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p1/preprocess/Endometrioid_Cancer/code/GSE120490.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE120490"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE120490"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE120490.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE120490.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE120490.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on "transcriptomic analysis using the Affymetrix U133 Plus 2.0 microarray platform"
+
+# 2. Variable Availability and Conversion
+#    This dataset does not provide any row for trait, age, or gender.
+#    So all row indicators are None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define dummy conversion functions, since none of these variables is available.
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save Metadata (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. 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("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the columns for gene IDs and gene symbols in the gene_annotation dataframe.
+#    From our observations, the "ID" column matches the row index of the gene_data expression,
+#    and the "Gene Symbol" column in the annotation holds the associated gene symbols.
+
+# 2. Extract the mapping between probes and gene symbols and store it in mapping_df.
+mapping_df = get_gene_mapping(gene_annotation, "ID", "Gene Symbol")
+
+# 3. Apply the mapping to convert probe-level expression values into gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Confirm the resulting gene_data shape or a snippet if needed (commented out):
+# print(gene_data.shape)
+# print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check whether we actually have a clinical CSV file (i.e., trait data) from Step 2
+if os.path.exists(out_clinical_data_file):
+    # 2) Link the clinical and gene expression data
+    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)
+
+    # 3) Handle missing values
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Evaluate bias in the trait
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 5) Final validation (trait is available)
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=final_data,
+        note="Trait data successfully extracted in Step 2."
+    )
+
+    # 6) If the dataset is usable, save
+    if is_usable:
+        final_data.to_csv(out_data_file)
+
+else:
+    # If the clinical file does not exist, the trait is unavailable
+    # Perform final validation indicating that we lack trait data
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,  # Arbitrary non-None to skip usage
+        df=empty_df,
+        note="No trait data was found; linking and final dataset output are skipped."
+    )

