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@@ -1244,3 +1244,19 @@ p1/preprocess/Kidney_Papillary_Cell_Carcinoma/gene_data/GSE19949.csv filter=lfs
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p1/preprocess/Liver_Cancer/GSE164760.csv

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p1/preprocess/Liver_Cancer/code/GSE228783.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_Cancer"
+cohort = "GSE228783"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_Cancer"
+in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE228783"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_Cancer/GSE228783.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_Cancer/gene_data/GSE228783.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_Cancer/clinical_data/GSE228783.csv"
+json_path = "./output/preprocess/1/Liver_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Assess gene expression data availability
+is_gene_available = True  # Based on the transcriptome context, we assume gene expression is available
+
+# 2) Determine variable availability (row keys) and define conversion functions
+
+# From the sample characteristics dictionary, row=2 appears to record multiple disease states (CRC Met, CCC, HCC, etc.),
+# and we can map "HCC" → 1 for our trait of interest (Liver_Cancer) and everything else → 0
+trait_row = 2
+age_row = None     # Not found
+gender_row = None  # Not found
+
+def convert_trait(value: str) -> Optional[int]:
+    # Extract substring after colon
+    val = value.split(':')[-1].strip().lower()
+    if val == 'hcc':
+        return 1
+    elif val in ['crc met', 'ccc', 'other']:
+        return 0
+    else:
+        return None
+
+# Age and gender data are unavailable, so return None
+def convert_age(value: str) -> Optional[float]:
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    return None
+
+# 3) Initial filtering and metadata saving
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,  # "Liver_Cancer"
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical features
+    preview = preview_df(selected_clinical_df, n=5, max_items=200)
+    print("Clinical Data Preview:", preview)
+
+    # Save clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the given identifiers, they appear to be Affymetrix probe IDs rather than standard human gene symbols.
+# Therefore, gene mapping to standard gene symbols is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6
+# 1) Identify the matching columns: "ID" matches the probe IDs in gene_data.index, and "Gene Symbol" provides the actual gene symbol.
+probe_col = "ID"
+symbol_col = "Gene Symbol"
+
+# 2) Extract a mapping dataframe from gene_annotation
+mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)
+
+# 3) Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print to verify the resulting gene_data structure
+print("Mapped gene_data dimensions:", gene_data.shape)
+print("Mapped gene_data preview:")
+print(gene_data.head())
+# STEP 7: Data Normalization and Linking
+
+import pandas as pd
+
+# 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Read the real clinical trait data (extracted in Step 2) from CSV.
+#    Recall that we saved it with index=False, so we rename the single row to be the trait name.
+selected_clinical_df = pd.read_csv(out_clinical_data_file)
+selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+# Since age_row and gender_row are None, there are no age or gender rows to merge. 
+# Our final clinical data is just the trait row.
+combined_clinical_df = selected_clinical_df
+
+# Link the clinical and genetic data by matching sample IDs in columns.
+linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+processed_data = handle_missing_values(linked_data, trait)
+
+# 4. Check trait bias and remove any biased demographic features (none exist here, but code remains general).
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 5. Final validation and metadata saving. If the dataset is valid (not severely biased) and has enough data, mark usable.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=processed_data,
+    note="Using true trait data extracted at Step 2."
+)
+
+# 6. If final dataset is usable, save it. Otherwise, skip saving.
+if is_usable:
+    processed_data.to_csv(out_data_file)

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p1/preprocess/Liver_cirrhosis/clinical_data/GSE285291.csv

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p1/preprocess/Liver_cirrhosis/code/GSE139602.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE139602"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE139602"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE139602.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE139602.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE139602.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine gene expression data availability
+is_gene_available = True  # Based on the series description (transcriptome analysis)
+
+# 2. Identify rows for trait, age, and gender availability
+trait_row = 0   # Disease states are found in key 0
+age_row = None  # No age information found
+gender_row = None  # No gender information found
+
+# 2.2 Define conversion functions
+def convert_trait(value: str):
+    # Extract the text after the colon
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Binary encoding: 0 = healthy, 1 = disease
+    if val == 'healthy':
+        return 0
+    elif val in ['ecld', 'compensated cirrhosis', 'decompesated cirrhosis', 'acute-on-chronic liver failure']:
+        return 1
+    else:
+        return None
+
+def convert_age(value: str):
+    # Not applicable since age data is not available
+    return None
+
+def convert_gender(value: str):
+    # Not applicable since gender data is not available
+    return None
+
+# 3. Save initial 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. Clinical feature extraction if trait data is available
+if trait_row is not None:
+    # Assume 'clinical_data' is already loaded in the environment
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    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])
+# Observing the gene identifiers, they appear to be Affymetrix probe IDs, not standard gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene annotation dataframe corresponding to the probe IDs and the gene symbols.
+#    Here, "ID" is the same kind of probe ID as in the gene expression data, and "Gene Symbol" stores the gene symbols.
+
+# 2. Get a gene mapping dataframe, extracting the two relevant columns and renaming the gene symbol column to "Gene".
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="Gene Symbol"
+)
+
+# 3. Convert probe-level to gene-level expression data by applying the mapping.
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# Optional: print shape and a small preview to observe the result
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data preview:\n", gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/code/GSE150734.py

