 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/1/Asthma/GSE182798.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE182798.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE182798.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    # Split only once, to ensure we keep the part after the colon.
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Convert to a binary indicator: 1 if adult-onset asthma, else 0
+    # (other categories like IEI or healthy => 0)
+    if 'adult-onset asthma' in val:
+        return 1
+    else:
+        return 0
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val in ['female', 'f']:
+        return 0
+    elif val in ['male', 'm']:
+        return 1
+    return None
+
+# 1. Check gene expression data availability
+is_gene_available = True  # Based on the transcriptomic profiling background
+
+# 2.1 Identify row indices for trait, age, and gender
+trait_row = 0   # "diagnosis: adult-onset asthma", etc. => available
+age_row = 2     # "age: 33.42", "age: 46.08", ... => available
+# Row 1 (gender) has only one unique value => treat it as not available
+gender_row = None
+
+# 3. Metadata: initial filtering
+#   trait_row != None => trait is available
+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
+)
+
+# 4. If trait is available, extract clinical features
+if trait_row is not None:
+    selected_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,   # None
+        convert_gender=convert_gender
+    )
+    preview_result = preview_df(selected_clinical_df)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+    print(preview_result)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These IDs (e.g., 'A_19_P00315452') appear to be array probe identifiers rather than standard gene symbols.
+# Therefore, gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Identify the appropriate columns in the gene annotation
+#    - The probe ID column in the annotation that matches the expression data index is "ID"
+#    - The gene symbol column is "GENE_SYMBOL"
+
+# 2) Get a dataframe mapping probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 3) Convert probe-level expression data into gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print the shape or a small preview of the resulting gene_data
+print("Gene-level expression data shape:", gene_data.shape)
+print("Gene-level expression data (head):")
+print(gene_data.head())
+# STEP 7: Data Normalization and Linking
+
+# 1) Normalize gene symbols in the obtained gene expression data;
+#    remove unrecognized symbols and average duplicates.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2) Read previously saved clinical data. Because we saved it in Step 2 with index=False and each row representing
+#    a feature (trait or age), we need to transpose it so that the samples become rows and features become columns.
+clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+clinical_df = clinical_df.T
+# Rename the columns so they match the variables we want
+clinical_df.columns = [trait, "Age"]
+
+# 3) Link clinical with genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 4) Handle missing values in the linked data:
+#    remove samples with missing trait, remove genes with >20% missing,
+#    remove samples with >5% missing genes, then impute for the rest.
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Check for severe bias in the trait and remove biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6) Conduct final quality validation and save metadata
+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=trait_biased,
+    df=linked_data,
+    note="Processed with trait and gene data successfully."
+)
+
+# 7) If the dataset is usable, save the final linked data to CSV
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable. No final linked file was saved.")

p1/preprocess/Asthma/code/GSE184382.py

@@ -0,0 +1,155 @@
+# 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/1/Asthma/GSE184382.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE184382.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE184382.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset includes transcriptome microarray data.
+
+# 2. Variable Availability
+trait_row = None   # No row indicates "asthma" or similar
+age_row = None     # No row for age
+gender_row = None  # No row for gender
+
+# 2.2 Data Type Conversion
+def convert_trait(value: str) -> int:
+    """
+    Convert raw trait string to a binary indicator (0 or 1).
+    Since trait_row is None, this function won't be used.
+    """
+    # Placeholder implementation
+    return None
+
+def convert_age(value: str) -> float:
+    """
+    Convert raw age string to a float (continuous).
+    Since age_row is None, this function won't be used.
+    """
+    # Placeholder implementation
+    return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert raw gender string to 0 (female) or 1 (male).
+    Since gender_row is None, this function won't be used.
+    """
+    # Placeholder implementation
+    return None
+
+# 3. Save Metadata
+# Determine trait availability
+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
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "A_19_P...", "(+)E1A_r60_...", "3xSLv1") are not standard human gene symbols.
+# They appear to be array or custom IDs that require mapping to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify the columns in the annotation corresponding to the gene expression IDs and the gene symbols
+#    Here, 'ID' holds probe identifiers matching those in 'gene_data'
+#    and 'GENE_SYMBOL' holds the corresponding gene symbols.
+
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 3. Convert probe-level measurements to gene-level data using the mapping.
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# We know from prior steps:
+# - Trait is NOT available (trait_row = None), so no clinical CSV was saved.
+# - We do have gene data, so we will at least normalize it.
+
+# 1) Normalize gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2) Since trait is not available, we cannot link or handle clinical data.
+#    We'll set up placeholders for final validation.
+is_trait_available = False
+trait_biased = False   # Arbitrarily set; the library requires a boolean.
+
+# 3) We have no clinical data to integrate; skip missing value handling.
+
+# 4) With no trait, we cannot check bias meaningfully. Skipped.
+
+# 5) Final dataset validation
+#    The library requires df and is_biased if is_final=True, so we provide an empty DataFrame.
+#    This ensures it records the dataset as not usable.
+empty_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,   # Gene data is available
+    is_trait_available=False, # Trait is not available
+    is_biased=trait_biased,
+    df=empty_df,
+    note="No trait data; final record."
+)
+
+# 6) If the linked data were usable, we would save it. But here, is_usable will be False.
+if is_usable:
+    # This block won't run in our scenario, but included for completeness
+    empty_df.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable (no trait). No final linked file was saved.")