p1/preprocess/Endometrioid_Cancer/code/GSE40785.py

@@ -0,0 +1,181 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE40785"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE40785"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE40785.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE40785.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE40785.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset contains gene expression data
+#    Based on the series description, there are gene probes and expression profiling.
+#    So we conclude it likely has suitable gene expression data (not just miRNA or methylation).
+is_gene_available = True
+
+# 2. Determine availability of trait, age, and gender
+#    From the dictionary, we see various "histology:" entries at key=1, including "histology: Endometrioid".
+#    This indicates trait data is present (key=1). No mention of age or gender data was found in the dictionary.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+#    We'll define functions that convert the raw strings to appropriate data types.
+#    Trait: We'll treat "Endometrioid" or values containing "Endometrioid" as 1, and anything else as 0.
+def convert_trait(value: str) -> Optional[int]:
+    if not isinstance(value, str):
+        return None
+    # Split on colon if present
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower()  # value after colon, or the entire string if no colon
+    if 'endometrioid' in val:
+        return 1
+    elif 'histology' in val or 'mucinous' in val or 'clear cell' in val or 'serous' in val:
+        return 0
+    return None  # for unknown cases
+
+#    Since we don't have age_row or gender_row, we only define simple converters returning None if called.
+def convert_age(value: str) -> Optional[float]:
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    return None
+
+# 3. Conduct initial filtering on dataset usability and save metadata
+is_trait_available = (trait_row is not None)
+is_usable_flag = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    # Assume "clinical_data" is the DataFrame obtained previously
+    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 selected clinical features
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Preview of Clinical Features:", clinical_preview)
+
+    # Save the extracted clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the given gene expression data indices (e.g., "ILMN_1343291"), these are Illumina probe IDs,
+# not standard human gene symbols. Hence, gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Determine columns for probe IDs ("ID") and gene symbols ("Symbol") from our annotation.
+#    Then extract a gene mapping DataFrame with two columns: [ID, Gene].
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Let's preview and then save the resulting gene-level expression data.
+print("Preview of gene-level expression data:", preview_df(gene_data))
+gene_data.to_csv(out_gene_data_file)
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read back the clinical dataframe saved in Step 2. 
+# According to Step 2, we saved 1 row (the trait) × N columns (the samples) without the row index.
+selected_clinical_df = pd.read_csv(out_clinical_data_file)  # shape: (1, number_of_samples)
+# Rename the row index to the trait (e.g., "Eczema")
+selected_clinical_df.index = [trait]
+
+# 2) Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values using the trait column
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4) Evaluate bias in the trait (and remove biased demographic features if they existed)
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 5) Final validation. Since we do have trait data, set is_trait_available=True
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=final_data,
+    note="Trait data successfully extracted from Step 2."
+)
+
+# 6) If the dataset is deemed usable, save final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/code/GSE65986.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE65986"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE65986"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE65986.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE65986.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE65986.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+#    From the background info: "Gene expression ... was analyzed by Affymetrix U133plus2 array."
+#    Hence, it is gene expression rather than miRNA or methylation data.
+is_gene_available = True
+
+# 2. Identify data availability for trait, age, gender, and define conversion functions
+
+# Observed keys in the sample characteristics dictionary:
+#   0 -> histology: [Clear, Endometrioid, Serous]
+#   1 -> age: [64, 57, ...]
+#   2 -> Stage: ...
+#   3 -> pfs: ...
+#   4 -> prognosis: ...
+# There is no key containing gender data, and it is likely all female since this is an ovarian cancer study.
+
+trait_row = 0  # row 0 has multiple values including "Endometrioid", so it is not constant and is relevant to our trait.
+age_row = 1    # row 1 has multiple age values, so it is valid.
+gender_row = None  # no gender info is available or it is constant (all female), so treat as not available.
+
+def convert_trait(value: str):
+    """Convert the 'histology' entries to a binary trait.
+       'Endometrioid' -> 1, others -> 0."""
+    val = value.split(':')[-1].strip().lower()
+    if val == 'endometrioid':
+        return 1
+    elif val in ['clear', 'serous']:
+        return 0
+    return None  # unknown or unexpected text
+
+def convert_age(value: str):
+    """Convert 'age' entries to a continuous numeric type."""
+    val = value.split(':')[-1].strip()
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(value: str):
+    """Convert 'gender' entries to binary: female->0, male->1.
+       Although not used here, define for completeness."""
+    val = value.split(':')[-1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+
+# 3. Save metadata after initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction if trait data is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,           # clinical_data is assumed to be already loaded from a previous step
+        trait,
+        trait_row,
+        convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    print("Preview of selected clinical features:\n", preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These IDs are Affymetrix probe set identifiers, not standard gene symbols.
+# They require mapping to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the corresponding columns in the annotation dataframe for probe IDs and gene symbols
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print shape and preview if desired
+print("Mapped gene_data dimensions:", gene_data.shape)
+print("Preview of mapped gene_data:\n", gene_data.iloc[:5, :5])
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Read the clinical dataframe with header=0 to ensure the first row is recognized as column headers,
+#    leaving two rows of data to be indexed as [trait, "Age"].
+selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+selected_clinical_df.index = [trait, "Age"]
+
+# 3) Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 4) Handle missing values using the trait column
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Evaluate bias in the trait (and remove biased demographic features if they existed)
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 6) Final validation. Since we do have trait data, set is_trait_available=True
