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p1/preprocess/Pancreatic_Cancer/GSE131027.csv

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p1/preprocess/Pancreatic_Cancer/code/GSE157494.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE157494"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE157494"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Pancreatic_Cancer/GSE157494.csv"
+out_gene_data_file = "./output/preprocess/1/Pancreatic_Cancer/gene_data/GSE157494.csv"
+out_clinical_data_file = "./output/preprocess/1/Pancreatic_Cancer/clinical_data/GSE157494.csv"
+json_path = "./output/preprocess/1/Pancreatic_Cancer/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) Check gene expression data availability
+# Based on the series summary, it appears gene expression profiling was performed on this dataset.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# After reviewing the sample characteristics dictionary:
+# {0: ['sample type: xenografted tumor', 'sample type: Cell line']}
+# we see no key indicating the "Pancreatic_Cancer" trait variation, age, or gender.
+# Hence, all are considered not available because the entire dataset is already from pancreatic cancer
+# (no variation), and no age/gender info is provided.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions as placeholders
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3) Perform initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Since trait_row is None, clinical data extraction step is skipped.
+# 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])
+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. From the annotation preview, the 'ID' column matches the probe IDs in the expression data,
+#    and the 'Gene Symbol' column contains the corresponding gene symbols.
+probe_col = 'ID'
+symbol_col = 'Gene Symbol'
+
+# 2. Get a gene mapping dataframe by extracting the two columns from the annotation dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print some basic info about the resulting gene_data
+print("New gene_data shape:", gene_data.shape)
+print("First 20 mapped gene symbols:", gene_data.index[:20].tolist())
+# STEP 7
+# In this dataset, the trait information is not available (trait_row is None). Therefore, we skip clinical linking
+# and final data assembly. We only normalize and save the gene expression data, then mark the dataset as not usable
+# for trait-based analyses.
+
+# 1) Normalize gene symbols in the gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Since trait data is not available, we do not link clinical data or do missing-value handling on trait/covariates.
+#    Proceed to final validation to record dataset metadata.
+
+# Use an empty dataframe for final validation to meet the function's parameter requirements.
+empty_df = pd.DataFrame()
+
+# 3) Conduct final validation and save cohort info
+#    This dataset has gene data, but no trait data => it is not usable for trait-based analysis.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,  # not actually tested since trait is absent
+    df=empty_df,
+    note="Trait data not available. Only gene data is provided. This dataset is not usable for trait-based analysis."
+)
+
+# 4) If the dataset were usable, we would save the final linked data. Here it will not be usable because the trait is missing.
+if is_usable:
+    # No final data to save, since no trait is available
+    pass

p1/preprocess/Pancreatic_Cancer/code/GSE183795.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE183795"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE183795"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Pancreatic_Cancer/GSE183795.csv"
+out_gene_data_file = "./output/preprocess/1/Pancreatic_Cancer/gene_data/GSE183795.csv"
+out_clinical_data_file = "./output/preprocess/1/Pancreatic_Cancer/clinical_data/GSE183795.csv"
+json_path = "./output/preprocess/1/Pancreatic_Cancer/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 info, it is a microarray gene-expression dataset.
+
+# 2) Variable Availability and Data Type Conversion
+# Based on the sample characteristics, row 0 indicates whether a sample is tumor or not.
+trait_row = 0
+age_row = None  # No age information found
+gender_row = None  # No gender information found
+
+# Data type for trait is binary: tumor (1) vs. normal/adjacent tissue (0).
+def convert_trait(value: str):
+    # Typically "tissue: tumor", "tissue: adjacent non-tumor", "tissue: Normal pancreas"
+    # We take the substring after the colon, then map.
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'tumor' in val and 'non' not in val:  # covers "tumor" but not "non-tumor"
+        return 1
+    elif 'tumor' in val or 'normal' in val:
+        return 0
+    return None
+
+# Age and gender are not available, but we must define the functions for completeness
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    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_features_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical features
+    preview = preview_df(clinical_features_df)
+    print("Preview of selected clinical features:", preview)
+
+    # Save the clinical features to CSV
+    clinical_features_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])
+# The given identifiers (numeric strings like '7896748') are not standard human gene symbols.
+# Therefore, they require mapping to known 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) Identify the columns that store the probe ID and the gene symbol
+#    - The probe ID is in 'ID' and the gene symbol information is in 'gene_assignment'.
+probe_col = 'ID'
+symbol_col = 'gene_assignment'
+
+# 2) Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3) Convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For quick inspection, let's print out the shape and a small preview of the mapped gene data
+print("Mapped gene expression data shape:", gene_data.shape)
+print(gene_data.head())
+# STEP 7
+import pandas as pd
+
+# 1) Load the clinical dataframe from the CSV saved in Step 2, ensuring the single row "Pancreatic_Cancer" becomes our index
+selected_clinical_df = pd.read_csv(out_clinical_data_file)
+selected_clinical_df.index = [trait]  # Make "Pancreatic_Cancer" the row index
+
+# 2) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 3) Link the clinical and genetic data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 4) Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Determine whether the trait and demographic features are biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6) Conduct final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Trait is available. Completed linking and QC steps."
+)
+
+# 7) If the dataset is usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Pancreatic_Cancer/code/GSE222788.py

