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p1/preprocess/Retinoblastoma/code/GSE63529.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+cohort = "GSE63529"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Retinoblastoma"
+in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE63529"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Retinoblastoma/GSE63529.csv"
+out_gene_data_file = "./output/preprocess/1/Retinoblastoma/gene_data/GSE63529.csv"
+out_clinical_data_file = "./output/preprocess/1/Retinoblastoma/clinical_data/GSE63529.csv"
+json_path = "./output/preprocess/1/Retinoblastoma/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 actual gene expression profiling.
+
+# 2) Variable Availability
+# From the sample characteristics dictionary, there's no indication of:
+# - Retinoblastoma trait status,
+# - Age,
+# - Gender.
+# Hence we set all row indices to None.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str):
+    # No actual trait data is available, return None
+    return None
+
+def convert_age(value: str):
+    # No age data is available, return None
+    return None
+
+def convert_gender(value: str):
+    # No gender data is available, return None
+    return None
+
+# 3) Save Metadata (Initial Filtering)
+# trait data availability can be determined by trait_row
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Since trait_row is None, we skip this step.
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("Based on the presented identifiers (e.g. ILMN_1343291), they appear to be Illumina probe IDs.\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Determine the matching columns in gene_annotation that map to the gene expression data's probe IDs ("ID")
+#    and the actual gene symbol ("Symbol").
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 2) Apply the mapping to convert probe-level expression data into gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Retinoblastoma/code/GSE68950.py

@@ -0,0 +1,67 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+cohort = "GSE68950"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Retinoblastoma"
+in_cohort_dir = "../DATA/GEO/Retinoblastoma/GSE68950"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Retinoblastoma/GSE68950.csv"
+out_gene_data_file = "./output/preprocess/1/Retinoblastoma/gene_data/GSE68950.csv"
+out_clinical_data_file = "./output/preprocess/1/Retinoblastoma/clinical_data/GSE68950.csv"
+json_path = "./output/preprocess/1/Retinoblastoma/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  # Based on "Assay Type: Gene Expression" and "Provider: Affymetrix"
+
+# 2. Identify rows for trait, age, and gender in the sample characteristics dictionary
+#    Since "Retinoblastoma" is not found among the unique disease states, and no age/gender info is present, set them to None
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    return None  # Trait data is not available
+
+def convert_age(value: str) -> Optional[float]:
+    return None  # Age data is not available
+
+def convert_gender(value: str) -> Optional[int]:
+    return None  # Gender data is not available
+
+# 3. Conduct initial filtering: trait data is considered unavailable if trait_row is None
+is_trait_available = (trait_row is not None)
+
+# Save metadata with the initial filtering
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction is skipped because trait_row is None

p1/preprocess/Retinoblastoma/code/TCGA.py

@@ -0,0 +1,72 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Retinoblastoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Retinoblastoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Retinoblastoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Retinoblastoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Retinoblastoma/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 the trait "Retinoblastoma"
+trait_synonyms = ["retinoblastoma", "rb"]
+
+selected_subdirectory = None
+for subdir in subdirectories:
+    if subdir.lower() in ['crawldata.ipynb', '.ds_store']:
+        continue
+    subdir_lower = subdir.lower()
+    if any(syn in subdir_lower for syn in trait_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/Retinoblastoma/gene_data/GSE110811.csv

@@ -0,0 +1 @@
+Gene,GSM3017123,GSM3017124,GSM3017125,GSM3017126,GSM3017127,GSM3017128,GSM3017129,GSM3017130,GSM3017131,GSM3017132,GSM3017133,GSM3017134,GSM3017135,GSM3017136,GSM3017137,GSM3017138,GSM3017139,GSM3017140,GSM3017141,GSM3017142,GSM3017143,GSM3017144,GSM3017145,GSM3017146,GSM3017147,GSM3017148,GSM3017149,GSM3017150,GSM3017151,GSM3017152,GSM3017153,GSM3017154,GSM3017155,GSM3017156

p1/preprocess/Retinoblastoma/gene_data/GSE58780.csv

@@ -0,0 +1,14 @@
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p1/preprocess/Rheumatoid_Arthritis/clinical_data/GSE121894.csv

@@ -0,0 +1,2 @@
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p1/preprocess/Rheumatoid_Arthritis/clinical_data/GSE236924.csv

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p1/preprocess/Rheumatoid_Arthritis/code/GSE121894.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE121894"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE121894"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE121894.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE121894.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE121894.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine Gene Expression Data Availability
+is_gene_available = True  # Based on the series description mentioning "Gene expression profile"
+
+# Step 2: Identify Variable Availability and Define Conversion Functions
+
+# 2.1 Identify keys for trait, age, gender
+# Observing the sample characteristics, row 0 has "subject status" with two unique values.
+# That matches our trait of interest (RA vs. Healthy control), so we set trait_row=0.
+trait_row = 0
+# There's no mention of age or gender in the sample characteristics, set them to None.
+age_row = None
+gender_row = None
+
+# 2.2 Define conversion functions
+
+def convert_trait(value: str):
+    """
+    Convert subject status to a binary variable: 
+      Rheumatoid arthritis -> 1
+      Healthy control      -> 0
+      Unknown or Other     -> None
+    """
+    parts = value.split(":")
+    val_str = parts[1].strip().lower() if len(parts) > 1 else value.lower()
+    if "rheumatoid" in val_str:
+        return 1
+    elif "healthy" in val_str:
+        return 0
+    return None
+
+def convert_age(value: str):
+    """
+    No age data available, return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    No gender data available, return None.
+    """
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    # Assume 'clinical_data' DataFrame is available in this environment (previously loaded).
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted clinical features
+    print("Preview of selected clinical features:", preview_df(selected_clinical_df, n=5))
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the given identifiers (e.g., '10000_at', '10001_at', etc.), these appear to be Affymetrix probe set IDs.
+# They are not standard human gene symbols, so they need to be mapped to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Based on the preview of the annotation, the column "ID" matches the probe IDs in our gene expression data,
+#    and "Description" includes the gene information (which will be further parsed to extract gene symbols).
+# 2. Extract the mapping data using these two columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')
+
+# 3. Convert probe-level measurements to gene-level measurements by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a small preview to verify the resulting gene_data.
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data preview:\n", gene_data.head())
+# STEP 7
+
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. Read back the clinical data, reassign its single row index to the trait name, and link with genetic data.
+selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+selected_clinical_df.index = [trait]  # Ensure the clinical row is labeled by the trait
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically.
+df = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+# 5. Perform final validation with full dataset information.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=df,
+    note="Final step with linking, missing-value handling, bias checks."
+)
+
+# 6. If the data is usable, save the final linked data.
+if is_usable:
+    df.to_csv(out_data_file)
+    print(f"Final linked data saved to: {out_data_file}")
+else:
+    print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Rheumatoid_Arthritis/code/GSE140161.py

