Add files using upload-large-folder tool

.gitattributes

@@ -861,3 +861,21 @@ p1/preprocess/Sjögrens_Syndrome/gene_data/GSE51092.csv filter=lfs diff=lfs merg
 p1/preprocess/Stomach_Cancer/gene_data/GSE98708.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Stomach_Cancer/gene_data/GSE183136.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Stomach_Cancer/gene_data/GSE130823.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Stroke/GSE58294.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Sjögrens_Syndrome/gene_data/GSE66795.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Stroke/gene_data/GSE48424.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Stroke/gene_data/GSE68526.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Sarcoma/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Stroke/gene_data/GSE161533.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Stroke/gene_data/GSE47727.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Stroke/gene_data/GSE58294.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Substance_Use_Disorder/gene_data/GSE138297.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Thyroid_Cancer/GSE151179.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Thymoma/GSE42977.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Prostate_Cancer/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Thyroid_Cancer/GSE82208.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Thymoma/gene_data/GSE42977.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Thymoma/gene_data/GSE131027.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Prostate_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Thyroid_Cancer/GSE80022.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Thyroid_Cancer/gene_data/GSE138198.csv filter=lfs diff=lfs merge=lfs -text

p1/preprocess/Prostate_Cancer/TCGA.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3bbf3437810231a05a8a33244fdb0b456a205419e47f9ac0fd0a9f41b075ea96
+size 165480790

p1/preprocess/Prostate_Cancer/gene_data/TCGA.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:890ecd87b1a72d6f024ca89aca716617571a5bd650d1394b9310f986ab9eb496
+size 165478026

p1/preprocess/Sarcoma/gene_data/TCGA.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:affd5e63814cb48cd311be374db9e5944092f4cd6f4c56833a078f5444d320b7
+size 79542665

p1/preprocess/Sjögrens_Syndrome/gene_data/GSE66795.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:36ec421c3df8e66a99cd36c18a80075dcb9de3b67ea6f9704ac561a05b16e6d7
+size 41209920

p1/preprocess/Stroke/GSE58294.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aac70556b9b82709796331682b3477de899258a31c4d35c3ff19363f33e1be0e
+size 20321203

p1/preprocess/Stroke/gene_data/GSE161533.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d208865a1e0a2d86a225d5d57d2b021d6aeadb41c64abca895fd41bc8a56d1d
+size 16557136

p1/preprocess/Stroke/gene_data/GSE47727.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3911e4ca8b0d9286bd33dfe6347f7ca16dd00f295d016d279eaaebc43067913c
+size 28462595

p1/preprocess/Stroke/gene_data/GSE48424.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00dd792e0a6531655e940c53da609aacb9b587f391354cbec5d5bf0e24df81c2
+size 11568964

p1/preprocess/Stroke/gene_data/GSE58294.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69f36e4f9618774bbd303b0f2be4cd8f6dd651cc162cd324e238f00773ef4e5a
+size 20320832

p1/preprocess/Stroke/gene_data/GSE68526.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9162df01fa3b48faf0f29544794c10bfa1ff1b98c717e0d6a35f31690d3ee1cf
+size 22185928