@@ -0,0 +1,75 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE150734"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE150734"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE150734.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE150734.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE150734.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/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 background information "Gene expression profiling" we conclude this dataset likely has gene data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Inspecting the sample characteristics dictionary:
+# {0: ['fibrosis stage: 0', 'fibrosis stage: 1'], 
+#  1: ['pls risk prediction: High', 'pls risk prediction: Intermediate', 'pls risk prediction: Low']}
+#
+# None of these keys provide a direct or strongly inferable measure of "Liver_cirrhosis".
+# Hence, trait, age, and gender data are considered not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define placeholder converter functions (they will simply return None here).
+def convert_trait(raw_value: str):
+    return None
+
+def convert_age(raw_value: str):
+    return None
+
+def convert_gender(raw_value: str):
+    return None
+
+# Check if trait is available
+is_trait_available = (trait_row is not None)
+
+# 3. Save Metadata (initial filtering)
+# Since trait data is not available, this dataset fails 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
+# Skipped because trait_row is None (no clinical trait data available).

p1/preprocess/Liver_cirrhosis/code/GSE163211.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE163211"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE163211"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE163211.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE163211.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE163211.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/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 Nanostring assay for gene expression
+
+# 2. Variable Availability and Conversions
+#    From the sample characteristics, we identify:
+#    trait_row = 8 (nafld stage), age_row = 3, gender_row = 4
+trait_row = 8
+age_row = 3
+gender_row = 4
+
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    label = parts[1].strip().lower()
+    # Map "nash_f1_f4" to 1 (approx. cirrhosis if advanced) otherwise 0
+    if label == "nash_f1_f4":
+        return 1
+    elif label in ["normal", "steatosis", "nash_f0"]:
+        return 0
+    return None
+
+def convert_age(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    label = parts[1].strip()
+    try:
+        return float(label)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    label = parts[1].strip().lower()
+    if label == "female":
+        return 0
+    elif label == "male":
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    clinical_data_selected = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Clinical Data Preview:", preview_df(clinical_data_selected))
+    clinical_data_selected.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])
+requires_gene_mapping = False
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/code/GSE182060.py

@@ -0,0 +1,73 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE182060"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE182060"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE182060.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE182060.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE182060.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/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  # The series explicitly mentions "Gene expression profiling"
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics, we do not see any row containing
+# information about "Liver_cirrhosis", age, or gender. Hence:
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define placeholder conversion functions. They won't be used since
+# all rows are None, but we'll define them for completeness.
+
+def convert_trait(value: str) -> Optional[int]:
+    # No data available, so always return None
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # No data available, so always return None
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # No data available, so always return None
+    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 do not extract clinical features and skip this step.

p1/preprocess/Liver_cirrhosis/code/GSE182065.py

@@ -0,0 +1,141 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE182065"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE182065"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE182065.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE182065.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE182065.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine gene expression data availability
+#    Based on the series_description mentioning "expression profiling" and a 20-gene signature,
+#    we assume gene expression data is available.
+is_gene_available = True
+
+# 2. Determine data availability for trait, age, and gender
+#    The sample characteristics dictionary does not provide fields indicating variability
+#    for cirrhosis status, age, or gender.
+#    Therefore, set all row indices to None, meaning not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define conversion functions
+def convert_trait(value: str) -> int:
+    # No relevant data; return None
+    return None
+
+def convert_age(value: str) -> float:
+    # No relevant data; return None
+    return None
+
+def convert_gender(value: str) -> int:
+    # No relevant data; return None
+    return None
+
+# 3. Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction
+#    Since trait_row is None, we skip this substep.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on inspection of the gene identifiers (e.g., AARS, ABLIM1, ACOT2, etc.),
+# these appear to be valid human gene symbols and do not require additional mapping.
+
+requires_gene_mapping = False
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/code/GSE185529.py