@@ -0,0 +1,96 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE140161"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE140161"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE140161.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE140161.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE140161.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/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 pandas as pd
+from typing import Optional, Any
+
+# 1. Gene Expression Data Availability
+# From the background, the dataset uses an Affymetrix chip for whole blood transcriptome,
+# so it likely contains gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Sample characteristics dictionary indicates:
+#   0: [ 'tissue: Whole blood' ]
+#   1: [ 'Sex: female', 'Sex: male' ]
+#   2: [ 'antissa status: Positive', 'antissa status: Negative' ]
+#   3: [ 'antissb status: Negative', 'antissb status: Positive' ]
+#   4: [ 'disease state: Sjögren’s syndrome' ]
+#
+# We are interested in "Rheumatoid_Arthritis" (trait), "age", and "gender":
+# - Trait: Not found (all have disease state: Sjögren’s). There's only one unique value,
+#          so treat it as not available.
+# - Age:   Not found in the dictionary.
+# - Gender: Found in row 1 with two unique values ("female", "male").
+
+trait_row = None
+age_row = None
+gender_row = 1
+
+def convert_trait(value: Any) -> Optional[float]:
+    # No trait data is actually available, so this function is just a placeholder.
+    return None
+
+def convert_age(value: Any) -> Optional[float]:
+    # Age data is not available, placeholder function.
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # Extract the raw value after the colon
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()  # e.g. "female", "male"
+    if gender_str == 'female':
+        return 0
+    elif gender_str == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata with initial filtering
+# Trait data is not available (trait_row=None), so is_trait_available=False.
+is_trait_available = False
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip the extraction step.
+# (No code needed here for extraction, per instructions.)

p1/preprocess/Rheumatoid_Arthritis/code/GSE143153.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE143153"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE143153"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE143153.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE143153.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE143153.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/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 info, this dataset uses Agilent Whole Human Genome arrays,
+# so we set is_gene_available to True.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# The dataset is about "Primary SS" vs "non-SS" and does not appear to provide data for "Rheumatoid_Arthritis".
+# So we set the trait_row to None (trait not available).
+
+trait_row = None
+
+# For age, row 2 presents multiple unique age values like 'age: 56', 'age: 51', etc.
+# => age is available at row 2
+age_row = 2
+
+# For gender (sex), row 3 presents 'Sex: M' and 'Sex: F'.
+# => gender is available at row 3
+gender_row = 3
+
+# We define conversion functions to extract values from the string after the colon and convert to the desired type.
+
+def convert_trait(x: str):
+    """
+    Since we don't have RA data in this dataset, the function will return None.
+    Provided here just for completeness.
+    """
+    return None
+
+def convert_age(x: str):
+    """
+    Extract the substring after the colon and convert to float.
+    If conversion fails, return None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(x: str):
+    """
+    Extract the substring after the colon and convert to 0/1.
+    'F' -> 0, 'M' -> 1. Otherwise return None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().upper()
+    if val_str == 'F':
+        return 0
+    elif val_str == 'M':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata
+# Trait data availability is determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+# Perform initial filtering and save info.
+# If not is_final step, we do not need 'df' or 'is_biased'.
+# This will return a boolean indicating whether the dataset passes the initial filter.
+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 proceed if trait_row is not None. Here, trait_row is None, so we skip.
+# 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 gene identifiers: "1", "2", ..., "20"
+# These are purely numeric and not standard gene symbols.
+print("\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. From the annotation preview, we see that both the gene expression DataFrame and the annotation DataFrame
+#    share the "ID" column. The "GeneName" column appears to store gene symbols.
+# 2. Get a gene mapping DataFrame using 'ID' as the probe identifier column and 'GeneName' as the gene symbol column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GeneName")
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, we print the shape and a portion of the index.
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 10 gene symbols in mapped gene_data index:")
+print(gene_data.index[:10].tolist())
+# STEP 7
+
+import os
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Since in Step 2 we found trait_row = None, we determined there is no RA trait data available.
+# Without trait data, we cannot proceed with final linking or downstream analysis.
+
+# 2. Mark the dataset as not usable at the initial stage (is_final=False),
+#    because it fails the trait availability check.
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False
+)
+
+# 3. Since trait data is unavailable, we cannot do any final preprocessing or save final linked data.
+if not is_usable:
+    print("Dataset lacks trait data; cannot proceed with linking or final analysis. No final data saved.")