p1/preprocess/Substance_Use_Disorder/code/GSE161999.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Substance_Use_Disorder"
+cohort = "GSE161999"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
+in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE161999"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Substance_Use_Disorder/GSE161999.csv"
+out_gene_data_file = "./output/preprocess/1/Substance_Use_Disorder/gene_data/GSE161999.csv"
+out_clinical_data_file = "./output/preprocess/1/Substance_Use_Disorder/clinical_data/GSE161999.csv"
+json_path = "./output/preprocess/1/Substance_Use_Disorder/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 whether gene expression data is available
+is_gene_available = True  # Based on the dataset description indicating RNA-based (likely gene expression) data
+
+# Step 2: Identify data availability and define conversion functions
+# 2.1. Identify row indices
+trait_row = 1   # diagnosis: Alcohol/Control (binary)
+age_row = 2     # age: numeric, multiple unique values
+gender_row = None  # Only 'Sex: Male' found; single unique value => not available
+
+# 2.2. Define data type conversion functions
+def convert_trait(value: str):
+    """
+    Convert the trait diagnosis information to a binary variable:
+      'Alcohol' -> 1
+      'Control' -> 0
+      Otherwise -> None
+    """
+    # Extract the part after the colon
+    parts = value.split(':')
+    raw_val = parts[-1].strip().lower() if len(parts) > 1 else None
+    if raw_val == 'alcohol':
+        return 1
+    elif raw_val == 'control':
+        return 0
+    return None
+
+def convert_age(value: str):
+    """
+    Convert the age to a float. If parsing fails, return None.
+    """
+    parts = value.split(':')
+    raw_val = parts[-1].strip() if len(parts) > 1 else None
+    try:
+        return float(raw_val)
+    except:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender to binary:
+      'Female' -> 0
+      'Male' -> 1
+      Otherwise -> None
+    (Not used here because gender_row is None.)
+    """
+    parts = value.split(':')
+    raw_val = parts[-1].strip().lower() if len(parts) > 1 else None
+    if raw_val == 'male':
+        return 1
+    elif raw_val == 'female':
+        return 0
+    return None
+
+# Step 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
+)
+
+# Step 4: If trait data is available, extract clinical features
+if trait_row is not None:
+    extracted_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
+    clinical_preview = preview_df(extracted_clinical_df)
+    print(clinical_preview)
+
+    # Save extracted clinical data to CSV
+    extracted_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 (e.g., "1007_s_at", "1053_at") are Affymetrix probe set identifiers, not standard human gene symbols.
+# Therefore, gene mapping to 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 dataframe that correspond to the probe identifiers ("ID") 
+#    and the gene symbols ("Gene Symbol").
+
+# 2. Create a mapping dataframe using the get_gene_mapping function.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation, 
+    prob_col="ID", 
+    gene_col="Gene Symbol"
+)
+
+# 3. Apply the mapping to convert probe-level data into gene-level expression.
+gene_data = apply_gene_mapping(
+    expression_df=gene_data, 
+    mapping_df=mapping_df
+)
+
+# (Optional) Print some basic information about the resulting gene_data for verification.
+print("Shape of the mapped gene_data:", gene_data.shape)
+print("Preview of the mapped gene_data:")
+print(preview_df(gene_data, n=5))
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(extracted_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Substance_Use_Disorder/code/GSE273630.py

@@ -0,0 +1,75 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Substance_Use_Disorder"
+cohort = "GSE273630"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
+in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE273630"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Substance_Use_Disorder/GSE273630.csv"
+out_gene_data_file = "./output/preprocess/1/Substance_Use_Disorder/gene_data/GSE273630.csv"
+out_clinical_data_file = "./output/preprocess/1/Substance_Use_Disorder/clinical_data/GSE273630.csv"
+json_path = "./output/preprocess/1/Substance_Use_Disorder/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
+# Based on the background information, we have NanoString custom transcriptome data, so set True
+is_gene_available = True
+
+# Step 2: Variable Availability and Data Type Conversion
+# From the sample characteristics dictionary:
+# {0: ['tissue: Peripheral blood cells']}
+# There is no row providing trait, age, or gender information (all participants are the same)
+# Hence, they are not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions
+def convert_trait(raw_value: str):
+    # For demonstration, we parse after colon. But here, trait data is not available.
+    # Return None for any input in this scenario.
+    return None
+
+def convert_age(raw_value: str):
+    # Age data is constant (35-44) for all participants, effectively not a variable.
+    # Return None for any input in this scenario.
+    return None
+
+def convert_gender(raw_value: str):
+    # All participants are male, so there is no variation.
+    # Return None for any input in this scenario.
+    return None
+
+# Step 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
+)
+
+# Step 4: Since trait_row is None, skip clinical feature extraction