@@ -0,0 +1,145 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE222788"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE222788"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Pancreatic_Cancer/GSE222788.csv"
+out_gene_data_file = "./output/preprocess/1/Pancreatic_Cancer/gene_data/GSE222788.csv"
+out_clinical_data_file = "./output/preprocess/1/Pancreatic_Cancer/clinical_data/GSE222788.csv"
+json_path = "./output/preprocess/1/Pancreatic_Cancer/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. Decide whether this dataset contains gene expression data
+is_gene_available = True  # From the background, it uses a gene expression profiling panel
+
+# 2. Determine variable availability
+trait_row = None   # No row indicating variation of "Pancreatic_Cancer"; the entire cohort has this trait
+age_row = None     # No age information found
+gender_row = None  # No gender information found
+
+# 2.2 Define data type conversion functions
+def convert_trait(value: str) -> int:
+    # No actual data is available; return None for all
+    return None
+
+def convert_age(value: str) -> float:
+    # No age data is available; return None for all
+    return None
+
+def convert_gender(value: str) -> int:
+    # No gender data is available; return None for all
+    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 if trait data is not available)
+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,
+        convert_gender=convert_gender
+    )
+    print(preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# 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 identifiers (e.g., "A2M-mRNA", "ABCB1-mRNA"), they are recognized human gene symbols
+# with an appended "-mRNA" suffix. No additional mapping is required beyond removing the suffix.
+print("They are recognized human gene symbols with an added '-mRNA' suffix.\nrequires_gene_mapping = False")
+import pandas as pd
+import os
+
+# STEP 5
+# This code finalizes preprocessing by normalizing gene symbols, linking clinical data (if any),
+# performing quality control, and saving the results if the dataset is usable for trait analysis.
+
+# 1. Normalize gene symbols in the gene expression data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Check trait data availability by verifying whether the clinical CSV file exists
+if not os.path.exists(out_clinical_data_file):
+    # No trait data => finalize with is_final=True but supply a dummy dataframe and a biased flag
+    # so that validate_and_save_cohort_info won't raise an error
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Marking it as biased to ensure is_usable=False
+        df=pd.DataFrame(),  # Empty DataFrame
+        note="No trait data => dataset not suitable for trait association."
+    )
+else:
+    # 3. If trait data is available, read it and link with the genetic data
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 4. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 5. Check for any bias in trait or demographics
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 6. Final quality 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="Trait is available. Completed linking and QC steps."
+    )
+
+    # 7. Save the final linked data if usable
+    if is_usable:
+        linked_data.to_csv(out_data_file)