p1/preprocess/Rheumatoid_Arthritis/code/GSE176440.py

@@ -0,0 +1,126 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE176440"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE176440"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE176440.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE176440.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE176440.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/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 Microarray indication
+
+# 2. Determine availability of trait, age, and gender data
+#    Inspecting the sample characteristics dictionary, it appears that:
+#    - Row 1 has only one unique value: "disease state: rheumatoid arthritis patient"
+#      which is constant (not varying). So it is considered not available for analysis.
+#    - No other rows seem to contain relevant trait, age, or gender data.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(x: str) -> int:
+    # Placeholder. This function won't be used since trait_row is None
+    return 1 if 'rheumatoid arthritis' in x.lower() else None
+
+def convert_age(x: str) -> float:
+    # Placeholder. This function won't be used since age_row is None
+    return None
+
+def convert_gender(x: str) -> int:
+    # Placeholder. This function won't be used since gender_row is None
+    # If data were available, we would convert female->0, male->1
+    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. Since trait_row is None, skip clinical feature extraction
+#    (No code needed for extraction as per instructions)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided identifiers (e.g. "A_23_P100001", "A_23_P100011"), these appear to be array probe IDs rather than standard human gene symbols.
+# Therefore, gene mapping to standard gene symbols is required.
+
+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. Decide which columns store the probe IDs and which store the gene symbols, and obtain the mapping dataframe.
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Convert probe-level measurements to gene expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+# STEP 7
+
+import pandas as pd
+
+# Since trait_row was None in a previous step, there is no trait data available.
+# Hence, there's no clinical CSV to read or link. We'll finalize the dataset as not usable.
+is_trait_available = False
+
+# We do have gene data, so is_gene_available is True.
+# With no trait data, the dataset is not usable for our analysis.
+
+from tools.preprocess import validate_and_save_cohort_info
+import os
+
+# Final validation: mark dataset as unavailable for trait-based analysis.
+# We pass an empty DataFrame for df just to complete the function's parameters.
+# We must provide a boolean for is_biased when is_final=True:
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=is_trait_available,
+    is_biased=False,
+    df=pd.DataFrame(),
+    note="No trait data available to support further analysis."
+)
+
+print("Dataset is not usable due to missing trait data. No final data saved.")

p1/preprocess/Rheumatoid_Arthritis/code/GSE186963.py

@@ -0,0 +1,69 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE186963"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE186963"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE186963.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE186963.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE186963.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+# "Whole blood gene expression" suggests this dataset indeed contains gene expression data.
+is_gene_available = True
+
+# Step 2: Determine data availability for trait, age, and gender
+# The dataset is about Crohn's disease, not Rheumatoid_Arthritis. There's no mention of age or gender.
+# Hence, all these rows are unavailable because they do not match our trait of interest
+# ("Rheumatoid_Arthritis"), nor do we see valid age or gender columns.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define the data conversion functions
+# Since the dataset does not provide corresponding fields, these functions will return None.
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# Step 3: Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Skip clinical feature extraction because trait_row is None

p1/preprocess/Rheumatoid_Arthritis/code/GSE224330.py

@@ -0,0 +1,107 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE224330"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE224330"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE224330.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE224330.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE224330.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/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  # Background info indicates "Gene expression" data.
+
+# 2. Variable Availability and Conversion Setup
+# 2.1 Identify rows for trait, age, gender
+trait_row = None  # No row with explicit or inferable RA/healthy data.
+age_row = 1       # Row 1 contains multiple distinct age values.
+gender_row = 2    # Row 2 contains female/male info.
+
+# 2.2 Data Type Conversions
+def convert_age(value: str) -> Optional[float]:
+    # Example string: "age: 63y"
+    try:
+        # Split at ":", take second part, strip, remove trailing 'y'
+        val = value.split(":", 1)[1].strip().replace("y", "")
+        return float(val)
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # Example string: "gender: female" or "gender: male"
+    try:
+        val = value.split(":", 1)[1].strip().lower()
+        if val == "female":
+            return 0
+        elif val == "male":
+            return 1
+        else:
+            return None
+    except:
+        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 (skip if trait_row is None)
+# Since trait_row is None, we do not extract or save clinical data.
+# 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 IDs appear to be microarray probe identifiers (e.g., Agilent probes), not standard human gene symbols.
+# Hence, gene mapping is required.
+
+print("These identifiers are microarray probe IDs that need to be mapped to gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. In the gene annotation, the "ID" column corresponds to the probe identifiers,
+#    and the "GENE_SYMBOL" column stores human gene symbols.
+
+# 2. Extract the gene mapping dataframe from gene_annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Convert the probe-level data in gene_data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)

p1/preprocess/Rheumatoid_Arthritis/code/GSE224842.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE224842"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE224842"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE224842.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE224842.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE224842.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# From the series title and summary, it indicates "DNA microarray analyses" for gene expression in PBMCs.
+# Thus, we consider gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary:
+# {0: ['disease state: rheumatoid arthritis'], 1: ['cell type: PBMC']}
+# There is no variation for "disease state" (all are rheumatoid arthritis); age and gender are not provided.
+# Hence, we treat all three as not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions (though they won't be used here because all rows are None).
+def convert_trait(value: str):
+    """
+    Convert trait string to binary/continuous value.
+    This function splits at the colon (if present) and tries to interpret the resulting value.
+    Unknown or non-matching values are converted to None.
+    """
+    parts = value.split(':', 1)
+    extracted = parts[1].strip().lower() if len(parts) > 1 else value.strip().lower()
+
+    if 'rheumatoid arthritis' in extracted:
+        return 1
+    elif 'healthy' in extracted:
+        return 0
+    return None
+
+def convert_age(value: str):
+    """
+    Convert age string to a continuous value (float).
+    Splits at the colon, then tries to parse as float. Unknown values => None.
+    """
+    parts = value.split(':', 1)
+    extracted = parts[1].strip() if len(parts) > 1 else value.strip()
+    try:
+        return float(extracted)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender string to binary (female=0, male=1).
+    Splits at the colon, interprets the result, unknown => None.
+    """
+    parts = value.split(':', 1)
+    extracted = parts[1].strip().lower() if len(parts) > 1 else value.strip().lower()
+    if extracted in ['female', 'f']:
+        return 0
+    elif extracted in ['male', 'm']:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,            # "GSE224842"
+    info_path=json_path,      # "./output/preprocess/1/Rheumatoid_Arthritis/cohort_info.json"
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, skip extracting clinical features.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., A_23_P100001), they appear to be microarray probe IDs (not standard gene symbols).
+# Hence, gene mapping to standardized gene symbols is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene annotation that correspond to the probe ID and the gene symbol
+#    From the preview, "ID" matches the probe identifiers in gene_data, and "GENE_SYMBOL" is the gene symbol.
+
+# 2. Create a mapping dataframe from "ID" to "GENE_SYMBOL"
+gene_mapping = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 3. Apply the mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+# Quick check of the new gene_data structure
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("First 5 gene symbols:", gene_data.index[:5])
+# STEP 7
+
+import os
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Since trait_row was determined to be None in Step 2 (indicating no clinical trait data),
+# we cannot produce a final linked DataFrame or perform a final validation that requires df and is_biased.
+# Instead, we perform partial validation to record that trait data is unavailable.
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,     # We do have gene expression data
+    is_trait_available=False    # No trait data
+)
+
+print("No trait data available. Skipping linking, missing-value handling, and final validation.")