p1/preprocess/Substance_Use_Disorder/code/GSE94399.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Substance_Use_Disorder"
+cohort = "GSE94399"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
+in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE94399"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Substance_Use_Disorder/GSE94399.csv"
+out_gene_data_file = "./output/preprocess/1/Substance_Use_Disorder/gene_data/GSE94399.csv"
+out_clinical_data_file = "./output/preprocess/1/Substance_Use_Disorder/clinical_data/GSE94399.csv"
+json_path = "./output/preprocess/1/Substance_Use_Disorder/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 stating "Gene expression profiling..."
+
+# 2. Variable Availability and Data Type Conversion
+
+# Since the clinical dictionary only has "cohort" and "outcome" entries,
+# and everyone seems to share the same overarching trait (alcoholic hepatitis),
+# there is no varying trait data here. Similarly, no age or gender info is present.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define the conversion functions.
+# They won't be used since trait_row, age_row, and gender_row are None,
+# but we still define them to match the required function names.
+def convert_trait(value: str) -> Optional[Union[int, float]]:
+    return None
+
+def convert_age(value: str) -> Optional[Union[int, float]]:
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    return None
+
+# 3. Save Metadata (Initial Filtering)
+# trait data availability is determined by 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 do not extract 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])
+# These probe identifiers (e.g., "11715100_at") appear to be Affymetrix probe set IDs, not standard human gene symbols.
+# Hence, they require mapping to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns for gene ID and gene symbol in 'gene_annotation'.
+#    From the preview, probe IDs match 'ID' and gene symbols match 'Gene Symbol'.
+
+# 2. Get the gene mapping dataframe using the identified columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level measurements to gene expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+# According to the instructions, if the trait is not actually available (trait_row is None),
+# we should not proceed with further trait-dependent steps such as linking and bias checks.
+# Instead, we finalize the dataset as not usable.
+
+# Therefore, we skip all trait-dependent steps and directly perform final validation with
+# is_trait_available=False, marking this dataset as not usable.
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,    # We did confirm gene data are available
+    is_trait_available=False,  # Trait is not available
+    is_biased=None,
+    df=None,
+    note="Trait unavailable => dataset not usable."
+)
+
+# No further steps are done because trait is unavailable.

p1/preprocess/Substance_Use_Disorder/code/TCGA.py

@@ -0,0 +1,71 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Substance_Use_Disorder"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Substance_Use_Disorder/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Substance_Use_Disorder/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Substance_Use_Disorder/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Substance_Use_Disorder/cohort_info.json"
+
+import os
+
+# List subdirectories (already provided in the environment)
+subdirectories = [
+    'TCGA-LGG', 'CrawlData.ipynb', '.DS_Store',
+    'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)',
+    'TCGA_Thymoma_(THYM)', 'TCGA_Testicular_Cancer_(TGCT)',
+    'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)',
+    'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Pancreatic_Cancer_(PAAD)',
+    'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)',
+    'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Lung_Cancer_(LUNG)',
+    'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)',
+    'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)',
+    'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)',
+    'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)',
+    'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)',
+    'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)',
+    'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+trait_lower = trait.lower()
+relevant_folder = None
+
+# Look for any subdirectory name containing terms relevant to our trait name
+for folder in subdirectories:
+    folder_lower = folder.lower()
+    # Simple direct matching approach
+    if "substance" in folder_lower or "use" in folder_lower or "disorder" in folder_lower:
+        relevant_folder = folder
+        break
+
+# If we don't find a matching folder, skip this trait
+if not relevant_folder:
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print(f"No suitable directory found for trait {trait}. Skipping this trait.")
+else:
+    # If we did find a relevant directory (unlikely for this trait), load the files.
+    folder_path = os.path.join(tcga_root_dir, relevant_folder)
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(folder_path)
+
+    clinical_data = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_data = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+    print("Clinical data columns:", clinical_data.columns.tolist())

p1/preprocess/Substance_Use_Disorder/gene_data/GSE138297.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de9565415a246beb680e5ba05e15de50eccfec4d24f558d0cba022302313ca04
+size 15408302

