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p1/preprocess/Esophageal_Cancer/TCGA.csv

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p1/preprocess/Glioblastoma/code/GSE279426.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE279426"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE279426"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glioblastoma/GSE279426.csv"
+out_gene_data_file = "./output/preprocess/1/Glioblastoma/gene_data/GSE279426.csv"
+out_clinical_data_file = "./output/preprocess/1/Glioblastoma/clinical_data/GSE279426.csv"
+json_path = "./output/preprocess/1/Glioblastoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Gene Expression Data Availability
+# Based on the title ("Expression data... glioblastoma") and the descriptions, it is gene expression data.
+is_gene_available = True
+
+# Step 2: Variable Availability and Data Type Conversion
+
+# Observing the sample characteristics dictionary:
+# 0 => name_in_pmid_21471286: ... (IDs)
+# 1 => alternative_name: ... (IDs)
+# 2 => treatment_gefitinib: T0/T1/T2
+# 3 => type: human/xenograft
+# 4 => egfr_amplification: A0/A1
+# 5 => disease: GBM
+#
+# There is no row explicitly or implicitly indicating age or gender.
+# For the trait "Glioblastoma", all samples appear to be GBM (no variation), thus it's not useful for association analysis.
+# Hence, we conclude that trait_row = None, age_row = None, gender_row = None.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Even though we have no actual data rows for these variables, we'll define conversion functions as placeholders:
+
+def convert_trait(raw_value: str) -> int:
+    # Placeholder for trait data (not used since trait_row is None)
+    return None
+
+def convert_age(raw_value: str) -> float:
+    # Placeholder for age data (not used since age_row is None)
+    return None
+
+def convert_gender(raw_value: str) -> int:
+    # Placeholder for gender data (not used since gender_row is None)
+    return None
+
+# Step 3: Save Metadata
+# Check trait data availability: if trait_row is None => is_trait_available = False
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical Feature Extraction
+# 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])
+# From the given index values, such as "1007_s_at" and "1053_at", these look like Affymetrix probe set IDs 
+# rather than standard human gene symbols. Hence they require mapping.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Based on the preview, the column "ID" in the gene annotation matches the Affymetrix probe IDs
+#    used in the gene_data. The "Gene Symbol" column stores the gene symbols we want.
+probe_col = "ID"
+symbol_col = "Gene Symbol"
+
+# 2. Get a dataframe mapping probe IDs to their corresponding gene symbols.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Convert probe-level measurements to gene-level expression values.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data index preview:", gene_data.index[:10])
+# STEP 5
+# 1) Normalize the gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since there is no trait data (trait_row was None), we cannot link clinical features or perform bias checks.
+# We still must do final validation to record that trait data is unavailable.
+
+# Provide a placeholder for is_biased; it won't matter because is_trait_available is False.
+is_biased_placeholder = False
+
+# 5) Perform final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,        # We do have gene expression data
+    is_trait_available=False,      # Trait data is unavailable
+    is_biased=is_biased_placeholder,
+    df=normalized_gene_data,       # We pass the gene data but there's no trait column
+    note="No trait data; cannot complete linking or bias checks."
+)
+
+# 6) If the dataset is deemed usable, save the final linked data
+# In this scenario, is_usable will be False because the trait is not available.
+if is_usable:
+    normalized_gene_data.to_csv(out_data_file)

