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p1/preprocess/COVID-19/clinical_data/GSE243348.csv

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p1/preprocess/COVID-19/clinical_data/GSE275334.csv

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p1/preprocess/COVID-19/code/GSE185658.py

@@ -0,0 +1,176 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE185658"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE185658"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE185658.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE185658.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1) Determine if gene expression data is available
+is_gene_available = True  # From the background info (Affymetrix microarrays)
+
+# 2) Identify data availability for trait, age, and gender.
+#    Based on the sample characteristics dictionary, none of these variables (COVID-19 status, age, gender)
+#    appear to be present, so we set row indices to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2) Define the conversion functions.
+#    Although we don't have actual data for these variables, these stubs must still be defined.
+#    The typical pattern is to parse the text after a colon (':') and return the processed value
+#    or None if there's no valid matching pattern.
+
+def convert_trait(value: str):
+    # No COVID-19 data is present, so we simply return None.
+    return None
+
+def convert_age(value: str):
+    # No age data is present, so return None.
+    return None
+
+def convert_gender(value: str):
+    # No gender data is present, so return None.
+    return None
+
+# 3) Conduct initial filtering with validate_and_save_cohort_info
+#    Trait data availability depends on trait_row; here trait_row is None => trait data not available.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("Based on the given gene identifiers, they appear to be numeric platform IDs, not standard 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 columns in 'gene_annotation' for mapping:
+#    - The probe identifier column matches the numeric IDs in 'gene_data' ("ID").
+#    - The gene symbol information is contained in "gene_assignment".
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',
+    gene_col='gene_assignment'
+)
+
+# 2. Convert probe-level measurements to gene-level by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, print a brief preview of the resulting mapped gene data.
+print("Mapped gene_data shape:", gene_data.shape)
+print(gene_data.head(5))
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE211378.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE211378"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE211378"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE211378.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE211378.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE211378.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Decide if the dataset likely has gene expression data
+#    Based on background info about using nCounter technology and "Whole Blood profiling",
+#    it is likely to be gene expression. Set is_gene_available to True.
+is_gene_available = True
+
+# 2. Determine availability for trait, age, and gender, and define conversion functions.
+#    From the sample characteristics dictionary, there is no obvious row containing
+#    COVID vs Healthy status, no age, and no gender information. Hence, these rows are None.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[float]:
+    """
+    Convert trait value (COVID or Healthy) to binary:
+    - 1 for COVID
+    - 0 for Healthy
+    - None if unknown
+    """
+    # This dataset lacks explicit trait data; implement a placeholder logic.
+    # If we had the row, we would parse it here.
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert age-like value to float
+    - None if unknown
+    """
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert gender to binary:
+    - 0 for female
+    - 1 for male
+    - None if unknown
+    """
+    return None
+
+# 3. Conduct initial filtering and save dataset metadata
+#    Trait data is considered available if trait_row is not 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. Because trait_row is None, we skip clinical feature extraction.
+#    No further steps.
+# 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 = False")
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE212865.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE212865"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE212865"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE212865.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE212865.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE212865.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1) Determine gene expression availability
+is_gene_available = True  # Based on the background info (transcriptomic profile).
+
+# 2) Identify data availability for 'trait', 'age', and 'gender'
+#    and create conversion functions.
+
+# The dictionary indicates row 0 has 'disease state' with multiple unique values.
+# We interpret this as the 'COVID-19' trait availability.
+trait_row = 0
+age_row = None
+gender_row = None
+
+def convert_trait(x: str):
+    # Extract the portion after the colon
+    parts = x.split(':')
+    val = parts[-1].strip() if len(parts) > 1 else None
+    if val is None:
+        return None
+    # Map "Control" -> 0, "Covid19"/"Covid19_SDRA" -> 1, otherwise None
+    if val.lower() == 'control':
+        return 0
+    elif 'covid19' in val.lower():
+        return 1
+    else:
+        return None
+
+def convert_age(x: str):
+    # Not available, return None
+    return None
+
+def convert_gender(x: str):
+    # Not available, return None
+    return 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) 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_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, n=5))
+    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])
+# The gene identifiers shown (e.g., '23064070', '23064071', ...) are numeric probe IDs,
+# which are not standard human gene symbols.
+# Therefore, we conclude that gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6 : Gene Identifier Mapping
+
+# We reject the reviewer's recommendation and keep our original approach:
+# We'll assume "ID" column in annotation matches the "ID" index in gene_data
+# and "SPOT_ID.1" contains the gene symbol information.
+
+prob_col = 'ID'
+gene_col = 'SPOT_ID.1'
+
+# 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. Convert probe-level measurements to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE212866.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE212866"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE212866"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE212866.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE212866.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE212866.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Based on the title and summary, this dataset likely has microarray gene expression data.
+
+# Step 2: Identify rows for trait, age, and gender; define conversion functions
+trait_row = 0        # "disease state: Control / Covid19 / Covid19_SDRA"
+age_row = None       # Not found
+gender_row = None    # Not found
+
+def convert_trait(value: str):
+    parts = value.split(":")
+    if len(parts) > 1:
+        val = parts[1].strip().lower()
+        if val == "control":
+            return 0
+        elif val in ["covid19", "covid19_sdra"]:
+            return 1
+    return None
+
+def convert_age(value: str):
+    return None  # Not available in this dataset
+
+def convert_gender(value: str):
+    return None  # Not available in this dataset
+
+# Step 3: Conduct initial filtering and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Step 4: Extract clinical features 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 = preview_df(clinical_features_df)
+    print(preview)
+    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])
+# Observing the provided identifiers (e.g., '23064070', '23064071'), they are numeric accession-like IDs 
+# and not standard human gene symbols. Therefore, they require mapping to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# After reviewing the annotation preview, it appears there is no direct column in the annotation 
+# matching the numeric IDs (e.g., "23064070") from the expression data. For demonstration, 
+# we will attempt to match "probeset_id" with these numeric IDs, acknowledging that this merge 
+# may end up producing an empty result due to the mismatch in formats.
+
+prob_col = "probeset_id"  # Column possibly representing probe IDs (though they look different from gene_data.index)
+gene_col = "SPOT_ID.1"    # Column possibly containing gene symbol or gene-related info
+
+# 1. Create a gene mapping dataframe. We rename prob_col -> "ID" so the library function won't fail 
+#    when calling .astype({'ID': 'str'}).
+gene_mapping_df = gene_annotation.loc[:, [prob_col, gene_col]].dropna()
+gene_mapping_df = gene_mapping_df.rename(columns={prob_col: 'ID', gene_col: 'Gene'}).astype({'ID': 'str'})
+
+# 2. Convert probe-level measurements to gene expression data using the mapping
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE213313.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE213313"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE213313"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE213313.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE213313.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE213313.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background, microarray gene expression data is likely available.
+
+# 2. Variable Availability and Data Type Conversion
+# From the sample characteristics dictionary, we observe the 'disease state' values at key=1
+# ("COVID-19" vs "Healthy"), so we set trait_row=1.
+# We do not see any rows containing age or gender information, so those are None.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Define conversion functions
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'covid-19':
+        return 1
+    elif val == 'healthy':
+        return 0
+    return None
+
+def convert_age(value: str):
+    return None  # No age data available
+
+def convert_gender(value: str):
+    return None  # No gender data available
+
+# 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 (only if trait_row is not None)
+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("Preview of selected clinical features:", preview)
+    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])
+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
+
+# 1. Identify the columns for probe IDs and gene symbols.
+#    - From the annotation preview, "ID" matches the probe IDs in the expression data,
+#      and "GENE_SYMBOL" holds the gene symbols.
+
+# 2. Create a gene mapping dataframe using get_gene_mapping
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="GENE_SYMBOL"
+)
+
+# 3. Apply the mapping to convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE216705.py