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=final_data,
+    note="Trait and Age data in the first two rows of the clinical CSV."
+)
+
+# 7) If the dataset is deemed usable, save final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/code/GSE66667.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE66667"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE66667"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE66667.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE66667.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE66667.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# From the background info, microarrays were used to measure global gene expression, so:
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# The sample characteristics dictionary reveals that 'histology' (key=0) varies
+# and includes "Endometrioid". Hence we use key 0 as trait_row.
+# No age or gender info is present, so set those to None.
+
+trait_row = 0
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the histology field to a binary indicator:
+    1 if it matches 'Endometrioid', otherwise 0.
+    Unknown values => None (not expected here, but safe fallback).
+    """
+    parts = value.split(":", 1)
+    val = parts[1].strip() if len(parts) > 1 else value
+    if "endometrioid" in val.lower():
+        return 1
+    else:
+        return 0
+
+# Age and gender are not available
+convert_age = None
+convert_gender = None
+
+# Determine if trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3. Save Metadata (initial filtering)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    df_clinical_selected = geo_select_clinical_features(
+        clinical_data,
+        trait,
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    # Preview and save
+    preview_result = preview_df(df_clinical_selected)
+    print("Clinical features preview:", preview_result)
+
+    df_clinical_selected.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the given probe IDs (e.g., "1007_s_at", "1053_at"), they appear to be Affymetrix probe set IDs 
+# (or similar microarray probe identifiers). These are not standard human gene symbols and therefore
+# require mapping to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Determine the columns corresponding to the probe IDs ("ID") and gene symbols ("Gene Symbol").
+#    From the preview, the annotation column for the microarray IDs is "ID",
+#    and the column for gene symbols is "Gene Symbol".
+
+prob_col = "ID"
+gene_col = "Gene Symbol"
+
+# 2. Build a gene mapping dataframe from the annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert probe-level measurements to gene-level expression using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print some basic info about the new gene_data
+print("Mapped gene expression data shape:", gene_data.shape)
+print("First few gene IDs after mapping:", gene_data.index[:10])
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read back the clinical dataframe saved in Step 2. 
+# According to Step 2, we saved 1 row (the trait) × N columns (the samples) without the row index.
+selected_clinical_df = pd.read_csv(out_clinical_data_file)  # shape: (1, number_of_samples)
+# Rename the row index to the trait (e.g., "Eczema")
+selected_clinical_df.index = [trait]
+
+# 2) Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values using the trait column
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4) Evaluate bias in the trait (and remove biased demographic features if they existed)
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 5) Final validation. Since we do have trait data, set is_trait_available=True
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=final_data,
+    note="Trait data successfully extracted from Step 2."
+)
+
+# 6) If the dataset is deemed usable, save final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/code/GSE68600.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE68600"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE68600"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE68600.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE68600.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE68600.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # "Assay Type: Gene Expression" indicates gene expression data is likely present.
+
+# 2. Variable Availability and Data Type Conversion
+# Observing the sample characteristics dictionary:
+#  - trait ("Endometrioid_Cancer") can be inferred from row 4 (histology).
+#  - age is not found => age_row = None.
+#  - gender appears to be uniformly female => no variability => gender_row = None.
+
+trait_row = 4
+age_row = None
+gender_row = None
+
+# Define data-conversion functions:
+def convert_trait(sample_value: str):
+    """
+    Convert sample_value into a binary representation:
+    1 if 'endometrioid' is found in the histology,
+    0 if it is any other histology,
+    None if it can't be parsed.
+    """
+    parts = sample_value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Mark samples with 'endometrioid' as 1, all others as 0
+    if 'endometrioid' in val:
+        return 1
+    else:
+        return 0
+
+def convert_age(sample_value: str):
+    # Age data is not available in this dataset, so return None
+    return None
+
+def convert_gender(sample_value: str):
+    # Gender is uniformly female, so it's not useful for analysis. Return None.
+    return None
+
+# Determine whether trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3. Save Metadata (Initial Filtering)
+dataset_passed_filtering = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    # 'clinical_data' is assumed to be the DataFrame containing the sample characteristics
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical data
+    print(preview_df(selected_clinical_df))
+
+    # Save the extracted clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the identifiers, these appear to be Affymetrix probe IDs rather than standard gene symbols.
+# Therefore, they require mapping to gene symbols.
+requires_gene_mapping = True
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# Gene Identifier Mapping
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 1 & 2. Identify the columns for the probe IDs and gene symbols, then retrieve the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print a brief preview of the mapped gene_data
+print("Mapped gene expression data shape:", gene_data.shape)
+print("First 5 genes:\n", gene_data.head(5))
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read back the clinical dataframe saved in Step 2. 
+# According to Step 2, we saved 1 row (the trait) × N columns (the samples) without the row index.
+selected_clinical_df = pd.read_csv(out_clinical_data_file)  # shape: (1, number_of_samples)
+# Rename the row index to the trait (e.g., "Eczema")
+selected_clinical_df.index = [trait]
+
+# 2) Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values using the trait column
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4) Evaluate bias in the trait (and remove biased demographic features if they existed)
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 5) Final validation. Since we do have trait data, set is_trait_available=True
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=final_data,
+    note="Trait data successfully extracted from Step 2."
+)
+
+# 6) If the dataset is deemed usable, save final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/code/GSE73551.py