p1/preprocess/Glioblastoma/code/GSE39144.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+cohort = "GSE39144"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glioblastoma"
+in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE39144"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glioblastoma/GSE39144.csv"
+out_gene_data_file = "./output/preprocess/1/Glioblastoma/gene_data/GSE39144.csv"
+out_clinical_data_file = "./output/preprocess/1/Glioblastoma/clinical_data/GSE39144.csv"
+json_path = "./output/preprocess/1/Glioblastoma/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
+#    Assuming this is a gene expression dataset
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+#    No variation or data was found for trait, age, or gender in this dataset,
+#    so we set their row identifiers to None
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions, though we won't actually use them given our row selections
+def convert_trait(x: str):
+    # Example stub: parse after colon
+    val = x.split(':', 1)[-1].strip() if ':' in x else x.strip()
+    # If it were available and binary, we'd convert to 0/1 or continuous accordingly
+    return None
+
+def convert_age(x: str):
+    val = x.split(':', 1)[-1].strip() if ':' in x else x.strip()
+    # If it were available, parse as integer or float
+    return None
+
+def convert_gender(x: str):
+    val = x.split(':', 1)[-1].strip() if ':' in x else x.strip()
+    # If it were available, convert female -> 0, male -> 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip this step
+# 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 look like Affymetrix probe set IDs rather than human gene symbols.
+# Therefore, they need to be mapped to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP7: Gene Identifier Mapping
+
+# 1) Identify the annotation columns that match the probe IDs in the gene expression data ("ID") 
+#    and the gene symbol column in the annotation data ("Gene Symbol").
+
+# 2) Create a gene mapping dataframe by extracting these two columns from the annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3) Apply probe-to-gene mapping on the expression data to get gene-level expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 5
+# 1) Normalize the gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since there is no trait data (trait_row was None), we cannot link clinical features or perform bias checks.
+# We still must do final validation to record that trait data is unavailable.
+
+# Provide a placeholder for is_biased; it won't matter because is_trait_available is False.
+is_biased_placeholder = False
+
+# 5) Perform final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,        # We do have gene expression data
+    is_trait_available=False,      # Trait data is unavailable
+    is_biased=is_biased_placeholder,
+    df=normalized_gene_data,       # We pass the gene data but there's no trait column
+    note="No trait data; cannot complete linking or bias checks."
+)
+
+# 6) If the dataset is deemed usable, save the final linked data
+# In this scenario, is_usable will be False because the trait is not available.
+if is_usable:
+    normalized_gene_data.to_csv(out_data_file)

p1/preprocess/Glioblastoma/code/TCGA.py

@@ -0,0 +1,119 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glioblastoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glioblastoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Glioblastoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Glioblastoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Glioblastoma/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for "Glioblastoma" (GBM)
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+trait_keyword = "Glioblastoma"
+trait_abbreviation = "GBM"
+
+target_subdir = None
+for sd in subdirectories:
+    if trait_keyword.lower() in sd.lower() or trait_abbreviation.lower() in sd.lower():
+        # Pick the first matching directory, presumably the most specific one we find
+        target_subdir = sd
+        break
+
+if target_subdir is None:
+    # No suitable data found for this trait; mark as completed
+    print("No TCGA subdirectory found for the trait. Skipping.")
+else:
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    # 2. Locate clinical and genetic data files
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load the data
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print column names of clinical data
+    print(clinical_df.columns)
+# Step 1: Identify candidate columns for age and gender
+candidate_age_cols = []
+candidate_gender_cols = []
+
+# Print the results
+print("candidate_age_cols =", candidate_age_cols)
+print("candidate_gender_cols =", candidate_gender_cols)
+
+# Step 2: If there were any candidate columns, preview the data
+if candidate_age_cols or candidate_gender_cols:
+    # Assuming 'clinical_df' holds the clinical data
+    preview_columns = candidate_age_cols + candidate_gender_cols
+    preview_data = clinical_df[preview_columns].head(5).to_dict(orient='list')
+    print("Preview of candidate columns:", preview_data)
+age_col = None
+gender_col = None
+
+print("Chosen age column:", age_col)
+print("Chosen gender column:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# (Optional) Save the selected clinical data
+selected_clinical_df.to_csv(out_clinical_data_file)
+
+# 2. Normalize gene symbols in the genetic data
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3. Link the clinical and genetic data on sample IDs
+linked_data = selected_clinical_df.join(normalized_gene_df.T, how="inner")
+
+# 4. Handle missing values
+cleaned_df = handle_missing_values(linked_data, trait)
+
+# 5. Determine if the trait or demographic features are biased
+is_biased, final_df = judge_and_remove_biased_features(cleaned_df, trait)
+
+# 6. Final quality validation
+is_gene_available = not normalized_gene_df.empty
+is_trait_available = trait in final_df.columns
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_biased,
+    df=final_df,
+    note=""
+)
+
+# 7. If the dataset is usable, save the final dataframe
+if is_usable:
+    final_df.to_csv(out_data_file)

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p1/preprocess/Glucocorticoid_Sensitivity/code/GSE15820.py