p1/preprocess/Pancreatic_Cancer/code/GSE223409.py

@@ -0,0 +1,199 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+cohort = "GSE223409"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
+in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE223409"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Pancreatic_Cancer/GSE223409.csv"
+out_gene_data_file = "./output/preprocess/1/Pancreatic_Cancer/gene_data/GSE223409.csv"
+out_clinical_data_file = "./output/preprocess/1/Pancreatic_Cancer/clinical_data/GSE223409.csv"
+json_path = "./output/preprocess/1/Pancreatic_Cancer/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) Decide if the dataset is likely to contain gene expression data
+is_gene_available = True  # Based on the references to oncogenic genes in the series
+
+# 2) Determine availability for trait, age, and gender from the sample characteristics
+#    After inspecting the dictionary, none of these variables are explicitly provided or vary.
+
+trait_row = None    # No row indicates "Pancreatic_Cancer" (it's presumably the same for all)
+age_row = None      # No mention or variability for age
+gender_row = None   # No mention or variability for gender
+
+# 2.2) Define data type conversion functions
+def convert_trait(value: str) -> Optional[float]:
+    """
+    Convert trait data to a binary (0,1). Unknown values go to None.
+    Since trait_row is None, this function won't be used, but we define it for completeness.
+    """
+    if not value or pd.isna(value):
+        return None
+    # Typically, parse after ":", if any
+    parts = value.split(':', 1)
+    val_str = parts[-1].strip() if len(parts) > 1 else value.strip()
+    # If we had actual detection logic, we'd do it here. Returning None by default.
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert age to continuous. Unknown values go to None.
+    Since age_row is None, this function won't be used, but defined for completeness.
+    """
+    if not value or pd.isna(value):
+        return None
+    parts = value.split(':', 1)
+    val_str = parts[-1].strip() if len(parts) > 1 else value.strip()
+    # Try to parse to float
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert gender to binary: female -> 0, male -> 1, unknown -> None.
+    Since gender_row is None, this function won't be used, but defined for completeness.
+    """
+    if not value or pd.isna(value):
+        return None
+    parts = value.split(':', 1)
+    val_str = parts[-1].strip().lower() if len(parts) > 1 else value.strip().lower()
+    if val_str.startswith('f'):
+        return 0
+    elif val_str.startswith('m'):
+        return 1
+    return None
+
+# 3) Conduct initial filtering on dataset usability and save metadata
+#    Trait data availability is based on whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+passed_filtering = 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) Only if trait_row is not None would we extract clinical features.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait="Pancreatic_Cancer",      # or simply 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
+    print("Selected clinical features preview:", 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])
+print(
+    "The gene identifiers in the dataset are numeric and do not appear to be standard human gene symbols.\n"
+    "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) From inspection, 'ID' in gene_annotation aligns with the numeric row identifiers in gene_data,
+#    and 'GENE_SYMBOL' stores the gene symbols.
+prob_col = 'ID'
+gene_col = 'GENE_SYMBOL'
+
+# 2) Get the gene mapping dataframe
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3) Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1) Normalize gene symbols using the loaded gene_data from previous steps, then save the result.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Check if clinical data file exists. If missing, finalize by marking that trait data is not available
+#    but do NOT do a final validation because we don't have df/is_biased.
+if not os.path.exists(out_clinical_data_file):
+    print("No clinical data file found. This dataset cannot be used for trait-based analysis.")
+    # Record metadata but with is_final=False, indicating it fails trait requirement
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    # If clinical data is available, proceed with linking and QC steps
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+    # 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
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Determine if the trait/demographics are biased
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Conduct final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Trait is available. Completed linking and QC steps."
+    )
+
+    # 6) If the dataset is usable, save the final linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file)

p1/preprocess/Pancreatic_Cancer/code/TCGA.py

@@ -0,0 +1,118 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Pancreatic_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Pancreatic_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Pancreatic_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Pancreatic_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Pancreatic_Cancer/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for "Pancreatic_Cancer" or "PAAD"
+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 = "Pancreatic_Cancer"
+trait_abbreviation = "PAAD"
+
+target_subdir = None
+for sd in subdirectories:
+    if trait_keyword in sd or trait_abbreviation in sd:
+        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:
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    # 2. Locate clinical and genetic data files
+    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 = []
+
+print("candidate_age_cols =", candidate_age_cols)
+print("candidate_gender_cols =", candidate_gender_cols)
+
+extracted_cols = candidate_age_cols + candidate_gender_cols
+
+if extracted_cols:
+    extracted_df = clinical_df[extracted_cols]
+    print("Extracted Columns:", extracted_df.columns.tolist())
+    print("Preview (first 5 rows):", preview_df(extracted_df))
+else:
+    print("No candidate columns found.")
+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/Pancreatic_Cancer/gene_data/GSE120127.csv

@@ -0,0 +1,660 @@
+Gene,GSM3392954,GSM3392955,GSM3392956,GSM3392957,GSM3392958,GSM3392959
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p1/preprocess/Parkinsons_Disease/clinical_data/GSE72267.csv

@@ -0,0 +1,2 @@
+,GSM1859079,GSM1859080,GSM1859081,GSM1859082,GSM1859083,GSM1859084,GSM1859085,GSM1859086,GSM1859087,GSM1859088,GSM1859089,GSM1859090,GSM1859091,GSM1859092,GSM1859093,GSM1859094,GSM1859095,GSM1859096,GSM1859097,GSM1859098,GSM1859099,GSM1859100,GSM1859101,GSM1859102,GSM1859103,GSM1859104,GSM1859105,GSM1859106,GSM1859107,GSM1859108,GSM1859109,GSM1859110,GSM1859111,GSM1859112,GSM1859113,GSM1859114,GSM1859115,GSM1859116,GSM1859117,GSM1859118,GSM1859119,GSM1859120,GSM1859121,GSM1859122,GSM1859123,GSM1859124,GSM1859125,GSM1859126,GSM1859127,GSM1859128,GSM1859129,GSM1859130,GSM1859131,GSM1859132,GSM1859133,GSM1859134,GSM1859135,GSM1859136,GSM1859137
+Parkinsons_Disease,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p1/preprocess/Parkinsons_Disease/code/GSE101534.py