@@ -0,0 +1,176 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73551"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73551"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE73551.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE73551.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE73551.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background information, this dataset is likely gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# From the sample characteristics, row 0 has multiple "cell type" entries
+# that include "ENDOMETRIOID". We'll treat that as the trait row for Endometrioid_Cancer.
+trait_row = 0
+
+# No age or gender information is available or inferred from the sample characteristics
+age_row = None
+gender_row = None
+
+# For the trait, we convert "ENDOMETRIOID" to 1 and all other cell types to 0.
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
+    return 1 if val == "endometrioid" else 0
+
+# Since age and gender data are not available, these functions will return None.
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    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 (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the selected clinical features
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+
+    # Save extracted clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the listed identifiers (1,2,3, etc.), they are not standard human gene symbols.
+# These identifiers likely need to be mapped to recognized gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify the columns in the gene_annotation that correspond to the gene expression row IDs ("ID")
+#    and the human gene symbols ("GeneSymbol"). Extract them into a mapping dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',         # Matches the row IDs in our gene_data
+    gene_col='GeneSymbol'  # Stores the human gene symbols
+)
+
+# 3. Convert probe-level measurements to gene-level expression data using apply_gene_mapping
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Just display the first few rows for observation
+print(gene_data.head(5))
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read back the clinical dataframe saved in Step 2. 
+# According to Step 2, we saved 1 row (the trait) × N columns (the samples) without the row index.
+selected_clinical_df = pd.read_csv(out_clinical_data_file)  # shape: (1, number_of_samples)
+# Rename the row index to the trait (e.g., "Eczema")
+selected_clinical_df.index = [trait]
+
+# 2) Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values using the trait column
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4) Evaluate bias in the trait (and remove biased demographic features if they existed)
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 5) Final validation. Since we do have trait data, set is_trait_available=True
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=final_data,
+    note="Trait data successfully extracted from Step 2."
+)
+
+# 6) If the dataset is deemed usable, save final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/code/GSE73614.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73614"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73614"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE73614.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE73614.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE73614.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset contains gene expression data
+is_gene_available = True  # Based on background info describing transcriptional profiling
+
+# 2. Identify the availability of trait, age, and gender
+#    From the sample characteristics dictionary, we only see {0: ['tissue: ovarian']}.
+#    This has no variation (same value for all samples) and does not provide the Endometrioid_Cancer distinction.
+#    Hence, there's no useful variable for trait, age, or gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2. Define type-conversion functions.
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Binary conversion for 'Endometrioid_Cancer':
+    Return 1 if the value indicates endometrioid, 0 if indicates something else,
+    None if unknown.
+    """
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower() if len(parts) > 1 else value.strip().lower()
+    if 'endometrioid' in val:
+        return 1
+    elif 'serous' in val or 'clear' in val or 'ovarian' in val:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Continuous conversion for age:
+    Try to parse a number from the string, return None if parsing fails.
+    """
+    parts = value.split(':', 1)
+    val_str = parts[-1].strip() if len(parts) > 1 else value.strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Binary conversion for gender:
+    Return 0 for female, 1 for male, None if unknown.
+    """
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower() if len(parts) > 1 else value.strip().lower()
+    if val in ['female', 'f']:
+        return 0
+    elif val in ['male', 'm']:
+        return 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, we skip the clinical feature extraction step
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Determine which columns match the gene expression dataset and the gene symbols
+probe_col = "ID"
+symbol_col = "GENE_SYMBOL"
+
+# 2. Extract the gene identifier and gene symbol columns to form a mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Convert the probe-level expression data to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+print("Gene mapping completed. Final gene_data shape:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols in our gene_data and save the result
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Check if the clinical data file (trait data) exists
+if not os.path.exists(out_clinical_data_file):
+    print(f"File not found: {out_clinical_data_file}. No trait data is available for this cohort.")
+    # Perform an initial/partial validation because trait data is missing
+    is_usable = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+else:
+    # 3) Trait data is present, so read it and link with gene data
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # Rename the single row index to match the trait name
+    selected_clinical_df.index = [trait]
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 4) Handle missing values, using the trait column name
+    final_data = handle_missing_values(linked_data, trait_col=trait)
+
+    # 5) Check whether the trait (and optional demographics) are severely biased
+    trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+    # 6) Conduct final validation
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=final_data,
+        note="Trait data successfully extracted and processed."
+    )
+
+    # 7) If the dataset is deemed usable, save the final linked data
+    if is_usable:
+        final_data.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/code/GSE73637.py

