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@@ -1172,3 +1172,25 @@ p1/preprocess/Hypertension/gene_data/GSE117261.csv filter=lfs diff=lfs merge=lfs
 p1/preprocess/Huntingtons_Disease/gene_data/GSE135589.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Hypertension/gene_data/GSE71994.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Hypertension/gene_data/GSE77627.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/gene_data/GSE256539.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/gene_data/GSE71220.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Head_and_Neck_Cancer/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE84046.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Glioblastoma/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/GSE112943.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Glioblastoma/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hemochromatosis/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Head_and_Neck_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Fibromyalgia/GSE67311.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hemochromatosis/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE32030.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE162635.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Fibromyalgia/gene_data/GSE67311.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Lupus_(Systemic_Lupus_Erythematosus)/gene_data/GSE112943.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertrophic_Cardiomyopathy/GSE36961.csv filter=lfs diff=lfs merge=lfs -text

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635.csv

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p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331.csv

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p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.csv

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p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE32030.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE32030"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE32030"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE32030.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE32030.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE32030.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on background info indicating microarray gene expression data
+
+# 2) Variable Availability
+# Checking the sample characteristics dictionary, 'copd status' only appears as "yes", no variation.
+# No explicit age or gender info is provided in the dictionary. Thus, all are considered unavailable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    # For demonstration, but will not be used because trait_row is None
+    # e.g., return 1 if the string (after the colon) indicates COPD, else 0 or None
+    parts = str(value).split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else ''
+    if val == 'yes':
+        return 1
+    elif val == 'no':
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    # For demonstration, but will not be used because age_row is None
+    parts = str(value).split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else ''
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    # For demonstration, but will not be used because gender_row is None
+    parts = str(value).split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else ''
+    if val in ['m', 'male']:
+        return 1
+    elif val in ['f', 'female']:
+        return 0
+    return None
+
+# 3) Save Metadata with initial filtering
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction - skip since trait_row is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "1007_s_at", "1053_at") are probe set IDs (commonly used by Affymetrix microarray platforms).
+# Therefore, they are not standard human gene symbols and do require mapping.
+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 the columns in gene_annotation corresponding to the same identifiers as in gene_data and the gene symbols.
+#    From the preview, "ID" matches the probe IDs in gene_data, and "Gene Symbol" holds the gene symbols.
+
+# 2. Get the gene mapping dataframe using the 'get_gene_mapping' function.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",          # Probe ID column
+    gene_col="Gene Symbol"  # Gene symbol column
+)
+
+# 3. Convert probe-level measurements to gene-level expression data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, let's print the shape of the mapped gene_data and a small slice.
+print("Mapped gene_data shape:", gene_data.shape)
+print(gene_data.iloc[:5, :5])
+# STEP7
+
+# In a previous step, we determined that trait data is not available, which means
+# is_trait_available = False. Hence, clinical feature extraction was skipped
+# and "selected_clinical_df" was never created.
+
+# 1) Normalize gene symbols using synonym information from NCBI, then save the result.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Since the trait is unavailable, we skip linking clinical data (no "selected_clinical_df").
+
+# 3), 4), 5) Provide final validation. The library requires 'is_biased' to be a boolean.
+#    Since the dataset has no trait, we set is_biased=False.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=normalized_gene_data,
+    note="Trait data unavailable; only gene expression data was processed."
+)
+
+# 6) Without trait data, we cannot perform further trait-based analysis, so no final linked dataset is saved.