@@ -0,0 +1,264 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE101534"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE101534"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE101534.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE101534.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE101534.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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 data availability
+# Based on the series title "Genome-wide expression profiling...", we conclude:
+is_gene_available = True
+
+# Step 2: Determine variable availability and define row indices
+# The sample characteristics dictionary only has key=0 with multiple distinct values
+# representing different mutation statuses ("healthy", "patient", etc.).
+# We'll treat this as the trait variable (Parkinson's vs. non-Parkinson's).
+trait_row = 0
+age_row = None
+gender_row = None
+
+# Step 2.2: Data Type Conversion Functions
+def convert_trait(value: str):
+    """
+    Convert trait string to binary indicator:
+        'healthy' -> 0
+        'gene corrected' -> 0
+        'patient' -> 1
+        'inserted G2019S' -> 1
+        Unknown -> None
+    """
+    # Extract substring after colon if present
+    parts = value.split(':')
+    if len(parts) > 1:
+        val = parts[1].strip().lower()
+    else:
+        val = value.strip().lower()
+
+    if val == 'healthy':
+        return 0
+    elif val == 'gene corrected':
+        return 0
+    elif val == 'patient':
+        return 1
+    elif val == 'inserted g2019s':
+        return 1
+    else:
+        return None
+
+def convert_age(value: str):
+    # Not used because age_row is None, but defined as required
+    return None
+
+def convert_gender(value: str):
+    # Not used because gender_row is None, but defined as required
+    return None
+
+# Step 3: Initial filtering and saving metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (only if trait data is available)
+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,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical data
+    preview = preview_df(selected_clinical_df)
+    print("Clinical data preview:", preview)
+
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These appear to be numeric identifiers rather than standard human gene symbols, so 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 6: Gene Identifier Mapping
+
+# After reviewing the annotation and expression data, it appears that the numeric row IDs in gene_data
+# (e.g., "16650001", "16650003", etc.) do not match the "ID" column in gene_annotation (e.g., "16657436", "16657440").
+# There's no direct overlap, so standard probe-to-gene mapping yields an empty dataframe.
+
+# 1) Let's check if there's any overlap at all between gene_data.index and the annotation "ID":
+common_ids_with_ID = set(gene_data.index).intersection(set(gene_annotation["ID"].astype(str)))
+if len(common_ids_with_ID) > 0:
+    # If we somehow have overlap with the "ID" column in annotation:
+    print("Using gene_annotation['ID'] to map gene_data.")
+    mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GB_ACC")
+    gene_data = apply_gene_mapping(gene_data, mapping_df)
+    print("Mapping dataframe shape:", mapping_df.shape)
+    print("Gene expression dataframe shape after mapping:", gene_data.shape)
+else:
+    # 2) Otherwise, check if there's overlap with the "SPOT_ID" column:
+    if "SPOT_ID" in gene_annotation.columns:
+        # Convert SPOT_ID to string just in case
+        gene_annotation["SPOT_ID"] = gene_annotation["SPOT_ID"].astype(str)
+        common_ids_with_spot = set(gene_data.index).intersection(set(gene_annotation["SPOT_ID"]))
+        if len(common_ids_with_spot) > 0:
+            print("Using gene_annotation['SPOT_ID'] to map gene_data.")
+            # Create a mapping dataframe from SPOT_ID to GB_ACC
+            temp_annot = gene_annotation.rename(columns={"SPOT_ID": "ID", "GB_ACC": "Gene"})
+            temp_annot = temp_annot[["ID", "Gene"]]
+            # Map
+            gene_data = apply_gene_mapping(gene_data, temp_annot)
+            print("Gene expression dataframe shape after mapping:", gene_data.shape)
+        else:
+            # 3) If no column overlaps, skip mapping
+            print(
+                "No overlap found between gene_data row IDs and any relevant column in gene_annotation. "
+                "Skipping probe-to-gene mapping step. The gene_data DataFrame remains as probe-level data."
+            )
+    else:
+        # If there's no SPOT_ID column, just skip
+        print(
+            "No overlap found between gene_data row IDs and gene_annotation['ID'], "
+            "and 'SPOT_ID' column not available. Skipping mapping step."
+        )
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE103099.py