p1/preprocess/Asthma/code/GSE185658.py

@@ -0,0 +1,157 @@
+# 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/1/Asthma/GSE185658.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE185658.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # The background indicates Affymetrix microarrays for global gene expression
+
+# 2) Variable Availability and Data Type Conversion
+# Based on the sample characteristics dictionary, we only see a "group" field (row=1) that includes asthma vs healthy.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Define the conversion function for the trait (binary: 1 for Asthma, 0 for Healthy, None otherwise).
+def convert_trait(value):
+    parts = value.split(':')
+    label = parts[-1].strip().lower()  # Take text after ':'
+    if 'asthma' in label:
+        return 1
+    elif 'healthy' in label:
+        return 0
+    return None
+
+# We do not have age or gender data, so these conversion functions are not used.
+convert_age = None
+convert_gender = None
+
+# 3) Save Metadata (initial filtering)
+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
+)
+
+# 4) Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # previously obtained DataFrame of sample characteristics
+        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_dict = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_dict)
+
+    # Save the extracted clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric format (e.g., '7892501'), these are likely not standard human gene symbols.
+# Therefore, we conclude that gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns in the gene_annotation dataframe correspond to the probe ID and the gene symbol text.
+#    From the preview, "ID" appears to match the probe identifier (same as gene_data index),
+#    and "gene_assignment" appears to contain the gene symbols (though in a messy string).
+
+# 2. Build a mapping dataframe using these two columns.
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col="ID", gene_col="gene_assignment")
+
+# 3. Convert the probe-level measurements to gene expression data using the mapping, 
+#    distributing expression when a probe maps to multiple genes and summing the contributions for each gene.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# 1) Normalize gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2) Link clinical and genetic data
+#    We know from previous steps that we do have trait data in out_clinical_data_file.
+clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+# The clinical CSV contains a single row with the trait values and columns as sample IDs.
+# Label that row with the trait name, so that geo_link_clinical_genetic_data can handle it properly.
+clinical_df.index = [trait]
+
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 3) Handle missing values
+linked_data = handle_missing_values(df=linked_data, trait_col=trait)
+
+# 4) Determine bias
+trait_biased, linked_data = judge_and_remove_biased_features(df=linked_data, trait=trait)
+
+# 5) Final dataset validation
+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=trait_biased,
+    df=linked_data,
+    note="Completed data preprocessing and quality checks."
+)
+
+# 6) If usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable. No final file was saved.")