p1/preprocess/Telomere_Length/code/GSE16058.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Telomere_Length"
+cohort = "GSE16058"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Telomere_Length"
+in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE16058"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Telomere_Length/GSE16058.csv"
+out_gene_data_file = "./output/preprocess/1/Telomere_Length/gene_data/GSE16058.csv"
+out_clinical_data_file = "./output/preprocess/1/Telomere_Length/clinical_data/GSE16058.csv"
+json_path = "./output/preprocess/1/Telomere_Length/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 "Gene transcript", indicating gene expression data.
+
+# 2) Variable Availability and Data Type Conversion
+#    From inspecting the sample characteristics, there is no clear key containing telomere length, age, or gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+def convert_trait(value: str):
+    # This dataset does not explicitly provide telomere length measurements,
+    # so we'll return None.
+    return None
+
+def convert_age(value: str):
+    # No age information is available, return None.
+    return None
+
+def convert_gender(value: str):
+    # No gender information is available, return None.
+    return None
+
+# 3) Save Metadata (Initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+#    Skipped, because trait_row is None.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1) Identify the appropriate columns in the gene annotation DataFrame that correspond to the probe identifiers 
+#    and the gene symbols, respectively.
+#    From the preview, "ID" matches the gene expression data index, and "Gene Symbol" provides the gene symbols.
+
+# 2) Construct the mapping dataframe between the gene ID and the gene symbol.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3) Convert probe-level measurements to gene-level expression by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+import pandas as pd
+
+# Check if clinical data (and thus the trait) was actually extracted in a previous step
+# by testing whether "selected_clinical_df" exists.
+try:
+    selected_clinical_df
+    trait_data_available = True
+except NameError:
+    trait_data_available = False
+
+if not trait_data_available:
+    # Since there's no clinical DataFrame, we finalize the dataset as unusable for trait analysis
+    # (missing trait), and skip further processing.
+    empty_df = pd.DataFrame()
+    # Mark trait as biased (or simply unusable) to ensure the final validation flags it as not usable
+    is_trait_biased = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,   # We do have gene data, but no trait data
+        is_trait_available=False,
+        is_biased=is_trait_biased,
+        df=empty_df,
+        note="No trait data found; dataset is not usable for trait-based analysis."
+    )
+else:
+    # 1. Normalize the obtained gene data and save
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and gene expression data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically using the trait column name in the 'trait' variable
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features (trait, age, gender) using the same trait column name
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note=f"Preprocessed with trait column named '{trait}'."
+    )
+
+    # 6. If usable, save linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Telomere_Length/code/GSE52237.py