@@ -0,0 +1,287 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE73637"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73637"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE73637.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE73637.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE73637.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+
+# 1. Determine if gene expression data is available
+#    Based on background info, this dataset uses gene expression for GECA analysis.
+is_gene_available = True
+
+# 2. Identify data availability and define row indices
+#    From the sample characteristics dictionary, histopathology is stored in row 3,
+#    which includes “Endometrioid” among other values (i.e., multiple categories).
+#    There's no mention of age or gender, so age_row and gender_row are None.
+
+trait_row = 3     # row containing histopathology with "Endometrioid"
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(value: str):
+    """
+    Convert histopathology values to binary, indicating whether
+    'Endometrioid' is present (1) or not (0). Unknown/unexpected
+    values become None.
+    """
+    parts = value.split(':', 1)
+    if len(parts) == 2:
+        val_str = parts[1].strip().lower()
+    else:
+        val_str = parts[0].strip().lower()
+
+    # Heuristic: Any mention of 'endometrioid' or 'endometroid' is mapped to 1
+    # Otherwise, map known terms to 0, else None.
+    if 'endometrioid' in val_str or 'endometroid' in val_str:
+        return 1
+    elif any(keyword in val_str for keyword in ['serous', 'carcinoma', 'clear cell', 'adenocarcinoma']):
+        return 0
+    else:
+        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
+
+# 3. Perform 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. If trait_row is available, extract clinical data and save
+if trait_row is not None:
+    # Suppose 'clinical_data' is the DataFrame that holds the sample characteristics
+    # in the same format as shown in the "Sample Characteristics Dictionary".
+    data_dict = {
+        0: ['cell type: ovarian cells'],
+        1: [
+            'cell line: COV504',
+            'cell line: COV362',
+            'cell line: UWB1.289+BRCA1',
+            'cell line: OV56',
+            'cell line: UWB1.289',
+            'cell line: COV318',
+            'cell line: NCI/ADR-RES',
+            'cell line: OVCAR3',
+            'cell line: OVCAR4',
+            'cell line: OVCAR8',
+            'cell line: IGR-OV1',
+            'cell line: SK-OV-3',
+            'cell line: OVCAR5',
+            'cell line: ES-2',
+            'cell line: TOV-21G',
+            'cell line: TOV-112D',
+            'cell line: PEO1',
+            'cell line: PEO4'
+        ],
+        2: ['tumor site of origin: Ovarian'],
+        3: [
+            'histopathology: Serous',
+            'histopathology: Endometrioid',
+            'histopathology: Poorly differentiated serous',
+            'histopathology: Undifferentiated carcinoma',
+            'histopathology: Poorly differentiated carcinoma',
+            'histopathology: Moderately differentiated carcinoma',
+            'histopathology: Endometroid with serous/clear cell',
+            'histopathology: Well-differentiated adenocarcinoma',
+            'histopathology: Poorly differentiated clear cell',
+            'histopathology: Clear Cell'
+        ]
+    }
+    # Convert the dictionary to a DataFrame similar to how GEO data often appear
+    clinical_data = pd.DataFrame.from_dict(data_dict, orient='index').fillna('')
+
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted DataFrame
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_result)
+
+    # Save clinical data to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the output, the gene identifiers are numeric (e.g., '1', '2', '3', etc.),
+# which indicates they are not human gene symbols and likely require mapping.
+# Therefore:
+
+requires_gene_mapping = True
+# STEP5
+import pandas as pd
+import io
+
+# 1. Extract the lines that do NOT start with '^', '!', or '#', but do NOT parse them into a DataFrame yet.
+annotation_text, _ = filter_content_by_prefix(
+    source=soft_file,
+    prefixes_a=['^', '!', '#'],
+    unselect=True,
+    source_type='file',
+    return_df_a=False,
+    return_df_b=False
+)
+
+# 2. Manually parse the filtered text into a DataFrame, specifying engine="python" to avoid buffer overflow issues.
+gene_annotation = pd.read_csv(
+    io.StringIO(annotation_text),
+    delimiter='\t',
+    on_bad_lines='skip',
+    engine='python'
+)
+
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns correspond to the gene expression ID and the gene symbol
+#    From the previews, "ID" matches the numeric identifiers in gene_data, 
+#    and "GeneSymbol" stores the actual gene symbols.
+
+# 2. Get a gene mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GeneSymbol")
+
+# 3. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP7
+
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Read back the clinical DataFrame we saved in Step 2.
+#    Since we saved a single row with no header or index, we read with header=None to keep that row as data.
+selected_clinical_df = pd.read_csv(out_clinical_data_file, header=None)
+
+#    If the number of columns aligns with the gene expression DataFrame's columns (i.e., same samples),
+#    rename the clinical DataFrame columns accordingly to achieve correct sample alignment.
+if selected_clinical_df.shape[1] == normalized_gene_data.shape[1]:
+    selected_clinical_df.columns = normalized_gene_data.columns
+
+#    Set the row index to the trait name (e.g., "Endometrioid_Cancer")
+selected_clinical_df.index = [trait]
+
+# 3) Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 4) Handle missing values using the trait column
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Evaluate bias in the trait (and remove biased demographic features if present)
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 6) Final validation. We do have trait data, so set is_trait_available=True
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=final_data,
+    note="Aligned columns in clinical DataFrame to match gene expression samples."
+)
+
+# 7) If the dataset is deemed usable, save final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)
+import pandas as pd
+
+# STEP8
+# 1) Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Read back the clinical DataFrame saved in Step 2 (one or more rows × number_of_samples columns, no header).
+selected_clinical_df = pd.read_csv(out_clinical_data_file, header=None)
+
+# In case there are empty columns (e.g., trailing commas), drop them
+selected_clinical_df = selected_clinical_df.dropna(axis=1, how='all')
+
+# If the number of columns matches the number of samples (columns) in the gene data, rename to align sample IDs
+if selected_clinical_df.shape[1] == normalized_gene_data.shape[1]:
+    selected_clinical_df.columns = normalized_gene_data.columns
+else:
+    print(f"Warning: Mismatch in shape. Clinical data has {selected_clinical_df.shape[1]} columns, "
+          f"while gene data has {normalized_gene_data.shape[1]} columns. Linking may fail.")
+
+# Set the row index to the trait, so we can keep track of it
+selected_clinical_df.index = [trait]
+
+# 2) Link the clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3) Handle missing values using the trait column
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4) Evaluate bias in the trait (and remove biased demographic features if present)
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 5) Final validation
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=final_data,
+    note="Ensured clinical and gene sample columns were aligned if possible."
+)
+
+# 6) If the dataset is usable, save the final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/code/GSE94523.py