p1/preprocess/Asthma/code/GSE188424.py

@@ -0,0 +1,134 @@
+# 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/1/Asthma/GSE188424.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE188424.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE188424.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Transcriptome data indicated in the series description
+
+# Step 2.1: Determine availability of trait, age, and gender data
+# From the dictionary {0: ['gender: female', 'gender: male']},
+# only gender data is found under key=0. No separate entries for trait or age are available.
+trait_row = None
+age_row = None
+gender_row = 0
+
+# Step 2.2: Define data conversion functions
+def convert_trait(value: str):
+    # No trait data row is available; return None.
+    return None
+
+def convert_age(value: str):
+    # No age data row is available; return None.
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert the gender string to 0 or 1:
+      - female -> 0
+      - male -> 1
+      - others/unknown -> None
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == 'female':
+        return 0
+    elif gender_str == 'male':
+        return 1
+    return None
+
+# Step 3: Save metadata via initial filtering
+# Trait data availability is determined by whether trait_row is None.
+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
+)
+
+# Step 4: Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., ILMN_1651199), these are Illumina probe IDs 
+# rather than human gene symbols and require mapping.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify the correct columns in gene_annotation corresponding to the Illumina probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene expression data by applying the gene mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# Since 'trait_row' was None, no clinical feature extraction occurred and trait data is unavailable.
+# We must skip linking and final data prep steps and directly do final validation to record that this dataset is unusable for trait-based analysis.
+
+empty_df = pd.DataFrame()  # Placeholder, as df must be provided to the validation function
+is_usable = 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 was found
+    is_biased=True,            # Arbitrary True to pass validation, making the dataset not usable
+    df=empty_df,
+    note="Trait data is unavailable; skipping linking and final data steps."
+)
+
+print("Trait data unavailable. Skipping linking and final data output.")

p1/preprocess/Asthma/code/GSE205151.py

@@ -0,0 +1,96 @@
+# 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/1/Asthma/GSE205151.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE205151.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE205151.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene expression data availability
+#    Based on the metadata: "mRNA was analyzed using a targeted Nanostring immunology array," 
+#    indicating this study involves gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Conversion
+
+#    From the sample characteristics, only two keys (0 and 1) are available:
+#    0 -> polyic_stimulation, and 1 -> cluster
+#    There's no mention of 'Asthma' variation, age, or gender. 
+#    So, all samples are asthmatic, which yields no variability in 'trait', 
+#    and age/gender aren't in the dictionary.
+
+trait_row = None    # No variation in "Asthma" (everyone is asthmatic)
+age_row = None      # Not found
+gender_row = None   # Not found
+
+def convert_trait(value: str) -> int:
+    """
+    Trait data is not available/variable here,
+    so we won't actually use this function.
+    """
+    return None
+
+def convert_age(value: str) -> float:
+    """
+    Age data not available.
+    """
+    return None
+
+def convert_gender(value: str) -> int:
+    """
+    Gender data not available.
+    """
+    return None
+
+# 3. Save Metadata (initial filtering)
+#    Trait data is not available because there's no variability.
+is_trait_available = False
+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
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip extraction for this dataset.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on inspection, these appear to be standard human gene symbols.
+print("requires_gene_mapping = False")

p1/preprocess/Asthma/code/GSE230164.py

@@ -0,0 +1,160 @@
+# 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/1/Asthma/GSE230164.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE230164.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE230164.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is likely available
+is_gene_available = True  # Based on the title "Gene expression profiling of asthma"
+
+# Step 2: Identify the rows for trait, age, and gender
+# From the provided sample characteristics dictionary (only key 0 with gender info),
+# we see no mention of the trait (asthma) or age, so these are not available.
+trait_row = None
+age_row = None
+gender_row = 0  # "gender: female" and "gender: male" are present
+
+# Data type conversion functions
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert trait values to binary (e.g., 'asthma' -> 1, 'control' or 'healthy' -> 0).
+    Returns None if unknown.
+    """
+    # Extract the actual data after the colon if present
+    parts = value.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else value.lower()
+
+    # Example mapping (if we had trait data)
+    if 'asthma' in val:
+        return 1
+    if 'control' in val or 'healthy' in val:
+        return 0
+
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert age values to continuous floats.
+    Returns None if parsing fails or data is unknown.
+    """
+    parts = value.split(':', 1)
+    val = parts[1].strip() if len(parts) > 1 else value
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert gender to binary (female -> 0, male -> 1).
+    Returns None if unknown.
+    """
+    parts = value.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else value.lower()
+    if 'female' in val:
+        return 0
+    if 'male' in val:
+        return 1
+    return None
+
+# Step 3: Initial filtering and saving of metadata
+is_trait_available = trait_row is not None
+
+dataset_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
+)
+
+# Step 4: Since trait_row is None, we skip substep of clinical feature extraction
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the given identifiers (e.g., ILMN_1651199), these appear to be Illumina probe IDs
+# rather than standard human gene symbols. Therefore, gene symbol mapping is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene annotation dataframe
+#    - "ID" column contains Illumina probe IDs matching those in the expression data
+#    - "Symbol" column contains the gene symbols
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# 2. Get a gene mapping dataframe by extracting the two columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# Since 'trait_row' was None, no clinical feature extraction occurred and trait data is unavailable.
+# We must skip linking and final data prep steps and directly do final validation to record that this dataset is unusable for trait-based analysis.
+
+empty_df = pd.DataFrame()  # Placeholder, as df must be provided to the validation function
+is_usable = 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 was found
+    is_biased=True,            # Arbitrary True to pass validation, making the dataset not usable
+    df=empty_df,
+    note="Trait data is unavailable; skipping linking and final data steps."
+)
+
+print("Trait data unavailable. Skipping linking and final data output.")