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE64593.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE64593"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64593"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64593.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64593.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64593.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Because the dataset is from an Affymetrix microarray, which typically measures gene expression.
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary:
+# {0: ['smoking status: smoker'],
+#  1: ['disease state: HIV+', 'disease state: HIV-'],
+#  2: ['cell type: alveolar macrophage']}
+# None of the keys mentions COPD explicitly, nor is there age or gender information. 
+# Hence no variation for COPD, age, or gender can be found.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions (though no data is available for actual use):
+def convert_trait(value: str):
+    # Typically we'd parse the string for COPD vs. non-COPD.
+    # This dataset does not provide COPD info; default to None.
+    return None
+
+def convert_age(value: str):
+    # Typically we'd parse the string for numeric age.
+    # No age data is present; default to None.
+    return None
+
+def convert_gender(value: str):
+    # Typically we'd parse the string for 'male' or 'female'.
+    # No gender data is present; default to None.
+    return None
+
+# 3. Save Metadata (initial filtering)
+# Trait data is not available => 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=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Skip because trait_row is None (no trait data available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given identifiers (e.g., "1007_s_at", "1053_at", etc.) represent Affymetrix probe set IDs, 
+# which are not standard human gene symbols and thus require mapping to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1 & 2. From the preview, the gene expression data uses the "ID" column (e.g., "1007_s_at"),
+#    and the corresponding column in gene_annotation that stores gene symbols is "Gene Symbol".
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP7
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Because we determined in earlier steps that there is no trait data (trait_row=None),
+# we have no clinical data to link. Hence, we define an empty DataFrame for clinical data.
+selected_clinical_df = pd.DataFrame()
+
+# We also know there is no trait information, so is_trait_available is False.
+# According to the instructions, if there is no trait data, we cannot proceed with
+# linking, missing-value handling (which depends on trait), or bias checking.
+# Instead, we finalize and record the metadata accordingly.
+
+# 2. (Skip linking clinical and genetic data due to no trait)
+
+# 3. (Skip missing value handling because trait column does not exist)
+
+# 4. (Skip bias checking since there's no trait column)
+
+# 5. Perform final quality validation and save the metadata.
+# Since no trait is available, we must pass a boolean for is_biased. 
+# We'll set is_biased=False because we cannot check bias on nonexistent trait data.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=pd.DataFrame(), 
+    note="No trait data detected; dataset is not suitable for trait association analysis."
+)
+
+# 6. Because no trait data is available, the dataset is not usable, so we do not save linked data.
+if is_usable:
+    # In principle, this should not happen because is_trait_available=False implies is_usable=False
+    print("Unexpectedly marked as usable despite lacking trait data.")

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/TCGA.py

@@ -0,0 +1,113 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify a suitable subdirectory for "Chronic_obstructive_pulmonary_disease_(COPD)"
+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)'
+]
+
+# Looking for "lung" as a proxy for COPD-related data
+candidate_subdirs = [s for s in subdirs if 'lung' in s.lower()]
+
+if not candidate_subdirs:
+    print("No matching subdirectory found for Chronic_obstructive_pulmonary_disease_(COPD). Skipping this trait.")
+else:
+    # Choose the most general lung-related directory if multiple exist
+    # Here, "TCGA_Lung_Cancer_(LUNG)" is the most general match
+    chosen_subdir = "TCGA_Lung_Cancer_(LUNG)"
+    if chosen_subdir not in candidate_subdirs:
+        chosen_subdir = candidate_subdirs[0]  # fallback if not found
+
+    cohort_dir = os.path.join(tcga_root_dir, chosen_subdir)
+
+    # 2. Identify file paths for clinical and genetic data
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load data into Pandas DataFrames
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print the column names of the clinical data
+    print("Clinical Data Columns:", clinical_df.columns.tolist())
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+# Extract and preview the candidate columns
+columns_to_extract = candidate_age_cols + candidate_gender_cols
+df_extracted = clinical_df.loc[:, columns_to_extract]
+preview_result = preview_df(df_extracted, n=5, max_items=200)
+print(preview_result)
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print(f"Chosen age column: {age_col}")
+print(f"Chosen gender column: {gender_col}")
+# 1) Extract and standardize clinical features
+age_col = "age_at_initial_pathologic_diagnosis"  # Updated valid age column
+gender_col = "gender"  # Remains valid
+
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols and save
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3) Link the clinical and genetic data
+linked_data = selected_clinical_df.join(normalized_gene_df, how='inner')
+
+# 4) Handle missing values
+processed_linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Determine whether the trait/demographic features are biased
+is_trait_biased, final_data = judge_and_remove_biased_features(processed_linked_data, trait)
+
+# 6) Conduct final validation
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=final_data,
+    note="Preprocessing complete for Chronic_kidney_disease (TCGA)."
+)
+
+# 7) If usable, save the final linked data and clinical subset
+if is_usable:
+    final_data.to_csv(out_data_file)
+    clinical_cols = [c for c in [trait, "Age", "Gender"] if c in final_data.columns]
+    if clinical_cols:
+        final_data[clinical_cols].to_csv(out_clinical_data_file)