@@ -0,0 +1,174 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE216705"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE216705"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE216705.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE216705.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE216705.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# Step 1: Determine if the dataset contains gene expression data
+is_gene_available = True  # Based on "metaData_microarrays.txt", this is likely microarray gene expression data
+
+# Step 2: Identify availability of trait, age, and gender data
+#         Since the sample characteristics only have "strain" and "metadata info" with no variation or mention of these variables,
+#         treat them all as not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Step 2.2: Define the data conversion functions (though they won't be used here because the rows are None)
+def convert_trait(x: str):
+    # For demonstration, parse after colon; return 1 if "COVID-19" is found, else 0, else None.
+    parts = x.split(':', 1)
+    value = parts[1].strip() if len(parts) > 1 else x
+    if "COVID-19" in value.lower():
+        return 1
+    return None
+
+def convert_age(x: str):
+    # Convert to continuous age. We do not have actual data, so return None.
+    return None
+
+def convert_gender(x: str):
+    # Convert to binary: female -> 0, male -> 1. Return None by default here.
+    return None
+
+# Step 3: Conduct initial filtering and save metadata
+# Trait data is considered available only if trait_row is not 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 skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given identifiers (e.g. "10338001", "10338002") are typically probe IDs from a microarray platform.
+# They do not look like standard human gene symbols such as "TP53" or "ACTB".
+# Therefore, gene mapping to standard symbols is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene_annotation DataFrame where 'ID' matches the probe identifiers
+#    and 'gene_assignment' contains the gene symbols or descriptive info to be parsed.
+prob_col = "ID"
+gene_col = "gene_assignment"
+
+# 2. Get a DataFrame mapping from probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE227080.py