@@ -0,0 +1,234 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE103099"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE103099"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE103099.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE103099.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE103099.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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. Determine if gene expression data is likely available
+is_gene_available = True  # Based on the series title mentioning LRRK2 expression
+
+# 2. Identify data availability for trait, age, and gender
+
+# From inspection:
+# - Row 0 has only "gender: female" → single value → treat as not available
+gender_row = None
+
+# - Row 1 has only "age: 2 year old" → single value → treat as not available
+age_row = None
+
+# - Row 2 describes infection status (control vs. infected). We interpret this as our trait.
+#   Hence, multiple unique values → potential trait data
+trait_row = 2
+
+# 2.2 Define data type conversion functions
+def convert_trait(x: str) -> int:
+    """
+    Converts raw infection descriptor into a binary trait:
+    0 = no infection (control), 1 = infection (Parkinson's-like).
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None  # Unknown format
+    val = parts[-1].strip().lower()
+    if val == "no infection":
+        return 0
+    else:
+        return 1
+
+# Age and gender are not available, so conversion functions are not needed.
+convert_age = None
+convert_gender = None
+
+# 3. Initial filtering and saving metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features if trait data is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # assumed to be loaded in a previous step
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview and save clinical data
+    preview = preview_df(selected_clinical_df)
+    print("Clinical Data Preview:", preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 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., "1007_s_at", "1255_g_at"), these are Affymetrix probe set IDs
+# and are not standard human gene symbols; therefore, mapping to gene symbols is needed.
+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))
+# Gene Identifier Mapping
+
+# 1. Identify the appropriate columns for probe IDs and gene symbols.
+#    From inspection of the gene annotation preview:
+#    - The 'ID' column matches the probe IDs like '1007_s_at'.
+#    - The 'Gene Symbol' column contains the gene symbols (e.g., 'DDR1 /// MIR4640').
+
+# 2. Create a mapping dataframe between probe IDs and gene symbols.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation, 
+    prob_col='ID', 
+    gene_col='Gene Symbol'
+)
+
+# 3. Convert probe-level data to gene-level data by applying the mapping, 
+#    distributing expression values equally across multiple mapped genes, 
+#    and summing for genes that map from multiple probes.
+gene_data = apply_gene_mapping(
+    expression_df=gene_data, 
+    mapping_df=mapping_df
+)
+
+# Optional: Preview result
+print("Mapped gene_data shape:", gene_data.shape)
+print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE202665.py

@@ -0,0 +1,253 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE202665"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE202665"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE202665.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE202665.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE202665.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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. Determine whether the dataset likely contains gene expression data
+is_gene_available = True  # Based on the series description ("mRNAarray")
+
+# 2. Identify data availability and define data type conversion functions
+trait_row = 0
+age_row = 3
+gender_row = None  # All samples are male; hence it's effectively a constant variable
+
+def convert_trait(value: str) -> int:
+    """
+    Convert trait data to binary integers:
+      'Parkinson's disease' -> 1
+      'Healthy Control' -> 0
+      otherwise -> None
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if "parkinson" in val:
+        return 1
+    elif "healthy" in val:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str) -> float:
+    """
+    Convert age data to a continuous variable (float or int).
+    Returns None if conversion is not possible.
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert gender data to binary integers:
+      female -> 0
+      male -> 1
+      otherwise -> None
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if "female" in val:
+        return 0
+    elif "male" in val:
+        return 1
+    else:
+        return None
+
+# 3. Conduct initial filtering with validate_and_save_cohort_info
+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,
+    note=''
+)
+
+# 4. If trait data is available, extract clinical features, preview, and save
+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 = preview_df(selected_clinical_df, n=5, max_items=200)
+    print("Clinical Data Preview:", preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the given gene identifiers (1, 2, 3, ...), they do not appear to be standard human gene symbols.
+# They likely require mapping to recognized 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify columns in the annotation dataframe
+#    The 'ID' column in gene_annotation matches the row IDs in the gene expression data,
+#    and 'GENE_SYMBOL' holds the gene symbols to which we need to map.
+
+# 2. Get a gene mapping dataframe
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="GENE_SYMBOL"
+)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# (Optional) Preview the mapped gene data dimensions and a few rows
+print("Mapped Gene Data Shape:", gene_data.shape)
+print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE202667.py