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE162635.csv

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+size 45983176

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv

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+oid sha256:967fcad06150eef5443e19636e1b06db24f74f4d4fd127fa0ccec7133deb4f05
+size 22261392

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv

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p1/preprocess/Fibromyalgia/code/GSE67311.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Fibromyalgia"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Fibromyalgia"
+in_cohort_dir = "../DATA/GEO/Fibromyalgia/GSE67311"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Fibromyalgia/GSE67311.csv"
+out_gene_data_file = "./output/preprocess/1/Fibromyalgia/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/preprocess/1/Fibromyalgia/clinical_data/GSE67311.csv"
+json_path = "./output/preprocess/1/Fibromyalgia/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+is_gene_available = True  # From the background info, this is a gene expression dataset
+
+# 2. Identify row keys for trait, age, and gender, and define conversion functions
+
+# We see that sample characteristics dictionary at row 0 includes:
+# 'diagnosis: fibromyalgia', 'diagnosis: healthy control'.
+# This matches our trait-of-interest (Fibromyalgia) vs. Healthy control cohorts, so:
+trait_row = 0
+
+# There's no apparent age or gender data in the dictionary, so:
+age_row = None
+gender_row = None
+
+# Define trait conversion function (binary: fibromyalgia=1, healthy=0)
+def convert_trait(value: str):
+    if ':' in value:
+        value = value.split(':', 1)[1].strip()
+    val_lower = value.lower()
+    if val_lower == 'fibromyalgia':
+        return 1
+    elif val_lower == 'healthy control':
+        return 0
+    return None
+
+# Age and gender are unavailable, so set the converting functions to None
+convert_age = None
+convert_gender = None
+
+# 3. Save metadata using initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction if trait data is available
+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 and save extracted clinical features
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_result)
+
+    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 given identifiers look like numeric probe IDs rather than standard human gene symbols, 
+# so they likely require mapping to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify which columns in the gene_annotation dataframe match our probe identifiers and gene symbols.
+#    From the preview, the "ID" column in gene_annotation corresponds to the same probe IDs
+#    used in gene_data (both numeric, e.g. '7896736', '7896738'), and "gene_assignment" stores text containing gene symbols.
+
+# 2. Get a gene mapping dataframe: extract the "ID" column (probe ID) and "gene_assignment" column (gene text).
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="gene_assignment"
+)
+
+# 3. Apply the probe-to-gene mapping to convert probe-level data into gene-level expression.
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+print("Gene-level expression data shape:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Try reading the clinical CSV file (trait data). If it's empty or unreadable, treat trait as unavailable.
+if os.path.exists(out_clinical_data_file):
+    try:
+        tmp_df = pd.read_csv(out_clinical_data_file, header=0)
+        # If successfully read, check the number of rows to rename their index properly.
+        row_count = tmp_df.shape[0]
+        if row_count == 1:
+            tmp_df.index = [trait]
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+
+        selected_clinical_df = tmp_df
+
+        # Link the clinical and gene expression data
+        linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+        # 3) Handle missing values
+        final_data = handle_missing_values(linked_data, trait_col=trait)
+
+        # 4) Evaluate bias in the trait (and remove biased demographics if any)
+        trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+        # 5) 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=final_data,
+            note="Trait and gender rows found; no age row."
+        )
+
+        # 6) If the dataset is usable, save
+        if is_usable:
+            final_data.to_csv(out_data_file)
+
+    except (pd.errors.EmptyDataError, ValueError):
+        # If file is present but empty or invalid, treat trait data as unavailable
+        empty_df = pd.DataFrame()
+        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,
+            df=empty_df,
+            note="Trait file is empty or invalid; linking and final dataset output are skipped."
+        )
+else:
+    # If the clinical file does not exist at all, the trait is unavailable
+    empty_df = pd.DataFrame()
+    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,
+        df=empty_df,
+        note="No trait data file was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Fibromyalgia/code/TCGA.py