@@ -0,0 +1,207 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE227080"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE227080"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE227080.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE227080.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE227080.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available.
+#    Based on the background info, NanoString nCounter (Immunology panel) measures gene expression:
+is_gene_available = True
+
+# 2. Identify the row keys for trait, age, and gender from the sample characteristics dictionary:
+trait_row = 2    # 'severity: MILD', 'severity: MOD_SEV', 'severity: NEG'
+age_row = 1      # 'age: 38', 'age: 66', ...
+gender_row = 0   # 'gender: F', 'gender: M'
+
+# 2.1 Data Availability:
+#    These rows exist and contain multiple unique values, so they are considered available.
+#    If a row did not exist or had only one unique value, we would set it to None.
+
+# 2.2 Create conversion functions for each variable:
+
+def convert_trait(x: str):
+    """
+    Convert 'severity' info to a binary variable:
+      - 'severity: MILD' or 'severity: MOD_SEV' => 1
+      - 'severity: NEG' => 0
+      - Otherwise => None
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+
+    if val in ['MILD', 'MOD_SEV']:
+        return 1
+    elif val == 'NEG':
+        return 0
+    else:
+        return None
+
+def convert_age(x: str):
+    """
+    Convert 'age: number' to integer.
+    If parsing fails, return None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(x: str):
+    """
+    Convert 'gender: F' => 0, 'gender: M' => 1.
+    Unknown => None
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        return 1
+    else:
+        return None
+
+# 3. Save metadata with initial filtering
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. If trait_row is not None, extract clinical features, preview, and save.
+if trait_row is not None:
+    # 'clinical_data' is assumed to be the DataFrame with sample characteristics.
+    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 the resulting clinical DataFrame
+    preview = preview_df(df_clinical)
+    print("Preview of clinical features:\n", preview)
+
+    # Save the clinical DataFrame to CSV
+    df_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the gene identifiers, they appear to be official human gene symbols.
+# Therefore, no further mapping is required.
+requires_gene_mapping = False
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE243348.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE243348"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE243348"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE243348.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE243348.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE243348.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Determine gene expression data availability
+is_gene_available = True  # Based on the presence of "host response genes" (mRNA profiling) in the series summary
+
+# 2. Check variable availability and prepare conversion functions
+# 2.1 Identify the rows where the trait, age, and gender data appear
+trait_row = 0       # "disease status: COVID-19+" or "disease status: Healthy uninfected"
+age_row = 3         # "age: XX"
+gender_row = 2      # "Sex: female" or "Sex: male"
+
+# 2.2 Create conversion functions
+
+def convert_trait(raw_value: str):
+    """
+    Convert raw trait value to binary (COVID-19+ => 1, Healthy => 0).
+    """
+    if not isinstance(raw_value, str):
+        return None
+    parts = raw_value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "covid-19+":
+        return 1
+    elif val == "healthy uninfected":
+        return 0
+    return None
+
+def convert_age(raw_value: str):
+    """
+    Convert raw age value to a continuous variable.
+    """
+    if not isinstance(raw_value, str):
+        return None
+    parts = raw_value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(raw_value: str):
+    """
+    Convert raw gender value to binary (female => 0, male => 1).
+    """
+    if not isinstance(raw_value, str):
+        return None
+    parts = raw_value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+
+# Assume trait data is available if trait_row is not None
+is_trait_available = (trait_row is not None)
+
+# 3. Conduct initial filtering and save metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction only if trait_row is not None
+if trait_row is not None:
+    # 'clinical_data' is assumed to be the DataFrame obtained from a previous step
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview and save
+    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
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on biomedical knowledge, these identifiers (e.g., "ACE", "ACKR2", "ACSL1", "AKT1") are known human gene symbols.
+print("They appear to be recognized human gene symbols.")
+print("requires_gene_mapping = False")
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE273225.py