@@ -0,0 +1,221 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE15820"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE15820"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE15820.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE15820.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE15820.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/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
+# From the background info ("expression profiling on Exon 1.0 microarrays"), we see it's gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Examining the sample characteristics dictionary:
+# {0: [...], 1: [...], 2: [...], 3: [...], 4: [...]}
+# None of the keys provide a measure of "Glucocorticoid_Sensitivity", age, or gender in a way
+# that varies across samples. Therefore, all three are considered unavailable.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# The data type conversion functions must still be defined, though they won't be used if their rows are None.
+def convert_trait(value: str):
+    # No trait data is available, so return None for all values.
+    return None
+
+def convert_age(value: str):
+    # No age data is available, so return None for all values.
+    return None
+
+def convert_gender(value: str):
+    # No gender data is available, so return None for all values.
+    return None
+
+# 3. Save Metadata (initial filtering)
+# If trait_row is None, then trait data is not available.
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    note="No trait data found; in vitro cell-line study."
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip this step (no clinical data to extract).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers ('52_36nbg_gcXX'), they do not match standard human gene symbols.
+# They appear to be custom microarray probe IDs and therefore likely require mapping.
+print("These identifiers appear to be probe-level IDs rather than standardized 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
+
+# The reviewer suggests that the annotation column used for probe identifiers should match
+# the row labels in the expression data (e.g., "52_36nbg_gc10"). However, from the annotation
+# preview, none of the columns directly contain the "52_36nbg_gcXX" pattern. The "ID" column
+# has entries like "52_36nENST...", while "probes" and "indices" have numeric references that
+# do not match the expression data index. Therefore, there is no direct column to match
+# "52_36nbg_gcXX". Proceeding with "ID" vs. "symbol" will likely yield an empty or very small
+# mapping, since the annotation file does not appear to align with the expression data.
+
+# 1. Decide which columns in the annotation correspond to the expression data "ID" and the gene symbol.
+#    We'll use "ID" for the probe/row identifiers and "symbol" for the gene symbols, aware that
+#    these do not actually match the expression data index.
+
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col="ID", gene_col="symbol")
+
+# 2. Convert probe-level data to gene-level data by applying the gene mapping.
+#    This will likely produce empty or near-empty results due to the mismatch.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE32962.py

@@ -0,0 +1,227 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE32962"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE32962"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE32962.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE32962.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE32962.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/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 dataset contains gene expression profiles.
+
+# 2. Variable Availability and Data Type Conversion
+# Based on the sample characteristics, the 'prednisolone sensitivity' matches our trait ("Glucocorticoid_Sensitivity").
+# Hence, trait_row is 4. Age has constant value (<1 year), so we set age_row=None. Gender data is not provided, so gender_row=None.
+trait_row = 4
+age_row = None
+gender_row = None
+
+# Define conversion functions
+def convert_trait(value: str):
+    # Typically "prednisolone sensitivity: resistant" or "prednisolone sensitivity: sensitive"
+    # We extract the part after the colon and convert to binary (resistant=1, sensitive=0).
+    part = value.split(":", 1)[-1].strip().lower()
+    if part == "resistant":
+        return 1
+    elif part == "sensitive":
+        return 0
+    else:
+        return None
+
+# Age and Gender are unavailable
+convert_age = None
+convert_gender = None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if we have trait data)
+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
+    )
+    previewed_clinical = preview_df(clinical_features_df, n=5)
+    print("Preview of clinical features:", previewed_clinical)
+    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])
+# These identifiers (e.g., "1007_s_at") are Affymetrix microarray probe IDs, 
+# which are not standard human gene symbols and typically need to be mapped.
+
+# Concluding our review:
+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) We identify that the "ID" column in the annotation DataFrame
+#    matches the probe identifiers (e.g. "1007_s_at") in gene_data.
+#    We also identify "Gene Symbol" as the column storing the gene symbols.
+
+# 2) Get the mapping between probe IDs and gene symbols.
+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)
+
+# Print shape and a quick look at the index to confirm changes.
+print("Gene-level expression data shape:", gene_data.shape)
+print("First 20 genes in the mapped data:", gene_data.index[:20])
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE33649.py