@@ -0,0 +1,73 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE185529"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE185529"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE185529.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE185529.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE185529.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/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 information, it seems to contain gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+#    From the sample characteristics dictionary {0: ['treatment: siCTRL', 'treatment: siBNC2']},
+#    we see no mention of "Liver_cirrhosis", "age", or "gender." Therefore:
+trait_row = None
+age_row = None
+gender_row = None
+
+#    Define conversion functions. These will simply return None given the unavailability of actual data.
+
+def convert_trait(value: str) -> Optional[float]:
+    # No actual data is present, so return None
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # No actual age data is present, so return None
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # No actual gender data is present, so return None
+    return None
+
+# 3. Initial Filtering and Saving Metadata
+#    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
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip the clinical feature extraction.

p1/preprocess/Liver_cirrhosis/code/GSE212047.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE212047"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE212047"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE212047.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE212047.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE212047.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/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 background and the general context (focusing on hepatic stellate cell gene expression),
+#    we'll assume this dataset contains some form of gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+#    From the sample characteristics dictionary:
+#    {
+#      0: ['strain: Lhx2 floxed;  C57BL/6J background', 'strain: Mx1Cre+; Lhx2 floxed;  C57BL/6J background'],
+#      1: ['treatment: 2 weeks after poly:IC induce Mx1Cre activation'],
+#      2: ['cell type: FACS-sorted VitA+ Hepatic Stellate Cells']
+#    }
+#    There is no row indicating "Liver_cirrhosis", age, or gender information. Thus these variables
+#    are not available for associative study. So we set them to None.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+#    Even though we have no rows for these variables, we still define the conversion functions as requested.
+
+def convert_trait(val: str):
+    # Attempt to parse value
+    parts = val.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw_value = parts[1].strip().lower()
+    # This dataset does not provide a trait distinction, so return None
+    return None
+
+def convert_age(val: str):
+    # Attempt to parse value
+    parts = val.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw_value = parts[1].strip().lower()
+    # No actual age data here, so return None
+    return None
+
+def convert_gender(val: str):
+    # Attempt to parse value
+    parts = val.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw_value = parts[1].strip().lower()
+    # No actual gender data here, so return None
+    return None
+
+# 3. Save Metadata (initial filtering)
+#    Trait data availability depends on trait_row.
+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
+#    Because trait_row is None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1) Identify the matching columns for probe IDs and gene symbols
+#    From the annotation preview, "ID" matches the probe identifiers in our gene_data index,
+#    and "gene_assignment" contains text strings from which gene symbols can be extracted.
+
+# 2) Get the gene mapping dataframe using the library function
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="gene_assignment"
+)
+
+# 3) Convert probe-level measurements to gene-level expression values
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For checking, let's print the shape and a few gene symbols
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols:", list(gene_data.index[:20]))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/code/GSE285291.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE285291"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE285291"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE285291.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE285291.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE285291.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+is_gene_available = True  # Based on the series design mentioning OXPHOS gene expression
+
+# 2. Identify rows for trait, age, and gender
+trait_row = 1   # "status: Control/Compensated/Decompensated" indicates variation for cirrhosis
+age_row = None  # No age info found; all are "age-matched"
+gender_row = None  # All participants are men; no variation
+
+# 2.2 Define data type conversion functions
+def convert_trait(raw_value: str):
+    """Convert 'Control' -> 0, 'Compensated' or 'Decompensated' -> 1, else None."""
+    parts = raw_value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'control':
+        return 0
+    elif val in ['compensated', 'decompensated']:
+        return 1
+    return None
+
+def convert_age(raw_value: str):
+    """No age info; always return None."""
+    return None
+
+def convert_gender(raw_value: str):
+    """No gender variation; always return None."""
+    return None
+
+# 3. Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features if trait data is available
+if trait_row is not None:
+    clinical_features_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait, 
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save the clinical dataframe
+    print(preview_df(clinical_features_df, n=5))
+    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])
+# Based on observation, these gene identifiers (e.g., A2M, AADAT, AANAT, etc.) appear to be standard human gene symbols.
+# Hence, they do not require additional mapping to gene symbols.
+print("These gene identifiers are standard human gene symbols.")
+print("requires_gene_mapping = False")
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/code/GSE66843.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE66843"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE66843"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE66843.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE66843.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE66843.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/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 the dataset likely contains gene expression data
+is_gene_available = True  # This dataset appears to be a gene expression study (not purely miRNA/methylation).
+
+# 2. Variable Availability and Data Type Conversion
+
+# Since the sample characteristics only mention time points, infection status, and cell lines,
+# there is no clear reference to "Liver_cirrhosis", age, or gender. Hence, data for these variables is not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data conversion functions. Even though no data is available, we still provide them.
+def convert_trait(value: str):
+    """Convert trait values to a numeric or binary form. Not applicable here, returns None."""
+    # Typically, we might parse after the colon, but there's no relevant data in this dataset.
+    return None
+
+def convert_age(value: str):
+    """Convert age values to floats. Not applicable here, returns None."""
+    return None
+
+def convert_gender(value: str):
+    """Convert gender values to binary (female=0, male=1). Not applicable here, returns None."""
+    return None
+
+# 3. Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)  # No trait data found
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction: Skip because trait_row is None (meaning no clinical data is available)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# They appear to be Illumina microarray probe IDs, which are not standard human gene symbols.
+# Therefore, they need to be mapped to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene_annotation dataframe:
+#    - The column containing the probe identifiers is "ID".
+#    - The column containing the gene symbols is "Symbol".
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 2. Convert probe-level expression data to gene-level data using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print some information to confirm the transformation.
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("First 20 gene symbols in the mapped data:")
+print(gene_data.index[:20])
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/code/GSE85550.py