@@ -0,0 +1,70 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE94523"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94523"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE94523.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE94523.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE94523.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Microarray expression implies gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics {0: ['tissue: endometrioid adenocarcinoma']},
+# we see there's only one unique value: "endometrioid adenocarcinoma".
+# This is constant for all samples, so it's not useful for association. 
+trait_row = None  # Not available because there's no variation
+age_row = None    # No information about age
+gender_row = None # No information about gender
+
+# Define the conversion functions (though we won't actually use them for None rows).
+def convert_trait(value: str):
+    # Since data is not available, return None
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+dataset_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, because trait_row is None

p1/preprocess/Endometrioid_Cancer/code/GSE94524.py

@@ -0,0 +1,74 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+cohort = "GSE94524"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
+in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94524"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE94524.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE94524.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE94524.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info, assume gene expression data is available.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify row keys (None if not available or constant)
+#    The sample characteristics dictionary only has one key, 0, whose value is
+#    "tissue: endometrioid adenocarcinoma" (single unique value).
+#    Therefore, no meaningful variation for trait, age, or gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define conversion functions
+def convert_trait(value: str) -> int:
+    # No data available, but if needed, here's a placeholder.
+    return None
+
+def convert_age(value: str) -> float:
+    # No data available, but if needed, here's a placeholder.
+    return None
+
+def convert_gender(value: str) -> int:
+    # No data available, but if needed, here's a placeholder.
+    return None
+
+# 3. Save Metadata (initial filtering) - trait is unavailable if 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
+#    Skip because trait_row is None (no available clinical variation).