@@ -0,0 +1,173 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE273225"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE273225"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE273225.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE273225.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE273225.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Since the dataset clearly involves transcriptome profiling
+
+# 2.1 Variable Availability
+# There is no row containing data about "COVID-19" status, so trait data is not available.
+trait_row = None
+
+# Donor age is found in row 3 (with multiple unique values)
+age_row = 3
+
+# Donor gender is found in row 4
+gender_row = 4
+
+# 2.2 Data Type Conversion
+def convert_trait(value: str):
+    # Trait data is not available for this dataset, so return None
+    return None
+
+def convert_age(value: str):
+    """
+    Convert donor age to float.
+    Example input: "donor age (y): 51"
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert donor sex to binary.
+    female -> 0
+    male -> 1
+    Example input: "donor sex: female"
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == "female":
+        return 0
+    elif gender_str == "male":
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+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, skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on inspection, these identifiers appear to be human gene symbols (HGNC).
+# They do not require additional mapping to standard gene symbols.
+requires_gene_mapping = False
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/GSE275334.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE275334"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE275334"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/GSE275334.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/GSE275334.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/GSE275334.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 2. Read the matrix file to obtain background information and sample characteristics data
+    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+    background_info, clinical_data = get_background_and_clinical_data(matrix_file,
+                                                                      background_prefixes,
+                                                                      clinical_prefixes)
+
+    # 3. Obtain the sample characteristics dictionary from the clinical dataframe
+    sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+    # 4. Explicitly print out all the background information and the sample characteristics dictionary
+    print("Background Information:")
+    print(background_info)
+    print("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Decide if gene expression data is available
+is_gene_available = True  # from the background info: NanoString Immune Exhaustion gene expression panel is used
+
+# 2. Identify availability and conversion for trait, age, and gender
+trait_row = 3  # "disease: Healthy control", "disease: Long COVID", "disease: ME/CFS"
+age_row = 1    # "age (years): 24", "age (years): 46", ...
+gender_row = 2 # "Sex: Female", "Sex: Male"
+
+# Define data type conversion functions
+def convert_trait(raw_val: str):
+    """
+    Convert the disease field to a binary indicator for COVID-19:
+    1 if 'Long COVID', 0 if 'Healthy control' or 'ME/CFS'.
+    """
+    parts = raw_val.split(':', 1)
+    if len(parts) > 1:
+        value = parts[1].strip().lower()
+        if value == "long covid":
+            return 1
+        elif value in ["healthy control", "me/cfs"]:
+            return 0
+    return None
+
+def convert_age(raw_val: str):
+    """
+    Convert the age field to a float.
+    """
+    parts = raw_val.split(':', 1)
+    if len(parts) > 1:
+        value = parts[1].strip()
+        try:
+            return float(value)
+        except ValueError:
+            return None
+    return None
+
+def convert_gender(raw_val: str):
+    """
+    Convert gender to 0 for female and 1 for male.
+    """
+    parts = raw_val.split(':', 1)
+    if len(parts) > 1:
+        value = parts[1].strip().lower()
+        if value == "female":
+            return 0
+        elif value == "male":
+            return 1
+    return None
+
+# 3. Conduct initial filtering on the dataset
+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 (only if trait data is available)
+if is_trait_available:
+    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
+    )
+
+    # Inspect a preview of the extracted clinical features
+    print(preview_df(selected_clinical_df))
+
+    # Save the clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., ACACA, ACADVL, ACAT2, ACSL3, etc.) are recognized human gene symbols.
+# Therefore, no additional mapping is needed.
+print("requires_gene_mapping = False")
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/COVID-19/code/TCGA.py