@@ -0,0 +1,135 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+cohort = "GSE85550"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
+in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE85550"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/GSE85550.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/GSE85550.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/GSE85550.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine if gene expression data is available
+is_gene_available = True  # Based on the series title and summary, it seems to be gene expression rather than other data
+
+# 2) Identify data availability for trait, age, and gender
+#    After reviewing the sample characteristics dictionary, no columns exist for these variables,
+#    so set them to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2) Data type conversion functions
+def convert_trait(val: str):
+    # No actual data to parse, return None
+    return None
+
+def convert_age(val: str):
+    # No actual data to parse, return None
+    return None
+
+def convert_gender(val: str):
+    # No actual data to parse, return None
+    return None
+
+# 3) Conduct initial filtering and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)  # Will be False
+)
+
+# 4) Since trait_row is None, we skip clinical features extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on biomedical knowledge, these identifiers (AARS, ABLIM1, etc.) match standard human gene symbols.
+# Therefore, no additional mapping is required.
+print("requires_gene_mapping = False")
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/code/TCGA.py

@@ -0,0 +1,138 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Liver_cirrhosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Liver_cirrhosis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Liver_cirrhosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Liver_cirrhosis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Liver_cirrhosis/cohort_info.json"
+
+import os
+import pandas as pd
+
+# List of subdirectories provided in the instructions:
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Synonyms for "Liver_Cancer"
+liver_synonyms = ["liver_cancer", "lihc", "hepatocellular", "hcc"]
+
+selected_subdirectory = None
+for subdir in subdirectories:
+    subdir_lower = subdir.lower()
+    if any(syn in subdir_lower for syn in liver_synonyms):
+        selected_subdirectory = subdir
+        break
+
+if not selected_subdirectory:
+    # If no matching directory is found, mark dataset as unavailable
+    is_final = False
+    is_gene_available = False
+    is_trait_available = False
+    _ = validate_and_save_cohort_info(
+        is_final=is_final,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+    print(f"No suitable directory found for '{trait}'. Skipped this trait.")
+else:
+    # Step 2: Identify clinicalMatrix file and PANCAN file
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as dataframes
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # Step 4: Print the column names of the clinical data
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))
+# Identify candidate columns
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+# Extract the candidate columns and preview
+age_data = clinical_df[candidate_age_cols] if candidate_age_cols else pd.DataFrame()
+gender_data = clinical_df[candidate_gender_cols] if candidate_gender_cols else pd.DataFrame()
+
+print("candidate_age_cols =", candidate_age_cols)
+print("candidate_gender_cols =", candidate_gender_cols)
+
+if not age_data.empty:
+    age_preview = preview_df(age_data, n=5, max_items=200)
+    print(age_preview)
+
+if not gender_data.empty:
+    gender_preview = preview_df(gender_data, n=5, max_items=200)
+    print(gender_preview)
+# Select the best column name for age
+age_col = "age_at_initial_pathologic_diagnosis"
+
+# Select the best column name for gender
+gender_col = "gender"
+
+# Print chosen columns
+print("Chosen age_col:", age_col)
+print("Chosen gender_col:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2. Normalize gene symbols in the expression data and save
+gene_df = normalize_gene_symbols_in_index(genetic_df)
+gene_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data
+#    The genetic data has samples as columns, so transpose before joining
+linked_data = selected_clinical_df.join(gene_df.T, how='inner')
+
+# 4. Handle missing values
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine and remove biased features
+biased_trait, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 6. Final quality validation
+gene_cols = [col for col in processed_data.columns if col not in [trait, "Age", "Gender"]]
+is_gene_available = len(gene_cols) > 0
+is_trait_available = trait in processed_data.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=biased_trait,
+    df=processed_data,
+    note="Final data processing for Kidney_Chromophobe"
+)
+
+# 7. Save linked data if usable
+if is_usable:
+    processed_data.to_csv(out_data_file)