p1/preprocess/Asthma/code/GSE270312.py

@@ -0,0 +1,162 @@
+# 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/1/Asthma/GSE270312.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE270312.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE270312.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+# STEP 1
+
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Gene Expression Data Availability
+# Based on the background stating "RNA transcriptome responses" were measured, we consider it gene expression data.
+is_gene_available = True
+
+# Step 2: Variable Availability and Conversion
+
+# 2.1 Identify rows for trait, age, and gender
+# From the sample characteristics dictionary, 'asthma status' = row 3, 'gender' = row 2. 
+# No age information is provided.
+trait_row = 3
+age_row = None
+gender_row = 2
+
+# 2.2 Define data conversion functions
+def convert_trait(value: str):
+    # Example: "asthma status: Yes"
+    # Split by colon, then strip extra spaces
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "yes":
+        return 1
+    elif val == "no":
+        return 0
+    return None
+
+def convert_age(value: str):
+    # No age data available, so return None
+    return None
+
+def convert_gender(value: str):
+    # Example: "gender: Female"
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+
+# Step 3: Save Metadata (initial filtering)
+# Trait data is considered available if we have a valid row for it
+is_trait_available = (trait_row is not None)
+
+filter_pass = 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
+)
+
+# Step 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,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the selected clinical features
+    preview_clinical = preview_df(selected_clinical_df)
+    # (You could print the preview or store it if needed; omitted here for brevity.)
+
+    # Save the clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed gene identifiers such as ABCF1, ACE, ACKR2, etc.,
+# these appear to be valid human gene symbols and do not require additional mapping.
+
+print("These genes are human gene symbols.")
+
+# Conclusion
+print("\nrequires_gene_mapping = False")
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2. Link the clinical and genetic data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine whether the trait/demographic features are severely biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+
+# 5. Conduct final quality validation and save metadata
+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=trait_biased,
+    df=linked_data,
+    note="Trait data and gene data successfully linked."
+)
+
+# 6. If the dataset is deemed usable, save the final linked data as a CSV file
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Dataset was not deemed usable; final linked data not saved.")

p1/preprocess/Asthma/code/TCGA.py

@@ -0,0 +1,59 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+
+import os
+
+# Step 1: Identify subdirectory that might relate to "Asthma"
+subdirs = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Since we're looking for "Asthma" and no subdirectory name suggests an asthma-related cancer,
+# no suitable subdirectory is found.
+suitable_subdir = None
+
+# Confirm no matching subdirectory
+for sd in subdirs:
+    # Normally, you'd check synonyms for "Asthma" if needed.
+    if "asthma" in sd.lower():
+        suitable_subdir = sd
+        break
+
+# If not found, skip the trait:
+if not suitable_subdir:
+    print("No suitable subdirectory found for trait 'Asthma'. Skipping this trait.")
+    # Mark as completed but unavailable in metadata
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # (Would proceed to load data if a matching subdirectory was found.)
+    pass

p1/preprocess/Asthma/gene_data/GSE123086.csv

@@ -0,0 +1 @@
+Gene,GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049

p1/preprocess/Asthma/gene_data/GSE123088.csv

@@ -0,0 +1 @@
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p1/preprocess/Asthma/gene_data/GSE182797.csv

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p1/preprocess/Asthma/gene_data/GSE182798.csv

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p1/preprocess/Asthma/gene_data/GSE184382.csv

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p1/preprocess/Asthma/gene_data/GSE185658.csv

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p1/preprocess/Asthma/gene_data/GSE188424.csv

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p1/preprocess/Asthma/gene_data/GSE230164.csv

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p1/preprocess/Asthma/gene_data/GSE270312.csv

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p1/preprocess/Atrial_Fibrillation/clinical_data/GSE115574.csv

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p1/preprocess/Atrial_Fibrillation/clinical_data/GSE143924.csv

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p1/preprocess/Atrial_Fibrillation/clinical_data/GSE235307.csv