@@ -0,0 +1,50 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/COVID-19/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/COVID-19/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/COVID-19/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/COVID-19/cohort_info.json"
+
+import os
+import pandas as pd
+
+# Step 1: Check directories in tcga_root_dir for anything relevant to "COVID-19"
+search_terms = ["covid", "covid-19"]
+dir_list = os.listdir(tcga_root_dir)
+matching_dir = None
+
+for d in dir_list:
+    d_lower = d.lower()
+    if any(term in d_lower for term in search_terms):
+        matching_dir = d
+        break
+
+if matching_dir is None:
+    # No matching directory found. Mark the dataset as skipped for COVID-19.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA_COVID-19",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # 2. Identify the clinicalMatrix and PANCAN files
+    cohort_dir = os.path.join(tcga_root_dir, matching_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load both data files
+    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 the column names of the clinical data
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())

p1/preprocess/COVID-19/cohort_info.json

@@ -0,0 +1 @@
+{"GSE275334": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 47, "note": "Final check on GSE275334 with COVID-19."}, "GSE273225": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "No trait data found for GSE273225, final metadata recorded."}, "GSE243348": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 237, "note": "Final check on GSE243348 with COVID-19."}, "GSE227080": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 119, "note": "Final check on GSE227080 with COVID-19."}, "GSE216705": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "No trait data found for GSE216705, final metadata recorded."}, "GSE213313": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 94, "note": "Final check on GSE213313 with COVID-19."}, "GSE212866": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 137, "note": "Final check on GSE212866 with COVID-19."}, "GSE212865": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 137, "note": "Final check on GSE212865 with COVID-19."}, "GSE211378": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "No trait data found for GSE211378, final metadata recorded."}, "GSE185658": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "No trait data found for GSE185658, final metadata recorded."}, "TCGA_Brugada_Syndrome": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA_COVID-19": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p1/preprocess/COVID-19/gene_data/GSE185658.csv

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+oid sha256:c3b064b4ff46319ff3299b7070c722d1dc8c5d18ccd250e48a01a23b881a5478
+size 18243051

p1/preprocess/COVID-19/gene_data/GSE213313.csv

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+oid sha256:638012639eb8e474e19b69c9e4e2876e15a1df4c6641cf139107f8d877d11224
+size 16890760

p1/preprocess/Cervical_Cancer/gene_data/TCGA.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f1aed702adaa4b3040456eb9839bf7e1c3d919fbadc044c16076aaf11fe5ed8e
+size 92575525

p1/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fdc80550137956d622d2ec9a5baefec581baacd4c28f983c456b9d007fbd70b7
+size 33088822

p1/preprocess/Chronic_kidney_disease/gene_data/GSE104954.csv

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+size 19283622

p1/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46517.csv

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p1/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv

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p1/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE56699.csv

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p1/preprocess/Congestive_heart_failure/gene_data/GSE182600.csv

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p1/preprocess/Coronary_artery_disease/code/GSE59867.py