@@ -0,0 +1,181 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Telomere_Length"
+cohort = "GSE52237"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Telomere_Length"
+in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE52237"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Telomere_Length/GSE52237.csv"
+out_gene_data_file = "./output/preprocess/1/Telomere_Length/gene_data/GSE52237.csv"
+out_clinical_data_file = "./output/preprocess/1/Telomere_Length/clinical_data/GSE52237.csv"
+json_path = "./output/preprocess/1/Telomere_Length/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+is_gene_available = True  # From the series description mentioning gene expression analysis
+
+# 2. Determine data availability for trait, age, and gender
+#    Based on the sample characteristics dictionary, none of these variables appear.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions (though we have no actual data rows)
+def convert_trait(value: str):
+    # Telomere length is continuous if present, but here it's unavailable.
+    # We'll define a generic continuous conversion.
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_age(value: str):
+    # Age is continuous if present, but here it's unavailable.
+    # We'll define a generic continuous conversion.
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Convert female to 0 and male to 1 if present, but here it's unavailable.
+    # We'll define a generic binary conversion.
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()
+    if val_str in ['female', 'f']:
+        return 0
+    elif val_str in ['male', 'm']:
+        return 1
+    return None
+
+# 3. Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. 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])
+# These gene identifiers appear to be Affymetrix probe set IDs (e.g. "1007_s_at"), not human gene symbols.
+# Therefore, gene mapping 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. Identify the matching columns in the gene annotation dataframe
+identifier_col = "ID"           # The probe identifier column
+symbol_col = "Gene Symbol"      # The gene symbol column
+
+# 2. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=identifier_col, gene_col=symbol_col)
+
+# 3. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print information for verification
+print("Gene-level data shape:", gene_data.shape)
+print("First 10 gene symbols in the mapped data:", list(gene_data.index[:10]))
+# STEP7
+
+import pandas as pd
+
+# Check if clinical data (and thus the trait) was actually extracted in a previous step
+# by testing whether "selected_clinical_df" exists.
+try:
+    selected_clinical_df
+    trait_data_available = True
+except NameError:
+    trait_data_available = False
+
+if not trait_data_available:
+    # Since there's no clinical DataFrame, we finalize the dataset as unusable for trait analysis
+    # (missing trait), and skip further processing.
+    empty_df = pd.DataFrame()
+    # Mark trait as biased (or simply unusable) to ensure the final validation flags it as not usable
+    is_trait_biased = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,   # We do have gene data, but no trait data
+        is_trait_available=False,
+        is_biased=is_trait_biased,
+        df=empty_df,
+        note="No trait data found; dataset is not usable for trait-based analysis."
+    )
+else:
+    # 1. Normalize the obtained gene data and save
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and gene expression data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically using the trait column name in the 'trait' variable
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features (trait, age, gender) using the same trait column name
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note=f"Preprocessed with trait column named '{trait}'."
+    )
+
+    # 6. If usable, save linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Telomere_Length/code/GSE80435.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Telomere_Length"
+cohort = "GSE80435"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Telomere_Length"
+in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE80435"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Telomere_Length/GSE80435.csv"
+out_gene_data_file = "./output/preprocess/1/Telomere_Length/gene_data/GSE80435.csv"
+out_clinical_data_file = "./output/preprocess/1/Telomere_Length/clinical_data/GSE80435.csv"
+json_path = "./output/preprocess/1/Telomere_Length/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # From background info: "Expression arrays were used..."
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics dictionary:
+# {0: ['region: Lymph nodes- Inguinal', 'region: Lymph nodes- Axilla', 'region: Lymph nodes- Groin']}
+# None of these rows provide data for 'Telomere_Length', 'age', or 'gender'.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions (though they won't be used here since all rows are None)
+def convert_trait(value: str):
+    try:
+        # Hypothetical splitting on colon
+        part = value.split(":")[-1].strip()
+        return float(part) if part else None
+    except:
+        return None
+
+def convert_age(value: str):
+    try:
+        part = value.split(":")[-1].strip()
+        return float(part) if part else None
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Convert female->0, male->1. Otherwise None.
+    part = value.split(":")[-1].strip().lower()
+    if 'female' in part:
+        return 0
+    elif 'male' in part:
+        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,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# We skip this step because trait_row is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns in the gene annotation match our probe IDs and gene symbols.
+#    From our preview, the "ID" column matches probe identifiers (e.g., ILMN_1825594),
+#    and the "Symbol" column contains gene symbols (though some may be special locus IDs).
+
+# 2. Get a dataframe mapping probe IDs to gene symbols.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3. Convert probe-level measurements to gene-level by applying the mapping.
+#    Each probe that maps to multiple genes is split, then summed for each gene.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: to quickly inspect the new gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data index (sample):", gene_data.index[:10])
+# STEP7
+
+import pandas as pd
+
+# Check if clinical data (and thus the trait) was actually extracted in a previous step
+# by testing whether "selected_clinical_df" exists.
+try:
+    selected_clinical_df
+    trait_data_available = True
+except NameError:
+    trait_data_available = False
+
+if not trait_data_available:
+    # Since there's no clinical DataFrame, we finalize the dataset as unusable for trait analysis
+    # (missing trait), and skip further processing.
+    empty_df = pd.DataFrame()
+    # Mark trait as biased (or simply unusable) to ensure the final validation flags it as not usable
+    is_trait_biased = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,   # We do have gene data, but no trait data
+        is_trait_available=False,
+        is_biased=is_trait_biased,
+        df=empty_df,
+        note="No trait data found; dataset is not usable for trait-based analysis."
+    )
+else:
+    # 1. Normalize the obtained gene data and save
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and gene expression data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically using the trait column name in the 'trait' variable
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features (trait, age, gender) using the same trait column name
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note=f"Preprocessed with trait column named '{trait}'."
+    )
+
+    # 6. If usable, save linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Telomere_Length/code/TCGA.py

@@ -0,0 +1,74 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Telomere_Length"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Telomere_Length/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Telomere_Length/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Telomere_Length/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Telomere_Length/cohort_info.json"
+
+# Step 1: Initial Data Loading
+
+import os
+import pandas as pd
+
+# List subdirectories (as already given in the environment)
+subdirectories = [
+    'TCGA-LGG', '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)'
+]
+
+# Define synonyms or keywords relevant to "Telomere_Length"
+telomere_synonyms = ["telomere"]
+
+relevant_folder = None
+
+for folder in subdirectories:
+    folder_lower = folder.lower()
+    if any(syn in folder_lower for syn in telomere_synonyms):
+        relevant_folder = folder
+        break
+
+if not relevant_folder:
+    # No suitable directory found, mark as skipped
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print(f"No suitable directory found for trait {trait}. Skipping this trait.")
+else:
+    # If a relevant directory is found, load the files
+    folder_path = os.path.join(tcga_root_dir, relevant_folder)
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(folder_path)
+
+    clinical_data = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_data = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+    print("Clinical data columns:", clinical_data.columns.tolist())

p1/preprocess/Telomere_Length/cohort_info.json

@@ -0,0 +1 @@
+{"GSE80435": {"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 found; dataset is not usable for trait-based analysis."}, "GSE52237": {"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 found; dataset is not usable for trait-based analysis."}, "GSE16058": {"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 found; dataset is not usable for trait-based analysis."}, "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/Testicular_Cancer/code/GSE42647.py