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p1/preprocess/Atrial_Fibrillation/code/GSE115574.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atrial_Fibrillation"
+cohort = "GSE115574"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
+in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE115574"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atrial_Fibrillation/GSE115574.csv"
+out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE115574.csv"
+out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE115574.csv"
+json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The series title and summary indicate "gene expression microarrays" were used
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify rows for trait, age, gender.
+# Examining the provided dictionary:
+# {0: ['disease state: atrial fibrillation patient with severe mitral regurgitation',
+#      'disease state: sinus rhythm patient with severe mitral regurgitation'],
+#  1: ['tissue: left atrium  - heart', 'tissue: right atrium  - heart']}
+# Row 0 has multiple unique values related to AF vs. sinus rhythm.
+trait_row = 0
+age_row = None    # No row provides age info
+gender_row = None # No row provides gender info
+
+# 2.2 Define conversion functions
+
+def convert_trait(value: str) -> int:
+    """Convert trait info to a binary indicator: AF=1, otherwise=0."""
+    # Extract the part after the first colon
+    parts = value.split(':', 1)
+    raw_str = parts[1].strip().lower() if len(parts) > 1 else value.lower()
+    if 'atrial fibrillation' in raw_str:
+        return 1
+    elif 'sinus rhythm' in raw_str:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str) -> float:
+    """Unused here because age_row is None, but define a placeholder."""
+    return None
+
+def convert_gender(value: str) -> int:
+    """Unused here because gender_row is None, but define a placeholder."""
+    return None
+
+# 3. Save Metadata (initial filtering)
+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_features = geo_select_clinical_features(
+        clinical_df=clinical_data,  # Assumes 'clinical_data' DataFrame is already in environment
+        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 selected features
+    preview_dict = preview_df(selected_features, n=5, max_items=200)
+    print("Clinical Features Preview:", preview_dict)
+
+    # Save the extracted clinical features
+    selected_features.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the probe IDs (e.g. '1007_s_at', '1053_at'), these are Affymetrix probe identifiers
+# which require mapping to official gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify the columns for the probe IDs and gene symbols in the annotation dataframe.
+#    In this dataset, they are stored in 'ID' and 'Gene Symbol'.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print the dimensions and the first 10 gene symbols to confirm
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 10 gene symbols:", gene_data.index[:10])
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_features, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+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=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Atrial_Fibrillation/code/GSE143924.py

@@ -0,0 +1,153 @@
+# 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/1/Atrial_Fibrillation/GSE143924.csv"
+out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE143924.csv"
+out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE143924.csv"
+json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+############################
+# 1. Gene Expression Data Availability
+############################
+is_gene_available = True  # Based on "Whole-tissue gene expression patterns" in the series summary
+
+############################
+# 2. Variable Availability and Data Type Conversion
+############################
+# From the sample characteristics dictionary:
+# {0: ['tissue: epicardial adipose tissue'],
+#  1: ['patient diagnosis: sinus rhythm after surgery',
+#      'patient diagnosis: postoperative atrial fibrillation after surgery (POAF)']}
+
+# The trait "Atrial_Fibrillation" can be inferred from row 1 since it contains
+# "sinus rhythm after surgery" vs. "postoperative atrial fibrillation after surgery (POAF)".
+trait_row = 1
+
+# There's no mention of age or gender information in the dictionary,
+# thus they are considered not available.
+age_row = None
+gender_row = None
+
+# Data Type Conversions
+def convert_trait(value: str) -> Optional[int]:
+    # Extract the value after the colon
+    parts = value.split(':', 1)
+    val_str = parts[1].strip() if len(parts) > 1 else value.strip()
+    
+    # Map recognized patterns to 0 or 1
+    if val_str.lower() == 'sinus rhythm after surgery':
+        return 0
+    elif 'postoperative atrial fibrillation' in val_str.lower():
+        return 1
+    else:
+        return None
+
+def convert_age(value: str) -> Optional[float]:
+    # No age data is truly available here; returning None
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # No gender data is truly available here; returning None
+    return None
+
+############################
+# 3. Save Metadata (Initial Filtering)
+############################
+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
+)
+
+############################
+# 4. Clinical Feature Extraction
+############################
+if trait_row is not None:
+    # Suppose clinical_data is our input DataFrame of sample characteristics
+    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 extracted clinical features
+    preview = preview_df(selected_clinical, n=5)
+    print("Selected Clinical Features Preview:", preview)
+    
+    # Save the selected clinical data
+    selected_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on biomedical knowledge, the listed gene identifiers appear to be recognized human gene symbols.
+# Therefore, they do not require additional gene symbol mapping.
+print("requires_gene_mapping = False")
+# STEP5
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+# Replace "selected_clinical_df" with the correct variable "selected_clinical"
+linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+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=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Atrial_Fibrillation/code/GSE235307.py