@@ -0,0 +1,252 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE33649"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE33649"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE33649.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE33649.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE33649.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine whether gene expression data is available
+is_gene_available = True  # Based on the series description, it measures transcriptome-wide response
+
+# 2) Identify the rows in the sample characteristics dictionary for each variable
+trait_row = 3
+age_row = 6
+gender_row = 5
+
+# 2) Define data conversion functions
+def _get_value_after_colon(value: str):
+    """Extract the portion of the string after the first colon, stripping extra whitespace."""
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    return parts[1].strip()
+
+def convert_trait(value: str):
+    """Convert the trait (Glucocorticoid_Sensitivity) to a float."""
+    val = _get_value_after_colon(value)
+    if val is None:
+        return None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_age(value: str):
+    """Convert the age to a float."""
+    val = _get_value_after_colon(value)
+    if val is None:
+        return None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender strings to binary values:
+    - female -> 0
+    - male   -> 1
+    """
+    val = _get_value_after_colon(value)
+    if val is None:
+        return None
+    val_lower = val.lower()
+    if 'female' in val_lower:
+        return 0
+    elif 'male' in val_lower:
+        return 1
+    return None
+
+# 3) Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) If trait data is available, extract clinical features and save
+if trait_row is not None:
+    clinical_selected_df = geo_select_clinical_features(
+        clinical_data,      # DataFrame from previous steps
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the selected clinical features
+    preview_info = preview_df(clinical_selected_df)
+    print("Preview of extracted clinical features:", preview_info)
+
+    # Save the clinical data as CSV
+    clinical_selected_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 are Illumina probe IDs, not human gene symbols
+print("These gene identifiers are Illumina probe IDs and require mapping to human 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))
+# 1. Decide which columns correspond to the gene expression ID and the gene symbol
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 2. Get a mapping between probe IDs and gene symbols
+# (This is already done by the get_gene_mapping function above.)
+
+# 3. Convert probe-level expression data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, preview the mapped gene_data
+print("Preview of gene expression data after mapping:")
+print(preview_df(gene_data))
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE42002.py

@@ -0,0 +1,75 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE42002"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE42002"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE42002.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE42002.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE42002.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/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
+#    From the background info, we see mRNA expression arrays were used, indicating gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+#    - Checking the sample characteristics dictionary, we only see genotype, condition (trauma/control), and tissue.
+#    - None of these directly map to the trait "Glucocorticoid_Sensitivity", nor do we have age or gender info.
+trait_row = None   # No row corresponds to "Glucocorticoid_Sensitivity"
+age_row = None     # No age information
+gender_row = None  # No gender information
+
+#    - Although data is not available for trait, age, and gender, we still define the conversion functions as requested.
+#      Here they will simply return None because there's no real data to process.
+
+def convert_trait(val: str) -> Optional[float]:
+    # No actual trait data; for demonstration, parse after the colon if it existed, return None.
+    return None
+
+def convert_age(val: str) -> Optional[float]:
+    # No actual age data in this dataset; return None.
+    return None
+
+def convert_gender(val: str) -> Optional[int]:
+    # No actual gender data in this dataset; return None.
+    return None
+
+# 3. Save Metadata (initial filtering)
+#    Trait data availability is determined by whether trait_row is None. Here it is None, so is_trait_available=False.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip extracting clinical features.

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE48801.py

@@ -0,0 +1,229 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE48801"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE48801"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE48801.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE48801.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE48801.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # From the background info, this dataset measures transcriptome-wide response.
+
+# 2. Variable Availability and Data Type Conversion
+#
+# Observing the sample characteristics dictionary:
+#  Key=0: treatment
+#  Key=1: in vitro lymphocyte gc sensitivity (measured as %inhibition by dex)
+#
+# There's no mention of age or gender, so those are not available. 
+# The "in vitro lymphocyte gc sensitivity" corresponds to our trait "Glucocorticoid_Sensitivity".
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Trait is continuous. We'll parse the numeric value after the colon, convert to float, and return None on failure.
+def convert_trait(value: str):
+    try:
+        val_str = value.split(":")[-1].strip()
+        return float(val_str)
+    except:
+        return None
+
+# Age and gender are not available, so we set these converters to None.
+convert_age = None
+convert_gender = None
+
+# Determine if trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3. Save Metadata (initial filtering)
+validate_and_save_cohort_info(
+    is_final=False, 
+    cohort=cohort, 
+    info_path=json_path, 
+    is_gene_available=is_gene_available, 
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data, 
+        trait=trait, 
+        trait_row=trait_row, 
+        convert_trait=convert_trait,
+        age_row=age_row, 
+        convert_age=convert_age, 
+        gender_row=gender_row, 
+        convert_gender=convert_gender
+    )
+    # Preview the extracted features
+    preview = preview_df(selected_clinical_df)
+    print("Preview of clinical features:\n", preview)
+    # Save the extracted 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])
+# The given identifiers (e.g., ILMN_1343291) appear to be Illumina probe IDs, which are not human gene symbols.
+# Therefore, they 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. We see from the gene annotation preview that the column "ID" matches the probe IDs in our gene_data,
+#    and "Symbol" corresponds to human gene symbols.
+
+# 2. Create the gene mapping dataframe by selecting the identifier column ("ID") and the symbol column ("Symbol").
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3. Convert probe-level data into gene-level data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Let's preview the resulting gene expression data (first few gene symbols)
+print("Gene expression data after mapping, first few rows:")
+print(gene_data.head())
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE50012.py