@@ -0,0 +1,60 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Testicular_Cancer"
+cohort = "GSE42647"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Testicular_Cancer"
+in_cohort_dir = "../DATA/GEO/Testicular_Cancer/GSE42647"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Testicular_Cancer/GSE42647.csv"
+out_gene_data_file = "./output/preprocess/1/Testicular_Cancer/gene_data/GSE42647.csv"
+out_clinical_data_file = "./output/preprocess/1/Testicular_Cancer/clinical_data/GSE42647.csv"
+json_path = "./output/preprocess/1/Testicular_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 information and sample characteristics, this dataset appears
+#    to be about a cell line treated with a demethylating agent, without clear evidence
+#    of standard gene expression profiling.
+is_gene_available = False
+
+# 2) Variable Availability and Data Type Conversion
+#    The dictionary only shows cell line and cell type info with no meaningful
+#    (variable) differences for trait, age, or gender. Therefore, none are available.
+trait_row = None
+age_row = None
+gender_row = 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
+#    Since trait_row is None, we skip this step.

p1/preprocess/Testicular_Cancer/code/GSE62523.py

@@ -0,0 +1,73 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Testicular_Cancer"
+cohort = "GSE62523"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Testicular_Cancer"
+in_cohort_dir = "../DATA/GEO/Testicular_Cancer/GSE62523"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Testicular_Cancer/GSE62523.csv"
+out_gene_data_file = "./output/preprocess/1/Testicular_Cancer/gene_data/GSE62523.csv"
+out_clinical_data_file = "./output/preprocess/1/Testicular_Cancer/clinical_data/GSE62523.csv"
+json_path = "./output/preprocess/1/Testicular_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  # From the background info, this dataset is a gene expression dataset.
+
+# 2. Variable Availability and Data Type Conversion
+#    The sample characteristics dictionary is:
+#    {0: ['cell line: HMEC-1'], 1: ['cell type: human microvascular endothelial cell line']}
+#
+#    None of these correspond to our trait ("Testicular_Cancer"), age, or gender in a usable way.
+#    Therefore, all three are considered unavailable.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions. Although no data is available, we still define them per requirement.
+def convert_trait(value: str) -> int:
+    return None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    return None
+
+# 3. Save Metadata - initial filtering based on availability of gene data and trait data
+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
+)
+print("Initial filtering result:", is_usable)
+
+# 4. Clinical Feature Extraction
+#    trait_row is None, so we skip extraction of clinical features.

p1/preprocess/Testicular_Cancer/code/TCGA.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Testicular_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Testicular_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Testicular_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Testicular_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Testicular_Cancer/cohort_info.json"
+
+# Step 1: Initial Data Loading
+
+import os
+import pandas as pd
+
+# List subdirectories (as already given in the environment)
+subdirectories = [
+    'TCGA-LGG', '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)'
+]
+
+# We want to find a folder relevant to "Testicular_Cancer"
+testicular_synonyms = ["testicular", "tgct"]
+
+relevant_folder = None
+
+for folder in subdirectories:
+    folder_lower = folder.lower()
+    if any(syn in folder_lower for syn in testicular_synonyms):
+        relevant_folder = folder
+        break
+
+if not relevant_folder:
+    # No suitable directory found, mark as skipped
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print(f"No suitable directory found for trait {trait}. Skipping this trait.")
+else:
+    # If a relevant directory is found, load the files
+    folder_path = os.path.join(tcga_root_dir, relevant_folder)
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(folder_path)
+
+    clinical_data = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_data = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+    print("Clinical data columns:", clinical_data.columns.tolist())
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "undescended_testis_corrected_age", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+# Extract candidate columns
+age_data = clinical_data[candidate_age_cols] if candidate_age_cols else pd.DataFrame()
+gender_data = clinical_data[candidate_gender_cols] if candidate_gender_cols else pd.DataFrame()
+
+# Preview the extracted columns
+age_preview = preview_df(age_data, n=5) if not age_data.empty else {}
+gender_preview = preview_df(gender_data, n=5) if not gender_data.empty else {}
+
+age_preview, gender_preview
+import re
+
+# Example dictionaries from a previous step (replace with actual dictionaries)
+age_candidates = {
+    "age_at_diagnosis": ["45", "48", "nan", None, "52"],
+    "patient_age": ["42", "forty", "37", "45", None]
+}
+gender_candidates = {
+    "gender": ["male", "female", "male", "female", "male"],
+    "sex_info": ["MALE", None, "n/a", "FEMALE", "FEMALE"]
+}
+
+def column_has_sufficient_valid_age(values, threshold=0.7):
+    valid_count = 0
+    for val in values:
+        if val is None:
+            continue
+        match = re.search(r'\d+', str(val))
+        if match:
+            valid_count += 1
+    return (valid_count / len(values)) >= threshold if values else False
+
+def column_has_sufficient_valid_gender(values, threshold=0.7):
+    valid_count = 0
+    for val in values:
+        if val is None:
+            continue
+        val_lower = str(val).lower()
+        if val_lower in ["male", "female"]:
+            valid_count += 1
+    return (valid_count / len(values)) >= threshold if values else False
+
+age_col = None
+gender_col = None
+
+# Find the first suitable age column
+for col_name, vals in age_candidates.items():
+    if column_has_sufficient_valid_age(vals):
+        age_col = col_name
+        break
+
+# Find the first suitable gender column
+for col_name, vals in gender_candidates.items():
+    if column_has_sufficient_valid_gender(vals):
+        gender_col = col_name
+        break
+
+print("Chosen age_col:", age_col)
+print("Chosen gender_col:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_data,
+    trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2. Normalize gene symbols in the genetic data and save the result
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data on sample IDs
+linked_data = selected_clinical_df.join(normalized_gene_data.T, how='inner')
+
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Determine whether the trait (and other features) are severely biased; remove biased age or gender if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Standard STAD pipeline complete."
+)
+
+# 7. If usable, save the fully linked and processed data
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Testicular_Cancer/cohort_info.json