@@ -0,0 +1,177 @@
+# 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/1/Atrial_Fibrillation/GSE235307.csv"
+out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE235307.csv"
+out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE235307.csv"
+json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression availability
+is_gene_available = True  # From the background information, this dataset involves gene expression analysis
+
+# Step 2.1: Identify row indices for trait, age, and gender
+trait_row = 5    # "cardiac rhythm after 1 year follow-up: Sinus rhythm / Atrial fibrillation"
+age_row = 2      # "age: ##"
+gender_row = 1   # "gender: Male / Female"
+
+# Step 2.2: Define functions for data type conversion
+def convert_trait(value: str):
+    """
+    Convert the trait data into binary (0 or 1).
+    Expecting strings like 'cardiac rhythm after 1 year follow-up: Sinus rhythm'
+    or 'cardiac rhythm after 1 year follow-up: Atrial fibrillation'.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[-1].strip().lower()
+    if 'atrial fibrillation' in val:
+        return 1
+    elif 'sinus rhythm' in val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    """
+    Convert the age data into a continuous float.
+    Expecting strings like 'age: 64'.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[-1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender data into binary (female=0, male=1).
+    Expecting strings like 'gender: Male' or 'gender: Female'.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[-1].strip().lower()
+    if val == 'male':
+        return 1
+    elif val == 'female':
+        return 0
+    return None
+
+# Step 3: Conduct initial filtering and 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)
+)
+
+# Step 4: If trait data is available, extract clinical features and preview
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,       # Assume 'clinical_data' DataFrame is loaded from previous steps
+        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 selected clinical features:", preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# From examining the provided gene expression data index (4,5,6...23), 
+# these do not look like standard human gene symbols.
+# They are more likely numeric probe IDs or array-specific identifiers.
+# Therefore, mapping to recognized human gene symbols is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which column in 'gene_annotation' matches the gene expression data ID and which column stores gene symbols.
+#    From the preview, 'ID' in the annotation appears to match the numeric IDs in the expression data,
+#    and 'GENE_SYMBOL' provides the corresponding gene symbols.
+
+# 2. Get the gene mapping dataframe by extracting those columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply the mapping to convert probe-level expression to gene-level expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For reference, let's print the shape and a small preview of the mapped gene_data.
+print("Mapped gene_data shape:", gene_data.shape)
+print("Preview of mapped gene_data:")
+print(preview_df(gene_data))
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+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=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Atrial_Fibrillation/code/GSE41177.py