p1/preprocess/Liver_cirrhosis/cohort_info.json

@@ -0,0 +1 @@
+{"GSE85550": {"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; dataset not usable for trait analysis."}, "GSE66843": {"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; dataset not usable for trait analysis."}, "GSE285291": {"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": 53, "note": "Completed trait-based preprocessing."}, "GSE212047": {"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; dataset not usable for trait analysis."}, "GSE185529": {"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}, "GSE182065": {"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; dataset not usable for trait analysis."}, "GSE182060": {"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}, "GSE163211": {"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": 318, "note": "Completed trait-based preprocessing."}, "GSE150734": {"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}, "GSE139602": {"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": 39, "note": "Completed trait-based preprocessing."}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 423, "note": "Final data processing for Kidney_Chromophobe"}}

p1/preprocess/Longevity/GSE48264.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:617823e2762ef477a7226f16072e929df795347ca06664e2fec294323b21a30d
+size 24533417

p1/preprocess/Longevity/clinical_data/GSE16717.csv

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p1/preprocess/Longevity/clinical_data/GSE48264.csv

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p1/preprocess/Longevity/code/GSE16717.py

@@ -0,0 +1,215 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Longevity"
+cohort = "GSE16717"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Longevity"
+in_cohort_dir = "../DATA/GEO/Longevity/GSE16717"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Longevity/GSE16717.csv"
+out_gene_data_file = "./output/preprocess/1/Longevity/gene_data/GSE16717.csv"
+out_clinical_data_file = "./output/preprocess/1/Longevity/clinical_data/GSE16717.csv"
+json_path = "./output/preprocess/1/Longevity/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene expression data availability
+is_gene_available = True  # Based on the background info indicating a microarray gene expression study
+
+# 2.1 Data Availability
+trait_row = 0    # 'group: long-lived sib', 'group: control', 'group: offspring'
+age_row = 2      # 'age: <float> years'
+gender_row = 1   # 'gender: female', 'gender: male'
+
+# 2.2 Data Type Conversion
+def convert_trait(value: str):
+    """
+    Convert 'group: <some_value>' to binary for longevity trait:
+      - 'long-lived sib' -> 1
+      - anything else -> 0
+    """
+    try:
+        val = value.split(":", 1)[1].strip().lower()
+    except (IndexError, AttributeError):
+        return None
+    if val == "long-lived sib":
+        return 1
+    else:
+        # 'control' or 'offspring'
+        return 0
+
+def convert_age(value: str):
+    """
+    Convert 'age: <numeric> years' to float.
+    """
+    try:
+        val = value.split(":", 1)[1].strip().lower()
+        val = val.replace("years", "").strip()
+        return float(val)
+    except:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert 'gender: male/female' to binary (female=0, male=1).
+    """
+    try:
+        val = value.split(":", 1)[1].strip().lower()
+    except (IndexError, AttributeError):
+        return None
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+
+# 3. 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. Clinical Feature Extraction if trait_row is not None
+if trait_row is not None:
+    clinical_selected = 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
+    )
+    # Observe preview
+    print("Preview of selected clinical data:", preview_df(clinical_selected, n=5))
+    # Save extracted clinical features
+    clinical_selected.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])
+# Because these IDs appear numeric and do not match standard human gene symbols,
+# they likely require mapping to standard gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify which columns store the probe IDs and which store the gene identifiers/symbols.
+#    From the annotation preview, "ID" matches the numeric identifiers in gene_data.index, and
+#    "GB_LIST" stores the accession info (which we'll treat as the "gene symbol" here).
+
+probe_column = "ID"
+gene_column = "GB_LIST"
+
+# Extract the mapping DataFrame from the annotation
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col=probe_column,
+    gene_col=gene_column
+)
+
+# 3. Convert probe-level measurements to gene-level expressions using the mapping
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# Print a quick preview of the resulting gene_data to confirm the transformation
+print("Preview of mapped gene data (columns, first few rows):")
+print(gene_data.iloc[:5, :5])
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+    # Clean possible extra quotes/spaces in the sample IDs to match with clinical data
+    normalized_gene_data.columns = normalized_gene_data.columns.str.strip().str.replace('"', '')
+
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV, which has shape (3, #samples).
+    #    The first row corresponds to the Longevity trait, the second to Age, the third to Gender,
+    #    and the columns are sample IDs.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+    # Remove any stray quotes/spaces from the sample IDs (the columns)
+    selected_clinical_df.columns = selected_clinical_df.columns.str.strip().str.replace('"', '')
+    # Set the row labels (index)
+    selected_clinical_df.index = [trait, 'Age', 'Gender']
+
+    # 3. Link clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 4. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 5. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 6. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 7. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Longevity/code/GSE44147.py