@@ -0,0 +1 @@
+{"GSE62523": {"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}, "GSE42647": {"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}, "TCGA": {"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": 156, "note": "Standard STAD pipeline complete."}}

p1/preprocess/Thymoma/GSE42977.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:292745155401b16941bd7e6f2ccc1082deeac09b3dd619d38e01ed63455b4b7a
+size 24576654

p1/preprocess/Thymoma/clinical_data/GSE131027.csv

@@ -0,0 +1,2 @@
+GSM3759992,GSM3759993,GSM3759994,GSM3759995,GSM3759996,GSM3759997,GSM3759998,GSM3759999,GSM3760000,GSM3760001,GSM3760002,GSM3760003,GSM3760004,GSM3760005,GSM3760006,GSM3760007,GSM3760008,GSM3760009,GSM3760010,GSM3760011,GSM3760012,GSM3760013,GSM3760014,GSM3760015,GSM3760016,GSM3760017,GSM3760018,GSM3760019,GSM3760020,GSM3760021,GSM3760022,GSM3760023,GSM3760024,GSM3760025,GSM3760026,GSM3760027,GSM3760028,GSM3760029,GSM3760030,GSM3760031,GSM3760032,GSM3760033,GSM3760034,GSM3760035,GSM3760036,GSM3760037,GSM3760038,GSM3760039,GSM3760040,GSM3760041,GSM3760042,GSM3760043,GSM3760044,GSM3760045,GSM3760046,GSM3760047,GSM3760048,GSM3760049,GSM3760050,GSM3760051,GSM3760052,GSM3760053,GSM3760054,GSM3760055,GSM3760056,GSM3760057,GSM3760058,GSM3760059,GSM3760060,GSM3760061,GSM3760062,GSM3760063,GSM3760064,GSM3760065,GSM3760066,GSM3760067,GSM3760068,GSM3760069,GSM3760070,GSM3760071,GSM3760072,GSM3760073,GSM3760074,GSM3760075,GSM3760076,GSM3760077,GSM3760078,GSM3760079,GSM3760080,GSM3760081,GSM3760082,GSM3760083
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p1/preprocess/Thymoma/clinical_data/GSE42977.csv