@@ -0,0 +1,55 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Fibromyalgia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Fibromyalgia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Fibromyalgia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Fibromyalgia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Fibromyalgia/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify subdirectories under tcga_root_dir
+subdirectories = os.listdir(tcga_root_dir)
+
+# Search terms related to "Fibromyalgia"
+search_terms = ["fibromyalgia", "fibro", "myalgia", "chronic_widespread_pain", "cwp"]
+
+trait_subdir = None
+for d in subdirectories:
+    d_lower = d.lower()
+    if any(term in d_lower for term in search_terms):
+        trait_subdir = d
+        break
+
+# 2. If none found, skip this trait
+if not trait_subdir:
+    print(f"No suitable subdirectory found for trait '{trait}'. Skipping...")
+    is_gene_available = False
+    is_trait_available = False
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+else:
+    # 2. Identify file paths
+    cohort_path = os.path.join(tcga_root_dir, trait_subdir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)
+
+    # 3. Load both files as dataframes
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t', low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t', low_memory=False)
+
+    # 4. Print the column names of the clinical data
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())

p1/preprocess/Fibromyalgia/cohort_info.json

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p1/preprocess/Hypertrophic_Cardiomyopathy/code/GSE36961.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertrophic_Cardiomyopathy"
+cohort = "GSE36961"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy"
+in_cohort_dir = "../DATA/GEO/Hypertrophic_Cardiomyopathy/GSE36961"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/GSE36961.csv"
+out_gene_data_file = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/clinical_data/GSE36961.csv"
+json_path = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This is a gene expression (mRNA) study
+
+# 2. Variable Availability
+trait_row = 3   # "disease state: hypertrophic cardiomyopathy (HCM)" vs "sample type: control"
+age_row = 1     # "age (yrs): 9, 10, 11, ... 51"
+gender_row = 0  # "Sex: male", "Sex: female"
+
+# 2.2 Data Type Conversion
+def convert_trait(value: str):
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) > 1:
+        raw_val = parts[1].strip().lower()
+    else:
+        raw_val = value.strip().lower()
+    if "cardiomyopathy" in raw_val:
+        return 1
+    elif "control" in raw_val:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) > 1:
+        raw_val = parts[1].strip()
+    else:
+        raw_val = value.strip()
+    try:
+        return float(raw_val)
+    except:
+        return None
+
+def convert_gender(value: str):
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) > 1:
+        raw_val = parts[1].strip().lower()
+    else:
+        raw_val = value.strip().lower()
+    if "female" in raw_val:
+        return 0
+    elif "male" in raw_val:
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+usable_init = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait,
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df, 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])
+print("Based on the gene identifier list, they appear to be standard gene symbols.\nrequires_gene_mapping = False")
+# STEP5
+# 1. Normalize gene symbols using the NCBI Gene database synonym information.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values.
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in the trait and demographic features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and metadata saving.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data
+)
+
+# 6. Save the fully processed data only if it's usable.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Hypertrophic_Cardiomyopathy/code/TCGA.py