p1/preprocess/Rheumatoid_Arthritis/code/GSE236924.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE236924"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE236924"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE236924.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE236924.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE236924.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Gene expression data availability
+is_gene_available = True  # Based on "array" in the series title and summary, it's likely gene expression
+
+# Step 2.1: Variable availability
+# According to the sample characteristics dictionary,
+# {0: ['disease: OA', 'disease: Control', 'disease: RA']}
+# we see that row 0 records disease states, including RA. So that can be used for the trait.
+trait_row = 0
+
+# No information about age or gender is provided, so they are not available
+age_row = None
+gender_row = None
+
+# Step 2.2: Data type conversion
+def convert_trait(x: str):
+    # Extract the substring after the colon
+    parts = x.split(":")
+    val = parts[-1].strip().lower() if len(parts) > 1 else None
+    if val is None:
+        return None
+
+    # Convert "ra" to 1, else 0 (for OA or Control)
+    if val == 'ra':
+        return 1
+    elif val in ['oa', 'control']:
+        return 0
+    return None
+
+def convert_age(x: str):
+    # No age data is available. Return None for all inputs.
+    return None
+
+def convert_gender(x: str):
+    # No gender data is available. Return None for all inputs.
+    return None
+
+# Step 3: Save metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (only if trait_row is not None)
+if trait_row is not None:
+    clinical_features_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical features
+    preview_data = preview_df(clinical_features_df)
+    print("Preview of clinical features:", preview_data)
+
+    # Save the clinical data
+    clinical_features_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the gene identifiers in the expression data, they appear to be Affymetrix probe set IDs.
+# Hence, they are not standard gene symbols and require mapping.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns that match the gene expression identifiers and the gene symbols respectively.
+#    From the annotation preview, the "ID" column matches the probe identifiers in gene_data.
+#    And the "Gene Symbol" column stores the gene symbols.
+
+# 2. Extract the mapping dataframe using the get_gene_mapping method.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level data to gene-level data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7
+
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. Read back the clinical data, reassign its single row index to the trait name, and link with genetic data.
+selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+selected_clinical_df.index = [trait]  # Ensure the clinical row is labeled by the trait
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically.
+df = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+# 5. Perform final validation with full dataset information.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=df,
+    note="Final step with linking, missing-value handling, bias checks."
+)
+
+# 6. If the data is usable, save the final linked data.
+if is_usable:
+    df.to_csv(out_data_file)
+    print(f"Final linked data saved to: {out_data_file}")
+else:
+    print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Rheumatoid_Arthritis/code/GSE42842.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE42842"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE42842"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE42842.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE42842.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE42842.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/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 background info, this dataset does use gene expression arrays.
+
+# 2) Identify rows for trait, age, and gender in the sample characteristics
+#    and define their data-conversion functions.
+
+# From the provided dictionary:
+#   0 -> ['gender: M', 'gender: F']
+#   1 -> ['cell type: PBMC']
+#   2 -> ['disease state: rheumatoid arthritis']
+#   3 -> ['treatment: methotrexate +  adalimumab', 'treatment: methotrexate + etanercept']
+#   4 -> ['efficacy: moderate response', 'efficacy: response']
+
+# The "trait" here is rheumatoid arthritis, but the dictionary has only one unique value in row 2.
+# This is not useful for association analysis, so we mark trait_row as None (not available).
+trait_row = None
+
+# No age data is found, so age_row is None.
+age_row = None
+
+# We do have gender data (M/F) in row 0, which varies, so gender_row = 0.
+gender_row = 0
+
+# Define conversion functions. Even if trait or age are not available, we still define them to keep consistent signatures.
+def convert_trait(raw_value: str):
+    """
+    This function would convert RA data if we had it.
+    But trait_row = None, so this is unused here.
+    """
+    return None
+
+def convert_age(raw_value: str):
+    """
+    Age not found in the dataset, so no actual conversion.
+    """
+    return None
+
+def convert_gender(raw_value: str):
+    """
+    Convert 'gender' values to a binary variable: F -> 0, M -> 1.
+    If unknown, return None.
+    """
+    # Split at the colon, take the latter part, strip spaces.
+    parts = raw_value.split(':')
+    if len(parts) > 1:
+        val = parts[1].strip().upper()
+        if val in ['F', 'FEMALE']:
+            return 0
+        elif val in ['M', 'MALE']:
+            return 1
+    return None
+
+# 3) Conduct initial filtering and save metadata using validate_and_save_cohort_info
+#    Trait availability depends on whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Because trait_row is None, we skip the clinical feature extraction step.
+#    (No code needed here, as there is no available trait data.)
+# 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("These gene identifiers are numeric, so they are not standard human gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. We identified that the "ID" column in the gene annotation DataFrame corresponds to the numerically labeled
+#    probes in our gene_data, and "GENE_SYMBOL" holds the actual gene symbols (though not visible in the
+#    first few rows).
+
+# 2. Extract the relevant columns for gene mapping
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 3. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7
+
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2) Since the trait is not available (trait_row is None from Step 2),
+#    we skip linking or trait-based processing. However, we still need to 
+#    finalize metadata to accurately record dataset status.
+print("Trait data is not available. Skipping clinical linking, missing-value handling, and bias checks.")
+
+# Prepare an empty placeholder DataFrame for final validation (required arguments).
+dummy_df = pd.DataFrame()
+
+# Final validation to mark that trait data is not available
+is_biased = False  # Arbitrarily set; won't matter since trait is unavailable
+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=dummy_df,
+    note="No trait data available for this cohort."
+)
+
+print("Dataset status recorded. No final merged data saved due to missing trait.")