@@ -0,0 +1,266 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE50012"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE50012"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE50012.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE50012.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE50012.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/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 textual description indicating transcriptional (gene expression) study,
+#    we conclude gene expression data is available.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+#    We identify the following rows in the sample characteristics dictionary:
+#    - trait_row = 3 ("in vitro lymphocyte gc sensitivity" => continuous)
+#    - age_row = 5  ("age (years): ..." => continuous, also some entries "gender: ...")
+#    - gender_row = 5 (the same row 5 includes gender data => binary)
+
+trait_row = 3
+age_row = 5
+gender_row = 5
+
+def convert_trait(value: str):
+    """
+    Convert glucocorticoid sensitivity data to float.
+    Return None if it doesn't match GC-sensitivity pattern or can't be parsed.
+    """
+    val_lower = value.lower()
+    if "in vitro lymphocyte gc sensitivity" in val_lower:
+        parts = value.split(":", 1)
+        if len(parts) == 2:
+            try:
+                return float(parts[1].strip())
+            except:
+                return None
+    return None
+
+def convert_age(value: str):
+    """
+    Convert age data to float.
+    Return None if it doesn't match age pattern or can't be parsed.
+    """
+    val_lower = value.lower()
+    if "age (years)" in val_lower:
+        parts = value.split(":", 1)
+        if len(parts) == 2:
+            try:
+                return float(parts[1].strip())
+            except:
+                return None
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert gender data to binary: female -> 0, male -> 1.
+    Return None if it doesn't match gender pattern or can't be parsed.
+    """
+    val_lower = value.lower()
+    if "gender:" in val_lower:
+        parts = value.split(":", 1)
+        if len(parts) == 2:
+            g = parts[1].strip().lower()
+            if g.startswith("female"):
+                return 0
+            elif g.startswith("male"):
+                return 1
+    return None
+
+# 2.1) Check trait availability
+if trait_row is not None:
+    is_trait_available = True
+else:
+    is_trait_available = False
+
+# 3) Save Metadata (initial filtering)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait is available)
+if trait_row is not None:
+    clinical_features_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # assume clinical_data was loaded in a previous step
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview output
+    preview_result = preview_df(clinical_features_df)
+    print("Preview of selected clinical features:\n", preview_result)
+
+    # Save extracted clinical data
+    clinical_features_df.to_csv(out_clinical_data_file, index=True)
+# 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 index, these appear to be Illumina probe IDs rather than human gene symbols.
+# Therefore, mapping to gene symbols is required.
+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))
+# STEP6: Gene Identifier Mapping
+
+# 1) Identify the columns in the gene_annotation DataFrame that match the probe IDs and gene symbols.
+#    From the preview, "ID" matches the ILMN_xxx probes and "Symbol" stores the gene symbols.
+
+# 2) Obtain the gene mapping dataframe with the two relevant columns: "ID" and "Symbol".
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3) Convert the probe-level data in 'gene_data' to gene-level expression using the mapping dataframe.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For inspection, let's print the shape of the resulting gene-level DataFrame
+print("Gene-level 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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE57795.py