@@ -0,0 +1,50 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypertrophic_Cardiomyopathy"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Hypertrophic_Cardiomyopathy/cohort_info.json"
+
+import os
+
+# 1. Identify subdirectories in tcga_root_dir
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# 2. Attempt to find a match to "Hypertrophic_Cardiomyopathy"
+trait_keywords = ["hypertrophic", "cardiomyopathy", "heart", "cardiac"]
+matched_dirs = []
+for d in subdirs:
+    lower_d = d.lower()
+    if any(keyword in lower_d for keyword in trait_keywords):
+        matched_dirs.append(d)
+
+# 3. If no suitable directories found, skip this trait and mark the task completed
+if not matched_dirs:
+    print("No suitable TCGA subdirectory found for trait:", trait)
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False,
+        note="No matched subdirectory found for this trait."
+    )
+else:
+    # Normally, we'd pick the best match if multiple. For now, pick the first.
+    chosen_dir = matched_dirs[0]
+    chosen_path = os.path.join(tcga_root_dir, chosen_dir)
+    # 2. Identify data file paths
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(chosen_path)
+    # 3. Load both files as dataframes
+    clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+    # 4. Print the clinical data columns
+    print("Clinical data columns:", clinical_df.columns.tolist())

p1/preprocess/Hypertrophic_Cardiomyopathy/cohort_info.json

@@ -0,0 +1 @@
+{"GSE36961": {"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": 142, "note": ""}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p1/preprocess/Hypertrophic_Cardiomyopathy/gene_data/GSE36961.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fd3cb153e8a94a03d27a8048601cf21af7c6b47d562397e7c7c0d3e9e2ed1bf
+size 32368017

p1/preprocess/Hypothyroidism/clinical_data/GSE151158.csv

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p1/preprocess/Hypothyroidism/clinical_data/GSE224330.csv

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p1/preprocess/Hypothyroidism/clinical_data/GSE75678.csv

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p1/preprocess/Hypothyroidism/clinical_data/GSE75685.csv

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p1/preprocess/Hypothyroidism/clinical_data/TCGA.csv

@@ -0,0 +1,573 @@
+,Hypothyroidism,Age,Gender
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p1/preprocess/Hypothyroidism/code/GSE151158.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE151158"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE151158"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypothyroidism/GSE151158.csv"
+out_gene_data_file = "./output/preprocess/1/Hypothyroidism/gene_data/GSE151158.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypothyroidism/clinical_data/GSE151158.csv"
+json_path = "./output/preprocess/1/Hypothyroidism/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The series uses RNA from liver tissue and measures expression of ~594 genes.
+
+# 2. Variable Availability and Data Type Conversion
+#    2.1 Identify keys for trait, age, gender
+trait_row = 12   # "hypothyroidism: N", "hypothyroidism: Y" => multiple values => available
+age_row = 1      # "age: 53", "age: 40", etc. => multiple numeric values => available
+gender_row = 2   # "Sex: F", "Sex: M", etc. => multiple values => available
+
+#    2.2 Define conversion functions
+def convert_trait(val):
+    if not isinstance(val, str):
+        return None
+    parts = val.split(":")
+    if len(parts) < 2:
+        return None
+    v = parts[1].strip().upper()
+    if v == 'Y':
+        return 1
+    elif v == 'N':
+        return 0
+    return None
+
+def convert_age(val):
+    if not isinstance(val, str):
+        return None
+    parts = val.split(":")
+    if len(parts) < 2:
+        return None
+    v = parts[1].strip()
+    try:
+        return float(v)
+    except ValueError:
+        return None
+
+def convert_gender(val):
+    if not isinstance(val, str):
+        return None
+    parts = val.split(":")
+    if len(parts) < 2:
+        return None
+    v = parts[1].strip().upper()
+    if v == 'M':
+        return 1
+    elif v == 'F':
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_final = False
+validate_and_save_cohort_info(
+    is_final=is_final,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview extracted clinical features
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview)
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed list of gene identifiers, these appear to be standard human gene symbols.
+print("requires_gene_mapping = False")
+# STEP5
+# 1. Normalize gene symbols using the NCBI Gene database synonym information.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values.
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in the trait and demographic features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and metadata saving.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data
+)
+
+# 6. Save the fully processed data only if it's usable.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Hypothyroidism/code/GSE224330.py