@@ -0,0 +1,227 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE202667"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE202667"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE202667.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE202667.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE202667.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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 "RNA signatures", this dataset likely contains gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+trait_row = 0  # 0 -> ["disease state: Parkinson's disease", 'disease state: Healthy Control']
+age_row = 3    # 3 -> ['age: 53', 'age: 57', 'age: 63', 'age: 75', ...]
+gender_row = None  # Only 'male' is observed (constant), so treat as not available
+
+# Define the conversion functions
+def convert_trait(x: str):
+    """Convert 'disease state: X' to binary (Parkinson's disease -> 1, Healthy Control -> 0)."""
+    if not isinstance(x, str):
+        return None
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'parkinson' in val:
+        return 1
+    elif 'healthy control' in val:
+        return 0
+    return None
+
+def convert_age(x: str):
+    """Convert 'age: XX' to numeric."""
+    if not isinstance(x, str):
+        return None
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        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_row is not None)
+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,
+        convert_gender=None  # Not used since gender_row is None
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric-only identifiers (e.g., '1', '2', '3', ...),
+# it is clear they are not standard human 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 columns in the annotation DataFrame that correspond to the same IDs as in the gene expression data,
+#    and the column that holds the gene symbols.
+#    From the previews, it appears "ID" matches the numeric IDs in the gene expression data,
+#    and "GENE_SYMBOL" corresponds to the gene symbols.
+
+# 2. Obtain the gene mapping DataFrame using these two columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For confirmation, let's print out some basic information about the remapped gene data.
+print("Remapped gene_data shape:", gene_data.shape)
+print("First 20 gene indices after mapping:")
+print(gene_data.index[:20])
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE30335.py

@@ -0,0 +1,71 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE30335"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE30335"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE30335.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE30335.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE30335.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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. Determine availability of gene expression data
+is_gene_available = True  # From the series summary, it clearly states "Blood gene expression"
+
+# 2. Determine availability for trait, age, and gender
+#    and define the corresponding data type conversion functions.
+
+# From the provided dictionary and background, 
+# there is no Parkinson's Disease status key (trait), 
+# no mention of age, and everyone in the dataset is male (constant feature).
+# Hence, none of them are effectively available for analysis.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Placeholder conversion functions returning None
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save initial metadata using validate_and_save_cohort_info 
+#    (trait availability is False because trait_row is None).
+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. Since trait_row is None, we skip the clinical feature extraction step.

p1/preprocess/Parkinsons_Disease/code/GSE49126.py

@@ -0,0 +1,220 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE49126"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE49126"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE49126.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE49126.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE49126.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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. Determine gene expression data availability
+is_gene_available = True  # Based on the provided info ("Transcriptomic profiling" on Agilent)
+
+# 2. Variable availability and data type conversion
+# Observing the sample characteristics dictionary:
+#   0: ['disease state: control', "disease state: Parkinson's disease"]
+#   1: ['cell type: peripheral blood mononuclear cells']
+# We see that row 0 has two unique values ("control" vs. "Parkinson's disease"), which map to the trait.
+trait_row = 0
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[int]:
+    if ':' in value:
+        val = value.split(':', 1)[1].strip().lower()
+        if 'control' in val:
+            return 0
+        elif 'parkinson' in val:
+            return 1
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # Not available in this dataset
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # Not available in this dataset
+    return None
+
+# 3. Save metadata via 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. If trait data is available, extract clinical features
+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 = preview_df(selected_clinical_df)
+    print("Clinical Data Preview:", preview)
+
+    # Save extracted clinical dataframe
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (numeric indices), they are not standard human gene symbols.
+# Therefore, they require mapping 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. Decide which columns in 'gene_annotation' match the gene expression data and the gene symbols:
+#    From the preview, the 'ID' column in 'gene_annotation' appears to correspond to the probe IDs
+#    in 'gene_data'. The 'GENE_SYMBOL' column is presumably the gene symbol field.
+
+probe_id_column = "ID"
+gene_symbol_column = "GENE_SYMBOL"
+
+# 2. Get a dataframe mapping probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, probe_id_column, gene_symbol_column)
+
+# 3. Convert the probe-level measurements to gene-level by applying the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Just print a brief shape info for verification
+print("Mapped gene_data shape:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE57475.py

@@ -0,0 +1,244 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE57475"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE57475"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE57475.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE57475.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE57475.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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)
+import re
+
+# 1) Determine if gene expression data is available
+is_gene_available = True  # Based on the series title and summary, it clearly involves gene expression data
+
+# 2) Identify the keys in the sample characteristics dictionary
+#    From the background, we see:
+#    key=0 -> age data
+#    key=1 -> gender data
+#    key=2 -> disease state (PD or control)
+trait_row = 2
+age_row = 0
+gender_row = 1
+
+# 2.2) Define conversion functions
+def convert_trait(value: str):
+    # Extract the portion after the first colon
+    match = re.split(r':\s*', value, maxsplit=1)
+    if len(match) < 2:
+        return None
+    val = match[1].strip().lower()
+    # Map PD to 1, control to 0
+    if "pd" in val:
+        return 1
+    elif "control" in val:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    # Extract the portion after the first colon
+    match = re.split(r':\s*', value, maxsplit=1)
+    if len(match) < 2:
+        return None
+    val = match[1].strip()
+    # Convert to float if possible
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Extract the portion after the first colon
+    match = re.split(r':\s*', value, maxsplit=1)
+    if len(match) < 2:
+        return None
+    val = match[1].strip().upper()
+    # Map F -> 0, M -> 1
+    if val == "F":
+        return 0
+    elif val == "M":
+        return 1
+    else:
+        return None
+
+# 3) Initial filtering on the usability of the dataset
+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 data is available, extract and preview clinical features
+if trait_row is not None:
+    df_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
+    clinical_preview = preview_df(df_clinical)
+    print("Clinical Feature Preview:", clinical_preview)
+
+    # Save the extracted clinical features
+    df_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers are Illumina probe IDs, not standard human gene symbols.
+# Therefore, we need to map them to the corresponding gene symbols.
+print("Based on the provided gene identifiers, they appear to be Illumina probe IDs "
+      "rather than standard human gene symbols.\nrequires_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 which columns in gene_annotation match the probe IDs in gene_data and the actual gene symbols.
+#    From the preview, the "ID" column in gene_annotation corresponds to the probe IDs (e.g. ILMN_1811966),
+#    and the "Symbol" column corresponds to the gene symbol (e.g. FCGR2B, TRIM44).
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# 2. Get a gene mapping dataframe by extracting these two columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Map probe-level expression data to gene-level expression data:
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE71220.py