p1/preprocess/Rheumatoid_Arthritis/code/GSE97475.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+cohort = "GSE97475"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
+in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE97475"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/GSE97475.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/GSE97475.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/GSE97475.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The series summary explicitly mentions microarray (transcriptomic) data
+
+# 2. Variable Availability and Data Type Conversion
+
+# After reviewing the sample characteristics dictionary, there is no row
+# indicating distinct Rheumatoid Arthritis vs. control status. Hence trait is unavailable.
+trait_row = None
+
+# For age, row 81 provides multiple distinct numeric entries.
+age_row = 81
+
+# For gender, row 118 provides 'Male' and 'Female'.
+gender_row = 118
+
+# 2.2 Define conversion functions
+
+def convert_trait(x: str) -> int:
+    """
+    Since trait data is not found in this dataset,
+    this function is defined but won't be used.
+    """
+    return None
+
+def convert_age(x: str) -> float:
+    """
+    Convert age string like 'subjects.demographics.age: 60' -> float(60).
+    If it fails, return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(x: str) -> int:
+    """
+    Convert gender string like 'subjects.demographics.sex: Male' -> 1
+    and 'subjects.demographics.sex: Female' -> 0.
+    If it fails, return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'male':
+        return 1
+    elif val == 'female':
+        return 0
+    return None
+
+# 3. Initial Filtering and Saving Metadata
+# trait data availability depends on whether trait_row is None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("The gene identifiers appear to be standard human gene symbols.")
+print("requires_gene_mapping = False")
+# STEP 5
+
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Since we determined trait_row is None earlier, trait data is unavailable (is_trait_available=False).
+# We cannot do a final validation that requires df and is_biased.
+# Instead, we record that the dataset fails due to missing trait data, and skip linking/analysis.
+
+is_trait_available = False
+
+if not is_trait_available:
+    # Record that the dataset is missing trait data without doing final validation.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+    print("Trait data not available => dataset not suitable for trait-based analysis. No final data saved.")
+else:
+    # If, hypothetically, trait data existed, we'd link and finalize. Skipped here.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    df = handle_missing_values(linked_data, trait)
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=df,
+        note="Final step with linking, missing-value handling, bias checks."
+    )
+
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or is severely biased. No final data saved.")

p1/preprocess/Rheumatoid_Arthritis/code/TCGA.py

@@ -0,0 +1,61 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Rheumatoid_Arthritis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Rheumatoid_Arthritis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Rheumatoid_Arthritis/cohort_info.json"
+
+import os
+
+# Step 1: Identify subdirectory that might relate to our trait "Rheumatoid_Arthritis"
+subdirs = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+suitable_subdir = None
+
+# Look for "rheumatoid" in subdirectories
+for sd in subdirs:
+    if "rheumatoid" in sd.lower():
+        suitable_subdir = sd
+        break
+
+if not suitable_subdir:
+    print("No suitable subdirectory found for trait 'Rheumatoid_Arthritis'. Skipping this trait.")
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Step 2: Identify clinical and genetic file paths
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, suitable_subdir))
+
+    # Step 3: Load data into dataframes
+    clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
+
+    # Step 4: Print clinical data columns
+    print("Clinical Data Columns:", clinical_df.columns.tolist())

p1/preprocess/Rheumatoid_Arthritis/cohort_info.json

@@ -0,0 +1 @@
+{"GSE97475": {"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}, "GSE42842": {"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 available for this cohort."}, "GSE236924": {"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": 132, "note": "Final step with linking, missing-value handling, bias checks."}, "GSE224842": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE224330": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE186963": {"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}, "GSE176440": {"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 available to support further analysis."}, "GSE143153": {"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}, "GSE140161": {"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}, "GSE121894": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 58, "note": "Final step with linking, missing-value handling, bias checks."}, "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/Rheumatoid_Arthritis/gene_data/GSE176440.csv

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+oid sha256:b3f50961527552fc1068262f086379dad753130d10b73cd5496018ede9f9fbc0
+size 12288669

p1/preprocess/Sarcoma/clinical_data/GSE197147.csv

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p1/preprocess/Sarcoma/code/GSE118336.py