p1/preprocess/Endometrioid_Cancer/code/TCGA.py

@@ -0,0 +1,111 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Endometrioid_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Endometrioid_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Endometrioid_Cancer/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify subdirectories under tcga_root_dir
+subdirectories = os.listdir(tcga_root_dir)
+
+trait_subdir = None
+for d in subdirectories:
+    lower_d = d.lower()
+    # Check for "endometrioid" or "ucec" to match "TCGA_Endometrioid_Cancer_(UCEC)"
+    if "endometrioid" in lower_d or "ucec" in lower_d:
+        trait_subdir = d
+        break
+
+# If none found, skip this trait
+if not trait_subdir:
+    print(f"No suitable subdirectory found for trait '{trait}'. Skipping...")
+    is_gene_available = False
+    is_trait_available = False
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+else:
+    # 2. Identify paths to the clinical and genetic data files
+    full_subdir_path = os.path.join(tcga_root_dir, trait_subdir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(full_subdir_path)
+
+    # 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')
+
+    # 4. Print the column names of the clinical data for inspection
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())
+# Identify candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Extract the candidate columns
+df_age = clinical_df[candidate_age_cols] if candidate_age_cols else pd.DataFrame()
+df_gender = clinical_df[candidate_gender_cols] if candidate_gender_cols else pd.DataFrame()
+
+# Preview the extracted columns
+age_preview = preview_df(df_age, n=5, max_items=200) if not df_age.empty else {}
+gender_preview = preview_df(df_gender, n=5, max_items=200) if not df_gender.empty else {}
+
+# Print the results
+print("candidate_age_cols =", candidate_age_cols)
+print("candidate_gender_cols =", candidate_gender_cols)
+print(age_preview)
+print(gender_preview)
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print("Selected age column:", age_col)
+print("Selected gender column:", gender_col)
+# 1) Extract and standardize clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols
+normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
+normalized_gene_df.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data
+linked_data = selected_clinical_df.join(normalized_gene_df.T, how='inner')
+
+# 4) Handle missing values
+linked_data_clean = handle_missing_values(linked_data, trait)
+
+# 5) Determine biased features
+trait_biased, linked_data_no_bias = judge_and_remove_biased_features(linked_data_clean, trait)
+
+# 6) Final quality validation
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data_no_bias,
+    note="Endometrioid Cancer TCGA cohort processed successfully."
+)
+
+# 7) Save usable data
+if is_usable:
+    linked_data_no_bias.to_csv(out_data_file)

p1/preprocess/Endometrioid_Cancer/cohort_info.json

@@ -0,0 +1 @@
+{"GSE94524": {"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}, "GSE94523": {"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}, "GSE73637": {"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": "Trait data successfully extracted from Step 2."}, "GSE73614": {"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}, "GSE73551": {"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": 50, "note": "Trait data successfully extracted from Step 2."}, "GSE68600": {"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": 113, "note": "Trait data successfully extracted from Step 2."}, "GSE66667": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 36, "note": "Trait data successfully extracted from Step 2."}, "GSE65986": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 55, "note": "Trait and Age data in the first two rows of the clinical CSV."}, "GSE40785": {"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": 30, "note": "Trait data successfully extracted from Step 2."}, "GSE120490": {"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 was found; linking and final dataset output are skipped."}, "TCGA": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": false, "sample_size": 201, "note": "Endometrioid Cancer TCGA cohort processed successfully."}}

p1/preprocess/Endometrioid_Cancer/gene_data/GSE120490.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c2759aa8e42735d02ef9d461e653d8193afbf79f2bc172dbf0a1ffec969bbfd
+size 38249974

p1/preprocess/Endometrioid_Cancer/gene_data/GSE73551.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:521c3184f53a970bae9c21a469f19dc9d3761202131547c1bef3be922fb6503f
+size 14281959