@@ -0,0 +1,162 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE224330"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE224330"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypothyroidism/GSE224330.csv"
+out_gene_data_file = "./output/preprocess/1/Hypothyroidism/gene_data/GSE224330.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypothyroidism/clinical_data/GSE224330.csv"
+json_path = "./output/preprocess/1/Hypothyroidism/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info indicating gene expression data
+
+# 2. Variable Availability
+trait_row = 3
+age_row = 1
+gender_row = 2
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    if not isinstance(value, str):
+        return 0
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return 0
+    val = parts[1].strip().lower()
+    return 1 if 'hypothyroidism' in val else 0
+
+def convert_age(value):
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower().replace('y', '')
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value):
+    if not isinstance(value, str):
+        return None
+    parts = 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
+
+# 3. Save Metadata with initial filtering
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait,
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Clinical Data Preview:", 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 the provided IDs (e.g., A_19_P00315452), they appear to be microarray probe IDs rather than standard human gene symbols.
+# Hence, they likely require mapping to proper gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Observe columns in the gene annotation dataframe preview:
+#    The 'ID' column matches our probe IDs (like 'A_19_P00315452'),
+#    and 'GENE_SYMBOL' column holds the corresponding gene symbols.
+
+# 2. Get a gene mapping dataframe for these two columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Convert probe-level measurements to gene expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, let's print the shape of the mapped gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data index sample:", gene_data.index[:10])
+# STEP5
+# 1. Normalize gene symbols using the NCBI Gene database synonym information.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values.
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in the trait and demographic features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and metadata saving.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data
+)
+
+# 6. Save the fully processed data only if it's usable.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Hypothyroidism/code/GSE32445.py

@@ -0,0 +1,109 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE32445"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE32445"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypothyroidism/GSE32445.csv"
+out_gene_data_file = "./output/preprocess/1/Hypothyroidism/gene_data/GSE32445.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypothyroidism/clinical_data/GSE32445.csv"
+json_path = "./output/preprocess/1/Hypothyroidism/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the series title mentioning "gene regulation patterns", it is likely gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics:
+# {0: ['strain: C57/Bl6'], 1: ['gender: male'], 2: ['age: 9 months'], 3: ['tissue: liver']}
+# - The data is from mice, not humans, so no valid human trait information is present.
+# - The "gender" and "age" in this dictionary each have only one unique value ("male", "9 months"), so these are constant.
+#   We consider them not available for associative studies.
+trait_row = None
+age_row = None
+gender_row = None
+
+def convert_trait(val: str) -> int:
+    # No trait data available in this dataset.
+    return None
+
+def convert_age(val: str) -> float:
+    # No human age data available; single constant value for mice.
+    return None
+
+def convert_gender(val: str) -> int:
+    # No human gender data available; single constant value observed (all male mice).
+    return None
+
+# 3. Save Metadata using 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
+# Since trait_row is None, clinical data extraction is skipped.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The gene identifiers (e.g., ILMN_1212602) are typical Illumina probe IDs,
+# which are not standard human gene symbols and need to be mapped.
+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))
+# STEP6
+# 1. Identify which columns in the annotation correspond to the Illumina probe IDs in gene_data and to gene symbols.
+#    However, from the preview we see "ID" in gene_annotation has entries like "1007_s_at" (Affymetrix),
+#    but gene_data uses "ILMN_xxxxxxx" (Illumina). They do not match.
+
+# 2. Attempt to extract the mapping between probe IDs ("ID") and gene symbols ("Gene Symbol").
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# Check overlap between the annotation and the expression probe IDs.
+common_probes = set(gene_mapping_df["ID"]).intersection(gene_data.index)
+if len(common_probes) == 0:
+    print("No valid Illumina annotation found for these probe IDs. Skipping mapping step.")
+else:
+    # 3. Convert the probe-level data to gene-level data by applying the mapping if there is overlap.
+    gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+
+# Print a brief shape and a few row indices to confirm the transformed data (if any).
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols/probes after potential mapping:", gene_data.index[:20])