@@ -0,0 +1,251 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE118336"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE118336"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE118336.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE118336.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE118336.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step: Dataset Analysis and Clinical Feature Extraction
+
+# 1. Gene Expression Data Availability
+#    The Series title indicates "HTA2.0 (human transcriptome array) analysis", which suggests
+#    actual gene expression data is available (and not simply miRNA or methylation data).
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+#    2.1 Identify keys in the sample characteristics for each variable:
+trait_row = None          # No mention of 'Sarcoma' or a relevant disease key in the dictionary.
+age_row = None            # No age information found.
+gender_row = None         # No gender information found.
+
+#    2.2 Define data-type conversion functions. Even though data is not available,
+#        we provide them as placeholders.
+
+def convert_trait(x: str) -> int:
+    # Not used because trait_row is None, but here's a placeholder function.
+    # Convert to 'binary' if used, or return None for unknown.
+    return None
+
+def convert_age(x: str) -> float:
+    # Not used because age_row is None, placeholder function
+    return None
+
+def convert_gender(x: str) -> int:
+    # Not used because gender_row is None, placeholder function
+    return None
+
+# 3. Save Metadata (initial filtering).
+#    Trait data availability depends on trait_row. Since trait_row is None, is_trait_available=False.
+is_trait_available = False
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Since trait_row is None, we skip clinical feature extraction and do not call geo_select_clinical_features.
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# We'll attempt to map the probe-level data to gene symbols only if there's a genuine overlap
+# between the expression data indices and the annotation IDs.
+
+probe_column_candidates = ["ID", "probeset_id"]
+gene_symbol_column_candidates = ["gene_assignment", "mrna_assignment"]
+
+chosen_probe_col = None
+chosen_symbol_col = None
+
+# 1. Find a probe column with overlap
+for col in probe_column_candidates:
+    if col in gene_annotation.columns:
+        overlap = set(gene_annotation[col]) & set(gene_data.index)
+        if len(overlap) > 0:
+            chosen_probe_col = col
+            break
+
+# 2. Pick a gene symbol column
+for col in gene_symbol_column_candidates:
+    if col in gene_annotation.columns:
+        chosen_symbol_col = col
+        break
+
+# If none found, skip mapping
+if not chosen_probe_col or not chosen_symbol_col:
+    print("No suitable probe or gene symbol columns found in the annotation. Skipping mapping.")
+else:
+    # Build a preliminary mapping DataFrame
+    mapping_df = get_gene_mapping(
+        gene_annotation,
+        prob_col=chosen_probe_col,
+        gene_col=chosen_symbol_col
+    )
+
+    # 3. Check for genuine overlap after dropping invalid entries
+    mapped_ids = set(mapping_df["ID"].unique()) & set(gene_data.index)
+    if len(mapped_ids) == 0:
+        print("No overlapping probe IDs after cleaning. Skipping mapping.")
+    else:
+        # Proceed with the mapping since there is an actual overlap
+        gene_data = apply_gene_mapping(gene_data, mapping_df)
+        print("Gene-level mapping performed successfully.")
+        print("Mapped gene_data shape:", gene_data.shape)
+        print("First few gene symbols after mapping:", gene_data.index[:10].tolist())
+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/Sarcoma/code/GSE133228.py

@@ -0,0 +1,247 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE133228"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE133228"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE133228.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE133228.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE133228.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on background info, we assume these data measure gene expression
+
+# 2) Variable Availability and Data Type Conversion
+
+# From the sample characteristics:
+#   0 -> ['gender: Male', 'gender: Female']
+#   1 -> ['age: 3', 'age: 11', 'age: 4', 'age: 25', ...] (multiple distinct ages)
+#   2 -> ['tumor type: primary tumor'] (only one value)
+
+# The trait "Sarcoma" is not explicitly found in any row, and row 2 has only one unique value.
+# Hence, trait_row = None (not useful for a variation-based analysis).
+trait_row = None
+
+# Age data is in row=1 with multiple distinct values
+age_row = 1
+
+# Gender data is in row=0 with multiple distinct values
+gender_row = 0
+
+# 2.2) Define data type converters
+
+def convert_trait(value: str) -> int:
+    # Not used because trait_row is None, but define for consistency.
+    # If we had data, we might extract part after the colon and map accordingly.
+    return None
+
+def convert_age(value: str) -> float:
+    # Typical pattern: "age: 25"
+    # Split by colon and take the numeric part
+    parts = value.split(':')
+    if len(parts) == 2:
+        try:
+            return float(parts[1].strip())
+        except ValueError:
+            return None
+    return None
+
+def convert_gender(value: str) -> int:
+    # Typical pattern: "gender: Male"/"gender: Female"
+    # Convert Female->0, Male->1, otherwise None
+    parts = value.split(':')
+    if len(parts) == 2:
+        g = parts[1].strip().lower()
+        if g == 'male':
+            return 1
+        elif g == 'female':
+            return 0
+    return None
+
+# 3) Save Metadata (initial filtering)
+# Trait data availability depends on whether trait_row is None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Since trait_row is None, we skip extracting clinical features
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "100009676_at", "10000_at") appear to be microarray probe IDs, not standard human gene symbols.
+# Typically, such probe IDs need to be mapped to the corresponding gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Decide which key in the gene annotation dataframe corresponds to the probe IDs 
+#    (same as those in the gene expression data) and which key corresponds to the gene symbol.
+#    From our preview, "ID" in the annotation matches the probe IDs in the gene expression data,
+#    while "Description" appears to hold gene names/symbols (albeit as descriptive text).
+
+prob_col = "ID"
+gene_col = "Description"
+
+# 2) Get a gene mapping dataframe using these columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3) Convert probe-level measurements to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: Inspect the resulting gene_data shape
+print("Mapped gene expression data shape:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE142162.py

@@ -0,0 +1,235 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE142162"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE142162"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE142162.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE142162.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE142162.csv"
+json_path = "./output/preprocess/1/Sarcoma/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 availability
+is_gene_available = True  # Based on Affymetrix hgu133Plus2 arrays and "Expression profiling" indication
+
+# 2) Identify data availability (rows) and define conversion functions
+
+# For this dataset, the trait is effectively constant ("tumor type: primary tumor"), so it's not useful
+# for an association study. Hence, trait_row is None.
+trait_row = None
+
+# Age is variable under key 1
+age_row = 1
+
+# Gender is variable under key 0
+gender_row = 0
+
+# 2.2) Data type conversion functions
+
+def convert_trait(value: str):
+    """
+    This dataset does not contain meaningful variation for the primary trait 'Sarcoma'.
+    We'll return None for all inputs.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Converts age values after the colon to a continuous numeric type.
+    Unknown values are returned as None.
+    Example input: "age: 25"
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    raw_val = parts[1].strip()
+    if not raw_val.isdigit():
+        return None
+    return float(raw_val)
+
+def convert_gender(value: str):
+    """
+    Converts gender to a binary variable:
+      Female -> 0
+      Male   -> 1
+    Unknown values are returned as None.
+    Example input: "gender: Male"
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    raw_val = parts[1].strip().lower()
+    if raw_val == 'male':
+        return 1
+    elif raw_val == 'female':
+        return 0
+    return None
+
+# 3) Conduct initial dataset filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# Gene Identifier Mapping
+probe_col = "ID"          # column in gene_annotation that matches the probe IDs in gene_data
+gene_symbol_col = "Description"  # column in gene_annotation containing the gene symbol or descriptive info
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE159847.py