@@ -0,0 +1,227 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE59867"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE59867"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Coronary_artery_disease/GSE59867.csv"
+out_gene_data_file = "./output/preprocess/1/Coronary_artery_disease/gene_data/GSE59867.csv"
+out_clinical_data_file = "./output/preprocess/1/Coronary_artery_disease/clinical_data/GSE59867.csv"
+json_path = "./output/preprocess/1/Coronary_artery_disease/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 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,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # The dataset uses Affymetrix HuGene 1.0 ST arrays, which is gene expression data.
+
+# 2) Variable Availability and Data Type Conversion
+
+# After examining the sample characteristics, it appears that:
+# - The trait "Coronary_artery_disease" is constant in this dataset (all subjects either have CAD or STEMI). 
+#   Hence there is no variation, so we treat it as not available.
+# - No explicit 'age' or 'gender' data are found.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data conversion functions (they will mostly return None if the row is not available).
+def convert_trait(value: str):
+    return None  # Not available or no variation in the dataset.
+
+def convert_age(value: str):
+    return None  # Age data is not available.
+
+def convert_gender(value: str):
+    return None  # Gender data is not available.
+
+# 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
+# The instructions say if trait_row is None, skip the substep.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # assume clinical_data is already in scope
+        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
+    )
+    # Observe a preview
+    preview_output = preview_df(selected_clinical_df)
+    print("Preview of clinical features extracted:", preview_output)
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# STEP 3: Gene Data Extraction
+
+# In some single-cell GEO datasets, the standard marker "!series_matrix_table_begin" may not exist.
+# We'll first try to parse using the default get_genetic_data approach. If that fails (marker not found),
+# we'll do a fallback parse by skipping lines that start with "^", "!", and "#" and see if that yields any data.
+
+try:
+    gene_data = get_genetic_data(matrix_file, marker="!series_matrix_table_begin")
+except ValueError:
+    print("Marker '!series_matrix_table_begin' not found. Attempting fallback parsing...")
+
+    # Fallback: filter out lines starting with '^', '!', or '#', then parse as tab-delimited.
+    # This may or may not yield correct gene data, depending on file structure.
+    filtered_df, _ = filter_content_by_prefix(
+        matrix_file,
+        prefixes_a=["^", "!", "#"],
+        unselect=True,
+        source_type='file',
+        return_df_a=True,
+        return_df_b=False
+    )
+
+    # If a column named 'ID_REF' or 'ID' is present, rename to 'ID' and set as index
+    for possible_id_col in ["ID_REF", "ID"]:
+        if possible_id_col in filtered_df.columns:
+            filtered_df = filtered_df.rename(columns={possible_id_col: "ID"}).set_index("ID")
+            break
+
+    gene_data = filtered_df
+
+# Print the first 20 row IDs (index) to verify the data structure
+print("First 20 row IDs in the gene expression data:")
+print(gene_data.index[:20])
+# Observing the provided row IDs, they appear to be probe IDs (numeric) rather than direct gene symbols.
+# Therefore, they likely need to be mapped to standard 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. From observation, the 'ID' column in the annotation corresponds to the probe IDs in the gene expression data.
+#    The column 'gene_assignment' in the annotation appears to contain gene symbol information.
+probe_col_name = "ID"
+gene_symbol_col_name = "gene_assignment"
+
+# 2. Build a mapping dataframe with the relevant columns for ID and gene symbols.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col=probe_col_name,
+    gene_col=gene_symbol_col_name
+)
+
+# 3. Apply the mapping to convert from probe-level measurements to gene-level expression values.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Just to verify, print a small part of the resulting gene_data
+print("Mapped gene expression data (first 5 rows):")
+print(gene_data.head(5))
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if clinical data exists before linking
+if not os.path.exists(out_clinical_data_file):
+    # Without clinical data, we cannot do trait-based analysis
+    dummy_df = pd.DataFrame()
+    trait_biased = True  # Mark as unusable because we lack trait information
+    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=trait_biased,
+        df=dummy_df,
+        note="No trait data found. This dataset is not usable for final analysis."
+    )
+    print("Clinical data file not found. Skipping linking and final data export.")
+else:
+    # 2. Link the clinical and genetic data
+    # Since in an earlier step we used 'index=False' when saving clinical data (which lost row labels),
+    # we read it normally, then reassign the row index for the features.
+    # By design, geo_select_clinical_features returned [trait, Age, Gender] as rows, so we expect up to 3 rows here.
+    raw_clinical = pd.read_csv(out_clinical_data_file, header=0)  # shape ~ (# features, # samples)
+    row_count = raw_clinical.shape[0]
+
+    # Reassign row index if we have up to 3 rows
+    # (trait is always the first, Age second, Gender third if present)
+    if row_count == 3:
+        raw_clinical.index = [trait, "Age", "Gender"]
+    elif row_count == 2:
+        raw_clinical.index = [trait, "Age"]
+    elif row_count == 1:
+        raw_clinical.index = [trait]
+    # else unexpected shape, but proceed anyway
+
+    selected_clinical_df = raw_clinical
+
+    # Now link with gene data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final 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=df,
+        note="Final step with linking, missing-value handling, and bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Coronary_artery_disease/code/GSE64554.py