@@ -0,0 +1,178 @@
+# 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/1/Atrial_Fibrillation/GSE41177.csv"
+out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE41177.csv"
+out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE41177.csv"
+json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # From the background stating "microarray analysis revealed ...", indicating gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+#   Trait (Atrial_Fibrillation): All patients have AF (persistent AF), so there's no variation (constant). Hence, not available.
+trait_row = None
+
+#   Age: Multiple unique age values are found in row 2.
+age_row = 2
+
+#   Gender: Multiple unique gender values are found in row 1.
+gender_row = 1
+
+# 2.2 Data Type Conversion
+
+def convert_trait(x: str) -> Optional[int]:
+    """
+    Convert any given value of the trait 'Atrial_Fibrillation' to a binary value.
+    However, here it's not used because trait_row is None for this dataset (constant trait).
+    Example logic shown for completeness.
+    """
+    if not x or ':' not in x:
+        return None
+    # If data indicated presence of AF, assign 1, otherwise 0.
+    # (In this dataset, it's constant, so this function is effectively unused.)
+    return 1
+
+def convert_age(x: str) -> Optional[float]:
+    """
+    Convert age string 'age: XXY' to a float. 
+    If unknown, return None.
+    """
+    if not x or ':' not in x:
+        return None
+    value = x.split(':', 1)[1].strip()  # e.g. "62Y"
+    value = value.replace('Y', '').strip()
+    if not value.isdigit():
+        return None
+    return float(value)
+
+def convert_gender(x: str) -> Optional[int]:
+    """
+    Convert gender string 'gender: male/female' to a binary value:
+        female -> 0
+        male   -> 1
+    If unknown, return None.
+    """
+    if not x or ':' not in x:
+        return None
+    gender_str = x.split(':', 1)[1].strip().lower()
+    if 'female' in gender_str:
+        return 0
+    if 'male' in gender_str:
+        return 1
+    return None
+
+# 3. Save Metadata (Initial filtering)
+#    Trait data is not available if trait_row is None.
+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
+)
+
+# 4. Clinical Feature Extraction
+#    Skip if trait_row is None.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait="Atrial_Fibrillation",
+        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
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of Selected Clinical Features:", preview)
+
+    # Save
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "1007_s_at", "1053_at") are Affymetrix probe IDs rather than standard human gene symbols.
+# Hence, we conclude that the data requires gene symbol mapping.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the relevant columns in the annotation dataframe:
+#    - 'ID' holds the same identifiers as gene_data.index (e.g., "1007_s_at")
+#    - 'Gene Symbol' holds the gene symbols.
+
+# 2. Extract mapping columns:
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level measurements to gene-level expression data:
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+import pandas as pd
+
+# Since trait_row is None, we have no clinical data to link. According to the instructions, we skip linking
+# and other steps requiring the trait. We only normalize and save gene data, then perform final validation
+# to mark this dataset as not usable (due to missing trait data).
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Final quality validation and metadata saving (trait not available).
+#    Since is_final=True, we must provide dummy values for 'df' and 'is_biased'.
+empty_df = pd.DataFrame()  # Placeholder DataFrame
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,         # Gene data is available
+    is_trait_available=False,       # Trait data is not available
+    is_biased=True,                 # Mark as biased/unusable since we're missing core trait data
+    df=empty_df,                    # Passing an empty df to satisfy function signature
+    note="No trait data available, skipping clinical linkage. Gene data extracted only."
+)
+
+# 3. Since the dataset is not usable, we do not proceed with saving any final linked data.

p1/preprocess/Atrial_Fibrillation/code/GSE47727.py

@@ -0,0 +1,124 @@
+# 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/1/Atrial_Fibrillation/GSE47727.csv"
+out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/GSE47727.csv"
+out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/GSE47727.csv"
+json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The platform (HumanHT-12 V3.0) indicates a typical gene expression microarray
+
+# 2. Variable Availability
+trait_row = None  # No row found containing AF or any case/control labels
+age_row = 0       # Key 0 has multiple unique values for age
+gender_row = 1    # Key 1 has at least two distinct values (male/female)
+
+# 2.2 Data Type Conversion
+def convert_trait(value: str):
+    """
+    Convert the trait value to a binary indicator (0 or 1).
+    This dataset has no trait info, so we'll implement a placeholder function.
+    """
+    # Normally, we'd parse after the colon and map "AF" -> 1, "control" -> 0, else None
+    return None
+
+def convert_age(value: str):
+    """
+    Convert the 'age (yrs)' string to a float.
+    Returns None if the format is unexpected.
+    """
+    try:
+        # Split at the colon, take the part after the colon, strip, and convert to float
+        parts = value.split(':')
+        if len(parts) < 2:
+            return None
+        return float(parts[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender to binary: female -> 0, male -> 1.
+    Returns None if the format is unexpected.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == 'female':
+        return 0
+    elif gender_str == 'male':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (initial filtering)
+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
+# Skip this step because trait_row is None (no trait data available)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The IDs (e.g., "ILMN_1343291") appear to be Illumina probe IDs, not standard human gene symbols.
+# Therefore, this data requires mapping to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in gene_annotation that match the probe IDs from gene_data (the 'ID' column),
+#    and the column that holds gene symbols (the 'Symbol' column).
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 2. Convert probe-level measurements to gene-level measurements.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print some basic info for verification.
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols after mapping:")
+print(gene_data.index[:20])