@@ -0,0 +1,237 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE159847"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159847"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE159847.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE159847.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE159847.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset likely contains gene expression data (Affymetrix/Agilent transcriptome).
+
+# 2. Variable Availability and Data Type Conversion
+
+# Since all samples are "complex sarcomas" (one trait), there's effectively no variation in the trait.
+trait_row = None
+
+# We do see multiple ages under key=1, so age is available.
+age_row = 1
+
+# We see males and females under key=0, so gender is available.
+gender_row = 0
+
+# Conversion functions
+def convert_trait(value: str):
+    # Not used since trait_row is None, but defined as per instructions
+    return None
+
+def convert_age(value: str):
+    # Example value: "age: 73"
+    try:
+        val = value.split(":", 1)[1].strip()
+        return float(val)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Example value: "Sex: M" or "Sex: F"
+    val = value.split(":", 1)[1].strip().lower()
+    if val == "m":
+        return 1
+    elif val == "f":
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step if trait_row is None
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait="Sarcoma",
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of selected clinical features:", preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These appear to be microarray probe IDs that are not standard human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the matching columns in gene_annotation for probe ID and gene symbol
+#    Based on the preview, 'ID' corresponds to the probe IDs (e.g., A_23_P1000xx),
+#    and 'GENE_SYMBOL' holds the gene symbol.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2. Apply the mapping to convert probe-level data into gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Check a small part of the resulting gene_data
+print("Mapped gene expression data shape:", gene_data.shape)
+print("First 5 genes in gene_data index:", gene_data.index[:5].tolist())
+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/Sarcoma/code/GSE159848.py

@@ -0,0 +1,234 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE159848"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159848"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE159848.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE159848.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE159848.csv"
+json_path = "./output/preprocess/1/Sarcoma/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  # The dataset is described as "Expression data from 50 mixoid liposarcomas"
+
+# 2. Identify availability of variables and define their data-conversion functions
+# According to the dictionary, row 0 has sex: M/F, and row 1 has age
+# For the trait "Sarcoma," the dataset is entirely mixoid liposarcomas (no variation), so we consider it unavailable.
+trait_row = None
+age_row = 1
+gender_row = 0
+
+def convert_trait(value: str):
+    """
+    Since trait is not available/variable in this dataset, always return None.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Extract the age value after the colon, converting it to float if possible.
+    Return None for invalid or unknown values.
+    """
+    val = value.split(':')[-1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Extract the gender value (M or F) after the colon and convert to binary:
+      M or male -> 1
+      F or female -> 0
+    Others -> None
+    """
+    val = value.split(':')[-1].strip().lower()
+    if val in ['m', 'male']:
+        return 1
+    elif val in ['f', 'female']:
+        return 0
+    else:
+        return None
+
+# 3. Save metadata with initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Since trait_row is None, skip extraction of clinical features
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed probe IDs (e.g., "A_23_P100001"), these are not standard human gene symbols.
+# Thus, gene-to-symbol mapping is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the matching columns in the gene annotation dataframe.
+#    From the preview, the probe identifiers are under "ID" and the gene symbols are under "GENE_SYMBOL".
+probe_col = "ID"
+symbol_col = "GENE_SYMBOL"
+
+# 2. Create the mapping dataframe from probe to gene symbol.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Convert probe-level measurements into gene expression data using the mapping dataframe.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print the shape and a small index preview to verify successful mapping
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols in mapped gene_data:", 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/Sarcoma/code/GSE162785.py

@@ -0,0 +1,218 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE162785"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162785"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE162785.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE162785.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE162785.csv"
+json_path = "./output/preprocess/1/Sarcoma/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  # Microarray analysis suggests gene expression data is available
+
+# 2) Variable availability and data type conversion
+
+# From the sample characteristics dictionary:
+# {0: ['cell line: A673', 'cell line: CHLA-10', 'cell line: EW7', 'cell line: SK-N-MC']}
+# There is only one key (0), whose values all refer to Ewing sarcoma cell lines. For our trait of interest ("Sarcoma"),
+# this dataset effectively has just one value for all samples (i.e., all are Ewing Sarcoma), so it is considered constant
+# and therefore not available for association analysis.
+trait_row = None   # No variability in the trait
+age_row = None     # No age data available
+gender_row = None  # No gender data available
+
+# Although the rows are None, we still define the conversion functions as requested.
+def convert_trait(value: str):
+    # Not used here because trait_row is None
+    # Typically, we would parse the string after the colon and convert, but there's no relevant data to parse.
+    return None
+
+def convert_age(value: str):
+    # Not used here because age_row is None
+    return None
+
+def convert_gender(value: str):
+    # Not used here because gender_row is None
+    return None
+
+# 3) Save metadata (initial filtering)
+# Trait data is considered unavailable since trait_row is None.
+is_trait_available = False
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction is skipped because trait_row is None (no clinical data for trait).
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation dataframe that match our probe IDs and contain gene symbols.
+#    From the preview, the "ID" column matches the numerical probe identifiers, and "gene_assignment" contains gene symbols.
+prob_col = "ID"
+gene_col = "gene_assignment"
+
+# 2. Create a mapping dataframe with these two columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert the probe-level expression data to gene-level expression data using this mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print shape and a small preview of the resulting mapped data
+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/Sarcoma/code/GSE162789.py