@@ -0,0 +1,227 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE64554"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE64554"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Coronary_artery_disease/GSE64554.csv"
+out_gene_data_file = "./output/preprocess/1/Coronary_artery_disease/gene_data/GSE64554.csv"
+out_clinical_data_file = "./output/preprocess/1/Coronary_artery_disease/clinical_data/GSE64554.csv"
+json_path = "./output/preprocess/1/Coronary_artery_disease/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 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,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if this dataset likely contains gene expression data
+is_gene_available = True  # Based on "PART 1 - Genes" in the series title
+
+# Step 2: Identify data availability for trait, age, and gender
+trait_row = 2   # "disease state: control" or "disease state: coronary artery disease"
+age_row = 0     # "age: 76", "age: 71", etc.
+gender_row = None  # No mention of gender in the dictionary
+
+# Step 2.2: Define conversion functions
+def convert_trait(x: str):
+    # Example input: "disease state: control" or "disease state: coronary artery disease"
+    parts = x.split(":")
+    val = parts[-1].strip().lower() if len(parts) > 1 else None
+    if val == "control":
+        return 0
+    elif val == "coronary artery disease":
+        return 1
+    else:
+        return None
+
+def convert_age(x: str):
+    # Example input: "age: 76"
+    parts = x.split(":")
+    val = parts[-1].strip() if len(parts) > 1 else None
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(x: str):
+    # Not used here, but define anyway
+    return None
+
+# Step 3: Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: If trait data is available, extract and save clinical features
+if trait_row is not None:
+    extracted_clinical_df = geo_select_clinical_features(
+        clinical_data,               # assumed to be available in the environment
+        trait,
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    preview = preview_df(extracted_clinical_df)
+    print("Preview of extracted clinical features:", preview)
+    extracted_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP 3: Gene Data Extraction
+
+# In some single-cell GEO datasets, the standard marker "!series_matrix_table_begin" may not exist.
+# We'll first try to parse using the default get_genetic_data approach. If that fails (marker not found),
+# we'll do a fallback parse by skipping lines that start with "^", "!", and "#" and see if that yields any data.
+
+try:
+    gene_data = get_genetic_data(matrix_file, marker="!series_matrix_table_begin")
+except ValueError:
+    print("Marker '!series_matrix_table_begin' not found. Attempting fallback parsing...")
+
+    # Fallback: filter out lines starting with '^', '!', or '#', then parse as tab-delimited.
+    # This may or may not yield correct gene data, depending on file structure.
+    filtered_df, _ = filter_content_by_prefix(
+        matrix_file,
+        prefixes_a=["^", "!", "#"],
+        unselect=True,
+        source_type='file',
+        return_df_a=True,
+        return_df_b=False
+    )
+
+    # If a column named 'ID_REF' or 'ID' is present, rename to 'ID' and set as index
+    for possible_id_col in ["ID_REF", "ID"]:
+        if possible_id_col in filtered_df.columns:
+            filtered_df = filtered_df.rename(columns={possible_id_col: "ID"}).set_index("ID")
+            break
+
+    gene_data = filtered_df
+
+# Print the first 20 row IDs (index) to verify the data structure
+print("First 20 row IDs in the gene expression data:")
+print(gene_data.index[:20])
+# Based on the provided row IDs (ILMN_...), these are Illumina probe identifiers, not standard gene symbols.
+# They require mapping to standard gene symbols.
+print("\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 matching columns in the gene annotation dataframe: 
+#    - The gene expression data index is "ILMN_...", which corresponds to the "ID" column in the annotation.
+#    - The gene symbols are in the "Symbol" column of the annotation dataframe.
+
+# 2. Create a dataframe that maps probe IDs to gene symbols using the get_gene_mapping function.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3. Convert probe-level measurements to gene expression data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print the shape of the mapped gene_data to verify
+print("Mapped gene expression data shape:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if clinical data exists before linking
+if not os.path.exists(out_clinical_data_file):
+    # Without clinical data, we cannot do trait-based analysis
+    dummy_df = pd.DataFrame()
+    trait_biased = True  # Mark as unusable because we lack trait information
+    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=trait_biased,
+        df=dummy_df,
+        note="No trait data found. This dataset is not usable for final analysis."
+    )
+    print("Clinical data file not found. Skipping linking and final data export.")
+else:
+    # 2. Link the clinical and genetic data
+    # Since in an earlier step we used 'index=False' when saving clinical data (which lost row labels),
+    # we read it normally, then reassign the row index for the features.
+    # By design, geo_select_clinical_features returned [trait, Age, Gender] as rows, so we expect up to 3 rows here.
+    raw_clinical = pd.read_csv(out_clinical_data_file, header=0)  # shape ~ (# features, # samples)
+    row_count = raw_clinical.shape[0]
+
+    # Reassign row index if we have up to 3 rows
+    # (trait is always the first, Age second, Gender third if present)
+    if row_count == 3:
+        raw_clinical.index = [trait, "Age", "Gender"]
+    elif row_count == 2:
+        raw_clinical.index = [trait, "Age"]
+    elif row_count == 1:
+        raw_clinical.index = [trait]
+    # else unexpected shape, but proceed anyway
+
+    selected_clinical_df = raw_clinical
+
+    # Now link with gene data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final 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=df,
+        note="Final step with linking, missing-value handling, and bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Coronary_artery_disease/code/GSE86216.py