@@ -0,0 +1,212 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Longevity"
+cohort = "GSE44147"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Longevity"
+in_cohort_dir = "../DATA/GEO/Longevity/GSE44147"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Longevity/GSE44147.csv"
+out_gene_data_file = "./output/preprocess/1/Longevity/gene_data/GSE44147.csv"
+out_clinical_data_file = "./output/preprocess/1/Longevity/clinical_data/GSE44147.csv"
+json_path = "./output/preprocess/1/Longevity/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset uses Affymetrix Gene ST arrays, so it likely has gene expression data.
+
+# 2.1 & 2.2 Variable Availability and Data Type Conversion
+trait_row = None   # No row for longevity/trait in the dictionary
+age_row = 2        # Multiple distinct values under row 2 (ex: "age: 2 days", "age: 5 days", ...)
+gender_row = None  # No mention of gender in the dictionary
+
+def convert_trait(x: str):
+    # No trait data available
+    return None
+
+def convert_age(x: str):
+    """
+    Convert 'age: X days' -> float(X).
+    Unknown formats return None.
+    """
+    try:
+        # Get the substring after the first colon
+        val_str = x.split(':', 1)[1].strip()  # e.g. "2 days"
+        # Look for a numeric value
+        match = re.search(r'(\d+)', val_str)
+        if match:
+            return float(match.group(1))
+        return None
+    except:
+        return None
+
+def convert_gender(x: str):
+    # No gender data available
+    return None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+should_continue_preprocessing = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step because trait_row is None (no clinical trait data available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These numeric values appear to be probe IDs rather than standard human gene symbols.
+# Hence, gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+import re
+
+# STEP: Gene Identifier Mapping
+
+# We will refine the "extract_human_gene_symbols" logic to avoid picking up partial tokens like "A-".
+# Instead of switching columns, we keep using "gene_assignment" but parse it more restrictively.
+
+# 1) Define a refined extraction function with a stricter regex, so short fragments like "A-" won't match.
+def extract_human_gene_symbols_refined(text: str):
+    """
+    Refine the pattern to pick up valid uppercase gene symbols (e.g., DDX11L2) while avoiding partial tokens.
+    """
+    if not isinstance(text, str):
+        return []
+    pattern = (
+        r"\b"
+        r"(?!NR_|XR_|LOC\d+|LINC\d+)"       # Exclude certain prefixes
+        r"(?:[A-Z][A-Z0-9]{1,8}|C\d+orf\d+)" # A stricter pattern: 2-9 alphanumeric chars, or 'C\d+orf\d+'
+        r"\b"
+    )
+    candidates = re.findall(pattern, text)
+    # Exclude trivial lab terms if needed (e.g., "DNA","RNA").
+    exclude_simple = {"DNA", "RNA", "PCR", "EST", "CHR"}
+    return [x for x in candidates if x not in exclude_simple]
+
+# 2) Wrap the apply_gene_mapping function call but override the "Gene" extraction step to use our refined extractor.
+def apply_gene_mapping_refined(expression_df, mapping_df):
+    # Filter mapping to only those probes present in expression_df
+    mapping_df = mapping_df[mapping_df['ID'].isin(expression_df.index)].copy()
+    # Extract refined gene symbols
+    mapping_df['Gene'] = mapping_df['Gene'].apply(extract_human_gene_symbols_refined)
+    # Count number of genes per probe
+    mapping_df['num_genes'] = mapping_df['Gene'].apply(len)
+    # Expand if a probe maps to multiple genes
+    mapping_df = mapping_df.explode('Gene')
+    mapping_df = mapping_df.dropna(subset=['Gene'])
+    mapping_df.set_index('ID', inplace=True)
+
+    # Distribute expression values equally among the mapped genes, then sum by gene
+    merged_df = mapping_df.join(expression_df)
+    expr_cols = [col for col in merged_df.columns if col not in ['Gene', 'num_genes']]
+    merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)
+    gene_expression_df = merged_df.groupby('Gene')[expr_cols].sum()
+    return gene_expression_df
+
+# --- Main logic starts here ---
+
+# 1 & 2. Identify the columns in the gene annotation for probe IDs and gene symbols,
+#    then create the mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")
+
+# 3. Convert probe-level measurements into gene-level expression using the refined function.
+gene_data = apply_gene_mapping_refined(gene_data, mapping_df)
+
+# Optional: View the resulting gene_data structure
+print("Mapped gene_data dimensions:", gene_data.shape)
+print("First 10 mapped gene symbols:", list(gene_data.index[:10]))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Longevity/code/GSE48264.py