@@ -0,0 +1,245 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE162789"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162789"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE162789.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE162789.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE162789.csv"
+json_path = "./output/preprocess/1/Sarcoma/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 dataset description focusing on Ewing sarcoma pathogenesis via HDAC
+
+# 2) Identify and set availability of trait, age, and gender
+#    Inspecting the sample characteristics, we see only one key (0).
+#    All samples appear to have the same trait (Ewing sarcoma), so there's no variation -> trait not available
+trait_row = None
+
+#    Age is available for 2 samples (14, 20). The other 4 do not mention age but we will treat them as missing
+age_row = 0
+
+#    Gender is only explicitly "female" for 2 samples and unknown for the rest, so there's effectively only one unique known value
+gender_row = None
+
+# 2) Define converters for trait, age, and gender
+def convert_trait(x: str) -> int:
+    """
+    Convert the trait from string to a binary or categorical label.
+    Not used here because trait_row is None, but defined for completeness.
+    """
+    # Example logic (convert to 1 if "Ewing sarcoma" appears, else None):
+    parts = x.split(":")
+    if len(parts) > 1:
+        val = parts[1].strip().lower()
+        if "ewing sarcoma" in val:
+            return 1
+    return None
+
+def convert_age(x: str) -> float:
+    """
+    Convert the age from string to a continuous float.
+    If no age information is present, return None.
+    """
+    # Example logic to parse "14 year old"
+    match = re.search(r'(\d+)\s*year\s*old', x.lower())
+    if match:
+        return float(match.group(1))
+    return None
+
+def convert_gender(x: str) -> int:
+    """
+    Convert the gender from string to binary (female -> 0, male -> 1).
+    If unknown, return None.
+    """
+    parts = x.split(":")
+    val = parts[-1].strip().lower() if len(parts) > 1 else x.lower()
+    if "female" in val:
+        return 0
+    elif "male" in val:
+        return 1
+    return None
+
+# 3) Save metadata using initial filtering
+is_trait_available = (trait_row is not None)
+
+# Since this is just the initial filtering, set is_final=False
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Because trait_row is None, we skip clinical feature extraction
+#    (No further steps for clinical data since the trait is considered not available.)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric identifiers (e.g., 7892501, 7892502, etc.), these look like probe IDs rather than official gene symbols.
+# Therefore, they need to be mapped to standard human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# 1. Identify the annotation columns that match the expression data IDs and the gene symbols.
+#    From inspection, "ID" corresponds to the probe ID (same format as gene_data.index),
+#    and "mrna_assignment" appears to have clearer references to actual gene symbols (e.g. "OR4F4", "SEPT14").
+
+# 2. Get the gene mapping DataFrame using "mrna_assignment" as the gene symbol source
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="mrna_assignment")
+
+# 3. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Let's see the result
+print("Gene data shape after mapping:", gene_data.shape)
+print("First 5 gene indices after mapping:", gene_data.index[:5].tolist())
+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/Sarcoma/code/GSE197147.py

@@ -0,0 +1,244 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE197147"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE197147"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE197147.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE197147.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE197147.csv"
+json_path = "./output/preprocess/1/Sarcoma/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 contains gene expression data
+is_gene_available = True  # The series description explicitly mentions "Gene expression profiling"
+
+# 2. Identify variable availability and define row indices
+#    Only one key (0) is present with multiple histotypes ("HB","NB","RMS","WT").
+#    We'll map "RMS" to the trait of interest (Sarcoma=1) and others to 0.
+trait_row = 0  # We can infer the Sarcoma trait from 'histotype: RMS' vs others
+age_row = None  # No age information in the sample dictionary
+gender_row = None  # No gender information in the sample dictionary
+
+# 2.2 Define conversion functions
+def convert_trait(value: str):
+    # Extract the part after the colon.
+    val = value.split(':')[-1].strip().lower()
+    # Map RMS => 1 (Sarcoma), others => 0
+    if val == 'rms':
+        return 1
+    elif val in ['hb', 'nb', 'wt']:
+        return 0
+    return None  # For any unexpected values
+
+def convert_age(value: str):
+    # No rows found for age, so no real conversion logic needed
+    return None
+
+def convert_gender(value: str):
+    # No rows found for gender, so no real conversion logic needed
+    return None
+
+# 2.1 Check if the trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3. Save metadata with 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. If trait data is available (trait_row != None), extract clinical features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted clinical features
+    print(preview_df(selected_clinical, n=5))
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided gene expression data index (e.g., 'TC0100006437.hg.1'), 
+# these appear to be probe or platform-specific identifiers rather than standard human gene symbols.
+# Therefore, we conclude that these IDs must be mapped to standard gene symbols for proper analysis.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify which annotation columns store the gene IDs and gene symbol references.
+#    From the preview, it appears the column "ID" matches the row index of our gene expression data,
+#    and the column "SPOT_ID.1" contains gene symbol references.
+probe_id_col = "ID"
+gene_symbol_col = "SPOT_ID.1"
+
+# 2. Get a gene mapping dataframe from the annotation dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col=probe_id_col,
+    gene_col=gene_symbol_col
+)
+
+# 3. Convert probe-level measurements into gene-level expression.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Print a small preview to confirm the result
+print("Mapped gene expression data preview:")
+print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE215265.py

@@ -0,0 +1,218 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE215265"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE215265"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE215265.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE215265.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE215265.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Based on the background info, we assume it's gene expression, not miRNA/methylation.
+
+# Step 2: Determine availability of variables (trait, age, gender) and define conversion functions
+# From the sample characteristics dictionary, all samples have the same cell type "Alveolar soft part sarcoma",
+# which is essentially constant. Therefore, trait_row is considered not available for our analysis.
+trait_row = None
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> int:
+    # Not used because trait_row is None, but we define it as requested.
+    # Typically, you would parse out the portion after ':' and map to a numerical category.
+    return None
+
+def convert_age(value: str) -> float:
+    # Not used because age_row is None, but we define it as requested.
+    return None
+
+def convert_gender(value: str) -> int:
+    # Not used because gender_row is None, but we define it as requested.
+    return None
+
+# Step 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
+)
+
+# Step 4: Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The provided IDs appear to be Affymetrix probe set identifiers (e.g., "1007_PM_s_at"), 
+# which are not standard human gene symbols.
+# Hence, they require mapping to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns for gene identifier and gene symbol
+# Based on the preview, "ID" matches our probe IDs (e.g., "1415670_PM_at"), 
+# and "Gene Symbol" contains the corresponding gene symbols.
+
+probe_identifier_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Extract gene mapping information
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_identifier_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+
+# Optionally show a small preview of the resulting gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data index sample:", 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)