p1/preprocess/Atrial_Fibrillation/code/TCGA.py

@@ -0,0 +1,41 @@
+# 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/1/Atrial_Fibrillation/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Atrial_Fibrillation/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Atrial_Fibrillation/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Atrial_Fibrillation/cohort_info.json"
+
+import os
+
+# Step 1: Check directories in tcga_root_dir for anything relevant to "Atrial_Fibrillation"
+dir_list = os.listdir(tcga_root_dir)
+matching_dir = None
+
+# We'll perform a simple match check (case-insensitive)
+# If we had any subdirectory name containing "atrial" or "fibrillation", we would choose it.
+for d in dir_list:
+    if "atrial" in d.lower() or "fibrillation" in d.lower():
+        matching_dir = d
+        break
+
+# If no suitable directory is found, mark trait as skipped
+if matching_dir is None:
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Normally we'd proceed with file loading, but per instructions,
+    # no directory matches the trait. Thus this block won't execute.
+    pass

p1/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": 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": "No trait data available, skipping clinical linkage. Gene data extracted only."}, "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": "Dataset processed with GEO pipeline. Checked for missing values and bias."}, "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": "Dataset processed with GEO pipeline. Checked for missing values and bias."}, "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": "Dataset processed with GEO pipeline. Checked for missing values and bias."}, "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}}

p1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.csv

@@ -0,0 +1,3 @@
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p1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE113842.csv

@@ -0,0 +1,3 @@
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p1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE123302.csv

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p1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE87847.csv

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p1/preprocess/Autism_spectrum_disorder_(ASD)/clinical_data/GSE89594.csv

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p1/preprocess/Autism_spectrum_disorder_(ASD)/code/GSE111175.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Autism_spectrum_disorder_(ASD)"
+cohort = "GSE111175"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)"
+in_cohort_dir = "../DATA/GEO/Autism_spectrum_disorder_(ASD)/GSE111175"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/GSE111175.csv"
+out_gene_data_file = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/gene_data/GSE111175.csv"
+out_clinical_data_file = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/clinical_data/GSE111175.csv"
+json_path = "./output/preprocess/1/Autism_spectrum_disorder_(ASD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the series description mentioning "Leukocyte gene expression levels"
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary, we see that:
+#   - The "diagnosis" information is in key 3 with multiple values (ASD, TD, LD, etc.),
+#     so trait_row = 3.
+#   - The "age" information is in key 2 with multiple numeric values,
+#     so age_row = 2.
+#   - The "gender" information is in key 1, but it seems only "M" is present (a single unique value),
+#     so it is not useful for association studies. We'll set gender_row = None.
+
+trait_row = 3
+age_row = 2
+gender_row = None
+
+# 2.2 Data Type Conversion
+def convert_trait(x: str):
+    """
+    Convert diagnosis info into a binary value (0 or 1), focusing on whether
+    the individual is on the autism spectrum.
+    'ASD', 'PDDNOS', 'AutFeat' are mapped to 1,
+    'TD', 'LD', 'PreemieNoDelay' are mapped to 0,
+    otherwise None.
+    """
+    # Extract the part after the colon
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip().lower()
+
+    if value in ["asd", "pddnos", "autfeat"]:
+        return 1
+    elif value in ["td", "ld", "preemienodelay"]:
+        return 0
+    else:
+        return None
+
+def convert_age(x: str):
+    """
+    Convert age info into a continuous float.
+    If parsing fails, return None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip()
+    try:
+        return float(value)
+    except ValueError:
+        return None
+
+def convert_gender(x: str):
+    """
+    Convert gender info into a binary value (0 or 1).
+    Female (F) -> 0, Male (M) -> 1, otherwise None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip().lower()
+
+    if value in ["m", "male"]:
+        return 1
+    elif value in ["f", "female"]:
+        return 0
+    else:
+        return None
+
+# 3. Save Metadata with initial filtering
+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
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_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 if gender_row is not None else None
+    )
+
+    # Preview the extracted features
+    preview = preview_df(selected_features)
+    print("Preview of selected clinical features:", preview)
+
+    # Save the clinical data to CSV
+    selected_features.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given gene identifiers are Illumina probe IDs, not standard human gene symbols. 
+# Therefore, they need to be mapped to human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Determine which columns in the gene_annotation dataframe correspond to the probe IDs and gene symbols.
+#    From our annotation preview, we see "ID" holds the same Illumina probe IDs, and "Symbol" holds the gene symbols.
+
+# 2. Build the gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene-level measurements by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print output to verify the new gene_data.
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols in the mapped data:\n", gene_data.index[:20])
+# STEP7
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_features, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality validation and metadata saving
+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=trait_biased,
+    df=linked_data_final,
+    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+)
+
+# 6. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)