@@ -0,0 +1,237 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE71220"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE71220"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE71220.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE71220.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE71220.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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 if this dataset contains gene expression data
+# Based on the background information, it mentions "Whole blood gene expression was measured" 
+# using Affymetrix microarray chips. Hence we can conclude:
+is_gene_available = True
+
+# Step 2: Check variable (trait, age, gender) availability
+
+# 2.1 Data Availability
+#   - The sample characteristics dictionary does not contain "Parkinson's Disease" or an equivalent field. 
+#     Therefore, we cannot map the trait "Parkinsons_Disease" to any row:
+trait_row = None
+
+#   - For age, the dictionary at key=2 contains age information ("age: XX"):
+age_row = 2
+
+#   - For gender, the dictionary at key=3 contains sex information ("Sex: F" or "Sex: M"):
+gender_row = 3
+
+# 2.2 Data Type Conversion
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the trait string to a binary value:
+      - If it indicates 'Parkinsons_Disease', return 1
+      - If it indicates a control or non-PD state, return 0
+      - Otherwise, return None
+    Since the dataset does not have explicit PD data, this function is mostly a placeholder.
+    """
+    # Typically, we parse after the colon:
+    val_part = value.split(':')[-1].strip().lower()
+    if 'parkinson' in val_part:
+        return 1
+    elif 'control' in val_part or 'no' in val_part:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert age from string to a float. If parsing fails, return None.
+    Example input: "age: 57"
+    """
+    val_part = value.split(':')[-1].strip()
+    try:
+        return float(val_part)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert gender to binary:
+      - Female (F) -> 0
+      - Male (M) -> 1
+      - Otherwise -> None
+    Example input: "Sex: F"
+    """
+    val_part = value.split(':')[-1].strip().lower()
+    if val_part == 'f':
+        return 0
+    elif val_part == 'm':
+        return 1
+    return None
+
+# Step 3: Save metadata (initial filtering) using validate_and_save_cohort_info
+#         trait data is not available because trait_row is None.
+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
+)
+
+# Step 4: Since trait_row is None, we do NOT proceed with clinical feature extraction (skip).
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on observation, these numeric IDs (e.g., 7892501, 7892504) appear to be probe identifiers, not standard gene symbols.
+# Therefore, they likely require mapping 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. Decide which columns in gene_annotation correspond to probe IDs and gene symbols
+probe_col = "ID"
+gene_col = "gene_assignment"
+
+# 2. Get a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: Print resulting shape to verify
+print("Gene data shape after mapping:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE72267.py