@@ -0,0 +1,226 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE57795"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE57795"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE57795.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE57795.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE57795.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression availability
+is_gene_available = True  # From the background info, microarray gene expression is performed
+
+# Step 2.1: Identify rows for trait, age, and gender
+# Observing the sample characteristics dictionary under key=5 for "dexamethasone response"
+trait_row = 5
+# No human age or gender fields in the dictionary
+age_row = None
+gender_row = None
+
+# Step 2.2: Define conversion functions
+
+def convert_trait(value: str):
+    val = value.split(':')[-1].strip().lower()
+    if 'sensitive' in val:
+        return 1
+    elif 'resistant' in val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    # Not used in this dataset
+    return None
+
+def convert_gender(value: str):
+    # Not used in this dataset
+    return None
+
+# Step 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
+)
+
+# Step 4: Extract and save clinical features if trait information is available
+if is_trait_available:
+    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
+    )
+    print(preview_df(df_clinical))
+    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 observed gene identifiers (e.g., ILMN_1343291), these appear to be Illumina probe IDs,
+# not standard human gene symbols, thus mapping is required.
+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. Decide which columns in the annotation match the gene expression data index ("ID") 
+#    and the gene symbols ("Symbol"). From the preview, "ID" corresponds to ILMN_xxx 
+#    probe IDs, and "Symbol" stores the gene symbols.
+
+# 2. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Let's print some info to verify
+print("Mapped gene expression data shape:", gene_data.shape)
+print("First 10 gene symbols in mapped data:", gene_data.index[:10].tolist())
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE58715.py

@@ -0,0 +1,208 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE58715"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE58715"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE58715.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE58715.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE58715.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/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 study description, this is gene expression data (not miRNA or methylation).
+
+# 2. Variable Availability and Data Type Conversion
+# From the provided sample characteristics, there do not appear to be human-subject trait, age, or gender data.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define placeholder conversion functions that return None (since the data is not available).
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save Metadata
+# Trait data availability can be determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip the clinical feature extraction step.
+# 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 have the "ILMN_" prefix, indicating Illumina probe IDs.
+# They are not standard human gene symbols and require mapping to gene symbols.
+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 the columns in the gene annotation dataframe that correspond to the probe IDs in the gene expression data
+#    and the gene symbol. From the preview, "ID" matches the "ILMN_*" probe identifiers, and "Symbol" houses the gene symbols.
+probe_id_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# 2. Obtain a mapping dataframe by extracting these two columns
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# For confirmation/inspection, one might optionally preview a small portion of the resulting mapped dataframe:
+print("Mapped gene expression data (head):")
+print(gene_data.head())
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE65645.py

@@ -0,0 +1,217 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE65645"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE65645"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE65645.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE65645.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE65645.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/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
+#    Based on the background information (lncRNA expression profiling), we set:
+is_gene_available = True
+
+# 2. Identify rows for trait, age, and gender availability
+#    From the sample characteristics dictionary:
+#    {0: ['sample_type: bone marrow'], 1: ['translocation: TEL_AML1', 'translocation: E2A_PBX1', 'translocation: MLL']}
+#    We see no mention of "Glucocorticoid_Sensitivity", age, or gender, so we set them all to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str):
+    # No actual data for trait, return None
+    return None
+
+def convert_age(value: str):
+    # No actual data for age, return None
+    return None
+
+def convert_gender(value: str):
+    # No actual data for gender, return None
+    return None
+
+# 3. Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Since 'trait_row' is None, we skip clinical feature extraction
+if is_trait_available:
+    # Would extract clinical features if available
+    pass
+# 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 displayed identifiers (e.g., A_19_P00315452, (+)E1A_r60_1, etc.),
+# these do not appear to be standard human gene symbols but rather probe or custom IDs.
+# Thus, 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. Decide which columns in the gene_annotation correspond to the probe IDs in gene_data and the gene symbols.
+#    From the previous previews, the 'ID' column matches probe identifiers, and 'GENE_SYMBOL' stores gene symbols.
+
+# 2. Get a gene mapping DataFrame with two columns: 'ID' (probe IDs) and 'Gene' (gene symbols).
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="GENE_SYMBOL"
+)
+
+# 3. Convert the probe-level measurements in 'gene_data' to gene-level measurements by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# If desired, you can preview the resulting gene_data here
+# print(gene_data.head())
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/GSE66705.py