p1/preprocess/Hypothyroidism/code/GSE75678.py

@@ -0,0 +1,153 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE75678"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75678"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypothyroidism/GSE75678.csv"
+out_gene_data_file = "./output/preprocess/1/Hypothyroidism/gene_data/GSE75678.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypothyroidism/clinical_data/GSE75678.csv"
+json_path = "./output/preprocess/1/Hypothyroidism/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Check if gene expression data is available.
+#    Based on the background text: "Gene Expression of Mexican Patients with Breast Cancer"
+#    it is indeed gene expression data (not miRNA or methylation).
+is_gene_available = True
+
+# 2) Determine variable availability and define row indices.
+#    We search for "Hypothyroidism" in row 21. The presence of "personal pathological hystory: Hypothyroidism"
+#    implies we can interpret that row as indicating whether a sample has hypothyroidism or not.
+trait_row = 21  # row for hypothyroidism = personal pathological hystory
+#    Age is available at row 19: "age at diagnosis: ..."
+age_row = 19
+#    Gender row (row 1) has only one unique value "female"; no variation => not useful for association.
+gender_row = None
+
+# 2.2) Functions to convert the data into the chosen types.
+
+def convert_trait(value: str):
+    """
+    Convert personal pathological history data to a binary indicator for Hypothyroidism.
+    Return 1 if 'Hypothyroidism' is in the string, else 0.
+    If the string is unparseable or unknown, return None.
+    """
+    # Split on ":", take the second part if exists
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()  # e.g. "hypothyroidism", or "rheumatoid arthritis"
+    return 1 if "hypothyroidism" in val_str else 0
+
+def convert_age(value: str):
+    """
+    Convert strings like 'age at diagnosis: 45' to a continuous float representing age.
+    Unknown or invalid values become None.
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+# Since gender has no variation, we do not need a conversion function.
+convert_gender = None
+
+# 3) Perform initial filtering and save metadata.
+#    Trait data availability depends on whether 'trait_row' is None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) If trait is available, extract clinical features and save them.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait="Hypothyroidism",
+        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_output = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_output)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# Gene Identifier Mapping
+mapping_df = get_gene_mapping(gene_annotation, "ID", "GENE_SYMBOL")
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP5
+# 1. Normalize gene symbols using the NCBI Gene database synonym information.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values.
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in the trait and demographic features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and metadata saving.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data
+)
+
+# 6. Save the fully processed data only if it's usable.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Hypothyroidism/code/GSE75685.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+cohort = "GSE75685"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Hypothyroidism"
+in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE75685"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypothyroidism/GSE75685.csv"
+out_gene_data_file = "./output/preprocess/1/Hypothyroidism/gene_data/GSE75685.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypothyroidism/clinical_data/GSE75685.csv"
+json_path = "./output/preprocess/1/Hypothyroidism/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the metadata, it appears to be an mRNA expression dataset.
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# We identified that "personal pathological hystory: Hypothyroidism" is under key 21.
+trait_row = 21  # multiple unique entries, including "Hypothyroidism"
+# Age at diagnosis is under key 19 with multiple values.
+age_row = 19
+# Gender is under key 1, but it's always 'Female', hence considered not available.
+gender_row = None
+
+# 2.2 Data Type Conversion
+
+def convert_trait(value: str):
+    """
+    Convert personal pathological history to a binary indicator of Hypothyroidism.
+    1 if 'Hypothyroidism' is present, else 0.
+    """
+    # Extract the substring after the first colon, lowercase it:
+    val = value.split(':', 1)[-1].strip().lower()
+    if 'hypothyroidism' in val:
+        return 1
+    elif val in {'na', 'n/a', ''}:
+        return None
+    else:
+        return 0
+
+def convert_age(value: str):
+    """
+    Convert age at diagnosis to an integer (continuous).
+    """
+    val = value.split(':', 1)[-1].strip()
+    try:
+        return int(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender to binary (female=0, male=1). 
+    Not used here because gender_row=None, but defined for completeness.
+    """
+    val = value.split(':', 1)[-1].strip().lower()
+    if 'female' in val:
+        return 0
+    elif 'male' in val:
+        return 1
+    else:
+        return None
+
+# Check if trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3. Save initial metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    # 'clinical_data' is assumed to be the DataFrame containing the parsed sample characteristics 
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical features
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_result)
+
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given identifiers are numeric indices and not standard human gene symbols.
+# Therefore, mapping to human gene symbols is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide columns for probe identifier and gene symbol in the annotation dataframe
+#    From observation, "ID" stores the probe identifiers matching the gene_data index,
+#    and "GENE_SYMBOL" seems to store the gene symbols.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 2. Convert probe-level data to gene-level data using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print basic information about the resulting gene_data
+print("Gene data shape after mapping:", gene_data.shape)
+print("Preview of mapped gene indices:", gene_data.index[:20])
+# STEP5
+# 1. Normalize gene symbols using the NCBI Gene database synonym information.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values.
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in the trait and demographic features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and metadata saving.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data
+)
+
+# 6. Save the fully processed data only if it's usable.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Hypothyroidism/code/TCGA.py