@@ -0,0 +1,239 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE72267"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE72267"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE72267.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE72267.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE72267.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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) Determine gene expression data availability.
+#    Based on the background info indicating Affymetrix transcriptomic data,
+#    we set is_gene_available to True.
+is_gene_available = True
+
+# 2) Identify the availability of trait, age, and gender data from the sample characteristics dictionary
+#    and define the corresponding row indices. From the dictionary:
+#       0: ['diagnosis: Healthy', "diagnosis: Parkinson's disease"]
+#       1: ['tissue: blood']
+#    Only row 0 provides a meaningful variable for the trait (Parkinson's disease vs. Healthy).
+#    Age and gender data are not present, so we set them to None.
+
+trait_row = 0
+age_row = None
+gender_row = None
+
+# 2.2) Define conversion functions for each variable.
+#      The trait is considered a binary variable (0 = Healthy, 1 = Parkinson's disease).
+#      For unknown or unexpected values, return None.
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    if len(parts) == 2:
+        val = parts[1].strip().lower()
+        if val == "healthy":
+            return 0
+        elif val == "parkinson's disease":
+            return 1
+    return None
+
+# For age and gender, we have None for the row indices.
+# Define placeholder converters (they won't be used if the row index is None).
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3) Conduct initial filtering of dataset usability and save metadata.
+#    We check if the trait data is available (i.e., trait_row is not 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) If trait_row is not None, extract clinical features and save the output.
+#    Assume a variable `clinical_data` contains the relevant sample characteristics DataFrame.
+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 = preview_df(selected_clinical_df)
+    print(preview)  # For inspection; remove or comment out if not needed.
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the identifiers, they appear to be Affymetrix microarray probe set IDs, 
+# which are not standard human gene symbols and therefore require gene mapping.
+
+print("""
+These IDs (e.g., '1007_s_at', '1053_at', etc.) are Affymetrix probe set identifiers 
+and do not directly correspond to standard human gene symbols. 
+They should be mapped to official gene symbols.
+
+requires_gene_mapping = True
+""".strip())
+# 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. From our earlier observations, the 'ID' column of the gene_annotation dataframe
+#    corresponds to the Affymetrix probe identifiers that match those in our gene_data index.
+#    The 'Gene Symbol' column in the gene_annotation holds the actual gene symbols.
+
+# 2. Create a gene mapping dataframe from the gene_annotation, specifying
+#    the probe ID column as 'ID' and the gene symbol column as 'Gene Symbol'.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',
+    gene_col='Gene Symbol'
+)
+
+# 3. Convert probe-level measurements to gene-level expression by applying the mapping.
+#    Each probe's expression is evenly distributed among mapped genes, then summed.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For inspection, one could optionally print the top rows of the resulting gene_data:
+#print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/GSE80599.py

@@ -0,0 +1,208 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+cohort = "GSE80599"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Parkinsons_Disease"
+in_cohort_dir = "../DATA/GEO/Parkinsons_Disease/GSE80599"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/GSE80599.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/GSE80599.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/GSE80599.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/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. Determine if gene expression data is available
+is_gene_available = True  # Based on platform information (Affymetrix HG-U219), it contains gene expression data
+
+# 2.1 Identify data availability for trait, age, and gender
+#    The entire cohort has Parkinson's Disease, so there's no variation for the 'trait' variable.
+trait_row = None  # No key provides variation for presence/absence of Parkinson's Disease
+
+#    Row 4 in the sample characteristics stores multiple 'age at examination' values
+age_row = 4
+
+#    Row 1 in the sample characteristics stores 'gender: Male' or 'gender: Female'
+gender_row = 1
+
+# 2.2 Define conversion functions
+def convert_trait(value: str):
+    # Not used here because trait_row is None
+    return None
+
+def convert_age(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw = parts[1].strip()
+    try:
+        return float(raw)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw = parts[1].strip().lower()
+    if raw == 'male':
+        return 1
+    elif raw == 'female':
+        return 0
+    return None
+
+# 3. Save metadata with 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. Since trait_row is None, skip clinical feature extraction
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the given identifiers, these are Affymetrix probe set IDs rather than standard gene symbols.
+# Therefore, they likely require mapping 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene annotation that correspond to probe identifiers and gene symbols
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Create a mapping dataframe specifying which probe maps to which gene symbol
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final 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=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Parkinsons_Disease/code/TCGA.py

@@ -0,0 +1,73 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Parkinsons_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Parkinsons_Disease/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Parkinsons_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Parkinsons_Disease/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Parkinsons_Disease/cohort_info.json"
+
+import os
+import pandas as pd
+
+# List of subdirectories provided in the instructions:
+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)'
+]
+
+# Synonyms for "Parkinsons_Disease"
+parkinsons_synonyms = ["parkinson", "parkinson's", "parkinsons"]
+
+selected_subdirectory = None
+for subdir in subdirectories:
+    # Skip non-directory markers
+    if subdir.lower() in ['crawldata.ipynb', '.ds_store']:
+        continue
+    subdir_lower = subdir.lower()
+    if any(syn in subdir_lower for syn in parkinsons_synonyms):
+        selected_subdirectory = subdir
+        break
+
+if not selected_subdirectory:
+    # If no matching directory is found, mark dataset as unavailable
+    is_final = False
+    is_gene_available = False
+    is_trait_available = False
+    _ = validate_and_save_cohort_info(
+        is_final=is_final,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+    print(f"No suitable directory found for '{trait}'. Skipped this trait.")
+else:
+    # Step 2: Identify clinicalMatrix file and PANCAN file
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as dataframes
+    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')
+
+    # Step 4: Print the column names of the clinical data
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))