@@ -0,0 +1,228 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+cohort = "GSE66705"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
+in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE66705"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/GSE66705.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/GSE66705.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/GSE66705.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine gene expression data availability
+is_gene_available = True  # Based on "Gene expression profiling" in the series design.
+
+# 2. Identify variable availability and define data type conversion functions
+trait_row = 0    # Row 0 has multiple categories ("RES","SEN","INT"), not constant, so it's available.
+age_row = None   # No age-related entry found, so not available.
+gender_row = None  # No gender-related entry found, so not available.
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert trait values to binary:
+    - SEN => 1
+    - INT or RES => 0
+    - #N/A or unknown => None
+    """
+    val = value.split(":", 1)[1].strip()
+    if val in ["#N/A", "NA", "n/a", "N/A"]:
+        return None
+    elif val == "SEN":
+        return 1
+    elif val in ["RES", "INT"]:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    return None  # Not applicable for this dataset
+
+def convert_gender(value: str) -> Optional[int]:
+    return None  # Not applicable for this dataset
+
+# 3. Save metadata with initial filtering
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Extract clinical features if trait data is available
+if trait_row is not None:
+    df_clinical = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview clinical data
+    print(preview_df(df_clinical))
+    # Save extracted clinical features
+    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 probe identifiers (e.g., "1007_s_at", "1053_at"), they are not standard human gene symbols.
+# They appear to be Affymetrix microarray probe IDs that 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))
+# Gene Identifier Mapping
+
+# 1. Determine columns corresponding to probe IDs (matching gene_data.index) and gene symbols
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Generate mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: Inspect the resulting gene expression data
+print(gene_data.head(10))
+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)
+        row_count = tmp_df.shape[0]
+        # Adjust index names based on the row count
+        if row_count == 1:
+            tmp_df.index = [trait]
+            note_msg = "Only trait row found; no age or gender."
+        elif row_count == 2:
+            tmp_df.index = [trait, "Gender"]
+            note_msg = "Trait and gender rows found; no age row."
+        elif row_count == 3:
+            tmp_df.index = [trait, "Age", "Gender"]
+            note_msg = "Trait, age, and gender rows found."
+        else:
+            # If row_count is unexpected, abort further steps
+            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=pd.DataFrame(),
+                note=f"Unexpected row_count={row_count} in clinical data."
+            )
+            raise SystemExit("Unexpected row_count in clinical data file. Stopping.")
+
+        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=note_msg
+        )
+
+        # 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; final dataset output 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 found; final dataset output skipped."
+    )

p1/preprocess/Glucocorticoid_Sensitivity/code/TCGA.py

@@ -0,0 +1,56 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Glucocorticoid_Sensitivity"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Glucocorticoid_Sensitivity/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Glucocorticoid_Sensitivity/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify subdirectories under tcga_root_dir
+subdirectories = os.listdir(tcga_root_dir)
+
+# Update search terms to reflect "Glucocorticoid_Sensitivity"
+search_terms = ["glucocorticoid", "corticoid", "adrenal"]
+
+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(f"Selected subdirectory: {trait_subdir}")
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())

p1/preprocess/Glucocorticoid_Sensitivity/cohort_info.json

@@ -0,0 +1 @@
+{"GSE66705": {"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": 104, "note": "Only trait row found; no age or gender."}, "GSE65645": {"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 file found; final dataset output skipped."}, "GSE58715": {"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 file found; final dataset output skipped."}, "GSE57795": {"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": 58, "note": "Only trait row found; no age or gender."}, "GSE50012": {"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": 48, "note": "Trait, age, and gender rows found."}, "GSE48801": {"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": 179, "note": "Only trait row found; no age or gender."}, "GSE42002": {"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}, "GSE33649": {"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": 48, "note": "Trait, age, and gender rows found."}, "GSE32962": {"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": 43, "note": "Only trait row found; no age or gender."}, "GSE15820": {"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 file found; final dataset output skipped."}, "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/Glucocorticoid_Sensitivity/gene_data/GSE15820.csv

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+size 12945312

p1/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE32962.csv

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+size 11282449

p1/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE33649.csv

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+size 12457644

p1/preprocess/Glucocorticoid_Sensitivity/gene_data/GSE50012.csv

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