@@ -0,0 +1,205 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Longevity"
+cohort = "GSE48264"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Longevity"
+in_cohort_dir = "../DATA/GEO/Longevity/GSE48264"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Longevity/GSE48264.csv"
+out_gene_data_file = "./output/preprocess/1/Longevity/gene_data/GSE48264.csv"
+out_clinical_data_file = "./output/preprocess/1/Longevity/clinical_data/GSE48264.csv"
+json_path = "./output/preprocess/1/Longevity/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 the dataset likely has gene expression data.
+#    From the background info, they used Affymetrix gene-chips, so it has gene expression data.
+is_gene_available = True
+
+# 2.1 Identify data availability for trait, age, and gender.
+#    - Trait ("Longevity") can be inferred from row 3, which has unique values: "None", "Hosp", "Death".
+#      These are not constant, so trait_row = 3.
+#    - Age data appears to be row 1 ("age(approx): 70 yr"), but since everyone is ~70 (only one unique value),
+#      it's effectively constant => not useful. age_row = None.
+#    - Gender is not explicitly listed, but from the study background, all subjects were men (no variation),
+#      so gender_row = None.
+
+trait_row = 3
+age_row = None
+gender_row = None
+
+# 2.2 Write conversion functions for the variables.
+def convert_trait(value: str):
+    """
+    Convert the 'survival' field to a binary variable.
+    'Death' -> 1, 'Hosp' or 'None' -> 0, otherwise -> None
+    """
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "death":
+        return 1
+    elif val in ["none", "hosp"]:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    """
+    Since age data is effectively constant (~70 yr), 
+    we won't use it. But here's a placeholder that attempts
+    to parse numerical age if needed.
+    """
+    return None  # Ignored because age_row is None
+
+def convert_gender(value: str):
+    """
+    All subjects are men (constant), so there's no variation.
+    Return None for demonstration.
+    """
+    return None
+
+# 3. Conduct initial filtering using 'validate_and_save_cohort_info'.
+#    Trait data is considered available if trait_row is not None.
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. If trait_row is not None, extract clinical features and preview/save the clinical DataFrame.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview)
+
+    # Save the clinical DataFrame
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric pattern (e.g., '2315251'), they are not standard human gene symbols.
+# They appear to be probe IDs that require mapping to gene symbols.
+print("They appear to be non-standard numeric IDs from a microarray platform.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Determine the columns in the annotation dataframe that correspond to the probe identifiers and the gene symbols.
+#    From inspection, the "ID" column matches the probe IDs (same format as in gene_data.index),
+#    and "gene_assignment" contains strings from which we can parse gene symbols.
+
+# 2. Get the gene mapping dataframe using the get_gene_mapping function.
+#    We specify prob_col='ID' and gene_col='gene_assignment'.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',
+    gene_col='gene_assignment'
+)
+
+# 3. Apply the mapping to convert probe-level data to gene-level data.
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Longevity/code/TCGA.py