@@ -0,0 +1,111 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Hypothyroidism"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Hypothyroidism/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Hypothyroidism/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Hypothyroidism/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Hypothyroidism/cohort_info.json"
+
+import os
+
+# 1. Identify subdirectories in tcga_root_dir
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Use trait-specific or relevant keywords for 'Hypothyroidism'
+trait_keywords = ["hypothyroid", "thyroid"]
+
+matched_dirs = []
+for d in subdirs:
+    lower_d = d.lower()
+    if any(keyword in lower_d for keyword in trait_keywords):
+        matched_dirs.append(d)
+
+# 3. If no suitable directories found, skip this trait and mark the task completed
+if not matched_dirs:
+    print("No suitable TCGA subdirectory found for trait:", trait)
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False,
+        note="No matched subdirectory found for this trait."
+    )
+else:
+    # Normally, we'd pick the best match if multiple. For now, pick the first.
+    chosen_dir = matched_dirs[0]
+    chosen_path = os.path.join(tcga_root_dir, chosen_dir)
+    # 2. Identify data file paths
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(chosen_path)
+    # 3. Load both files as dataframes
+    clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
+    # 4. Print the clinical data columns
+    print("Clinical data columns:", clinical_df.columns.tolist())
+# Step 1: Identify columns
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Step 2: Extract and preview the data from clinical_df
+if candidate_age_cols:
+    age_preview = preview_df(clinical_df[candidate_age_cols], n=5)
+    print(age_preview)
+if candidate_gender_cols:
+    gender_preview = preview_df(clinical_df[candidate_gender_cols], n=5)
+    print(gender_preview)
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print("Chosen age_col:", age_col)
+print("Chosen gender_col:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2. Normalize gene symbols and save the normalized gene expression data
+normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_genetic_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data by sample IDs
+transposed_genetic_df = normalized_genetic_df.T
+linked_data = selected_clinical_df.join(transposed_genetic_df, how='inner')
+
+# 4. Handle missing values in the linked data
+cleaned_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine trait bias (and remove any heavily biased demographic features)
+is_trait_biased, final_data = judge_and_remove_biased_features(cleaned_data, trait)
+
+# 6. Final validation and cohort info recording
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=final_data,
+    note="No specific notes."
+)
+
+# 7. Save final data if usable
+if is_usable:
+    # Save final clinical subset
+    demographic_cols = [col for col in [trait, 'Age', 'Gender'] if col in final_data.columns]
+    final_data[demographic_cols].to_csv(out_clinical_data_file)
+
+    # Save the entire linked dataset
+    final_data.to_csv(out_data_file)

p1/preprocess/Hypothyroidism/cohort_info.json

@@ -0,0 +1 @@
+{"GSE75685": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 54, "note": ""}, "GSE75678": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": false, "sample_size": 54, "note": ""}, "GSE32445": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE224330": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 31, "note": ""}, "GSE151158": {"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": 61, "note": ""}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 572, "note": "No specific notes."}}