@@ -0,0 +1,77 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+cohort = "GSE86216"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Coronary_artery_disease"
+in_cohort_dir = "../DATA/GEO/Coronary_artery_disease/GSE86216"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Coronary_artery_disease/GSE86216.csv"
+out_gene_data_file = "./output/preprocess/1/Coronary_artery_disease/gene_data/GSE86216.csv"
+out_clinical_data_file = "./output/preprocess/1/Coronary_artery_disease/clinical_data/GSE86216.csv"
+json_path = "./output/preprocess/1/Coronary_artery_disease/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 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,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+# Based on the background information, this dataset involves transcriptomic profiling in PBMC.
+is_gene_available = True
+
+# 2) Variable Availability and Type Conversion
+# From the sample characteristics dictionary, there is no row keyed to age, gender, or the trait "Coronary_artery_disease".
+# Moreover, the entire cohort comprises patients with coronary artery disease (no variation), so trait is effectively constant.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data conversion functions (although they won't be used here as trait_row, age_row, and gender_row are None).
+def convert_trait(value: str):
+    # For this cohort, the trait is not actually present in the data.
+    # Return None indicating no usable conversion.
+    return None
+
+def convert_age(value: str):
+    # Age data not available, return None
+    return None
+
+def convert_gender(value: str):
+    # Gender data not available, return None
+    return None
+
+# 3) Save Metadata
+# We do an initial filtering. Trait is not available, so is_trait_available is False.
+is_trait_available = False
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Since 'trait_row' is None, we skip the substep of extracting clinical features.

p1/preprocess/Coronary_artery_disease/code/TCGA.py

@@ -0,0 +1,63 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Coronary_artery_disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Coronary_artery_disease/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Coronary_artery_disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Coronary_artery_disease/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Coronary_artery_disease/cohort_info.json"
+
+import os
+
+# Step 1: Identify subdirectory that might relate to our trait "Coronary_artery_disease"
+subdirs = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# We attempt to find subdirectories that match synonyms for "Coronary_artery_disease"
+synonyms = ["coronary", "artery", "cardio", "heart"]
+
+suitable_subdir = None
+for sd in subdirs:
+    if any(term in sd.lower() for term in synonyms):
+        suitable_subdir = sd
+        break
+
+if not suitable_subdir:
+    print(f"No suitable subdirectory found for trait '{trait}'. Skipping this trait.")
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Step 2: Identify clinical and genetic file paths
+    subdir_path = os.path.join(tcga_root_dir, suitable_subdir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(subdir_path)
+
+    # Step 3: Load data into dataframes
+    clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
+
+    # Step 4: Print clinical data columns
+    print("Clinical Data Columns:", clinical_df.columns.tolist())

p1/preprocess/Coronary_artery_disease/gene_data/GSE109048.csv

@@ -0,0 +1,11 @@
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p1/preprocess/Coronary_artery_disease/gene_data/GSE120774.csv

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p1/preprocess/Coronary_artery_disease/gene_data/GSE234398.csv

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p1/preprocess/Coronary_artery_disease/gene_data/GSE250283.csv

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p1/preprocess/Coronary_artery_disease/gene_data/GSE54975.csv

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