@@ -0,0 +1,70 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Longevity"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Longevity/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Longevity/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Longevity/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Longevity/cohort_info.json"
+
+import os
+import pandas as pd
+
+# List of subdirectories provided in the instructions:
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Possible synonyms or related terms for "Longevity"
+longevity_synonyms = ["longevity", "long_life", "lifespan"]
+
+selected_subdirectory = None
+for subdir in subdirectories:
+    subdir_lower = subdir.lower()
+    if any(syn in subdir_lower for syn in longevity_synonyms):
+        selected_subdirectory = subdir
+        break
+
+if not selected_subdirectory:
+    # If no matching directory is found, mark dataset as unavailable
+    is_final = False
+    is_gene_available = False
+    is_trait_available = False
+    _ = validate_and_save_cohort_info(
+        is_final=is_final,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+    print(f"No suitable directory found for '{trait}'. Skipped this trait.")
+else:
+    # Step 2: Identify clinicalMatrix file and PANCAN file
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as dataframes
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # Step 4: Print the column names of the clinical data
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))

p1/preprocess/Longevity/cohort_info.json

@@ -0,0 +1 @@
+{"GSE48264": {"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": 108, "note": "Completed trait-based preprocessing."}, "GSE44147": {"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; dataset not usable for trait analysis."}, "GSE16717": {"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": "Completed trait-based preprocessing."}, "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/Longevity/gene_data/GSE16717.csv

@@ -0,0 +1 @@
+Gene,GSM418770,GSM418771,GSM418772,GSM418773,GSM418774,GSM418775,GSM418776,GSM418777,GSM418778,GSM418779,GSM418780,GSM418781,GSM418782,GSM418783,GSM418784,GSM418785,GSM418786,GSM418787,GSM418788,GSM418789,GSM418790,GSM418791,GSM418792,GSM418793,GSM418794,GSM418795,GSM418796,GSM418797,GSM418798,GSM418799,GSM418800,GSM418801,GSM418802,GSM418803,GSM418804,GSM418805,GSM418806,GSM418807,GSM418808,GSM418809,GSM418810,GSM418811,GSM418812,GSM418813,GSM418814,GSM418815,GSM418816,GSM418817,GSM418818,GSM418819,GSM418820,GSM418821,GSM418822,GSM418823,GSM418824,GSM418825,GSM418826,GSM418827,GSM418828,GSM418829,GSM418830,GSM418831,GSM418832,GSM418833,GSM418834,GSM418835,GSM418836,GSM418837,GSM418838,GSM418839,GSM418840,GSM418841,GSM418842,GSM418843,GSM418844,GSM418845,GSM418846,GSM418847,GSM418848,GSM418849,GSM418850,GSM418851,GSM418852,GSM418853,GSM418854,GSM418855,GSM418856,GSM418857,GSM418858,GSM418859,GSM418860,GSM418861,GSM418862,GSM418863,GSM418864,GSM418865,GSM418866,GSM418867,GSM418868,GSM418869,GSM418870,GSM418871,GSM418872,GSM418873,GSM418874,GSM418875,GSM418876,GSM418877,GSM418878,GSM418879,GSM418880,GSM418881,GSM418882,GSM418883,GSM418884,GSM418885,GSM418886,GSM418887,GSM418888,GSM418889,GSM418890,GSM418891,GSM418892,GSM418893,GSM418894,GSM418895,GSM418896,GSM418897,GSM418898,GSM418899,GSM418900,GSM418901,GSM418902,GSM418903,GSM418904,GSM418905,GSM418906,GSM418907,GSM418908,GSM418909,GSM418910,GSM418911,GSM418912,GSM418913,GSM418914,GSM418915,GSM418916,GSM418917,GSM418918,GSM418919

p1/preprocess/Longevity/gene_data/GSE48264.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97e26e70c844e19c2302a4083e91376c2addf302fddfee387f4b506b50e6615e
+size 24532979

p1/preprocess/Lower_Grade_Glioma/clinical_data/GSE24072.csv

@@ -0,0 +1,4 @@
+GSM590744,GSM590745,GSM590746,GSM590747,GSM590748,GSM591700,GSM591701,GSM591702,GSM591703,GSM591704,GSM591705,GSM591706,GSM591707,GSM591722,GSM591792,GSM591793,GSM591796,GSM591798,GSM591800,GSM591808,GSM591809,GSM591811,GSM591812,GSM591815,GSM591817,GSM591818,GSM591819,GSM591829,GSM591830,GSM591831,GSM591832,GSM591833
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