@@ -0,0 +1,2 @@
+GSM1054230,GSM1054231,GSM1054232,GSM1054233,GSM1054234,GSM1054235,GSM1054236,GSM1054237,GSM1054238,GSM1054239,GSM1054240,GSM1054241,GSM1054242,GSM1054243,GSM1054244,GSM1054245,GSM1054246,GSM1054247,GSM1054248,GSM1054249,GSM1054250,GSM1054251,GSM1054252,GSM1054253,GSM1054254,GSM1054255,GSM1054256,GSM1054257,GSM1054258,GSM1054259,GSM1054260,GSM1054261,GSM1054262,GSM1054263,GSM1054264,GSM1054265,GSM1054266,GSM1054267,GSM1054268,GSM1054269,GSM1054270,GSM1054271,GSM1054272,GSM1054273,GSM1054274,GSM1054275,GSM1054276,GSM1054277,GSM1054278,GSM1054279,GSM1054280,GSM1054281,GSM1054282,GSM1054283,GSM1054284,GSM1054285,GSM1054286,GSM1054287,GSM1054288,GSM1054289,GSM1054290,GSM1054291,GSM1054292,GSM1054293,GSM1054294,GSM1054295,GSM1054296,GSM1054297,GSM1054298,GSM1054299,GSM1054300,GSM1054301,GSM1054302,GSM1054303,GSM1054304,GSM1054305,GSM1054306,GSM1054307,GSM1054308,GSM1054309,GSM1054310,GSM1054311,GSM1054312,GSM1054313,GSM1054314,GSM1054315,GSM1054316,GSM1054317,GSM1054318,GSM1054319,GSM1054320,GSM1054321,GSM1054322,GSM1054323,GSM1054324,GSM1054325,GSM1054326,GSM1054327,GSM1054328,GSM1054329,GSM1054330,GSM1054331,GSM1054332,GSM1054333,GSM1054334,GSM1054335,GSM1054336,GSM1054337,GSM1054338,GSM1054339,GSM1054340,GSM1054341,GSM1054342,GSM1054343,GSM1054344,GSM1054345,GSM1054346
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p1/preprocess/Thymoma/code/GSE131027.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Thymoma"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Thymoma"
+in_cohort_dir = "../DATA/GEO/Thymoma/GSE131027"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Thymoma/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/1/Thymoma/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/1/Thymoma/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/1/Thymoma/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. Decide whether gene expression data is available
+is_gene_available = True  # Based on the series description focusing on "expression features"
+
+# 2. Check the availability of trait, age, and gender
+# From the sample characteristics, we see key=1 contains "cancer: Thymoma" among other types.
+# Hence trait data is available at row=1. There's no mention of age or gender keys.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    label = parts[1].strip().lower()
+    # Convert "Thymoma" to 1, everything else to 0
+    return 1 if "thymoma" in label else 0
+
+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. Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+pass_filter = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Extract clinical features only if trait data is available and we have not filtered out
+if (not pass_filter) and is_trait_available:
+    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 = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "1007_s_at", "1053_at", etc.) are Affymetrix probe set IDs, not human gene symbols.
+# Therefore, they require mapping to official gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. The gene annotation DataFrame has the Affymetrix probe identifiers in column 'ID', which matches the index of gene_data.
+#    The corresponding human gene symbols are in the column 'Gene Symbol'.
+
+# 2. Extract the two columns to get a mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level expression data to gene-level expression data using the many-to-many mapping approach
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+import pandas as pd
+
+# Check if clinical data (and thus the trait) was actually extracted in a previous step
+# by testing whether "selected_clinical_df" exists.
+try:
+    selected_clinical_df
+    trait_data_available = True
+except NameError:
+    trait_data_available = False
+
+if not trait_data_available:
+    # Since there's no clinical DataFrame, we finalize the dataset as unusable for trait analysis
+    # (missing trait), and skip further processing.
+    empty_df = pd.DataFrame()
+    # Mark trait as biased (or simply unusable) to ensure the final validation flags it as not usable
+    is_trait_biased = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,   # We do have gene data, but no trait data
+        is_trait_available=False,
+        is_biased=is_trait_biased,
+        df=empty_df,
+        note="No trait data found; dataset is not usable for trait-based analysis."
+    )
+else:
+    # 1. Normalize the obtained gene data and save
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and gene expression data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically using the trait column name in the 'trait' variable
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features (trait, age, gender) using the same trait column name
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note=f"Preprocessed with trait column named '{trait}'."
+    )
+
+    # 6. If usable, save linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Thymoma/code/GSE29695.py

@@ -0,0 +1,156 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Thymoma"
+cohort = "GSE29695"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Thymoma"
+in_cohort_dir = "../DATA/GEO/Thymoma/GSE29695"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Thymoma/GSE29695.csv"
+out_gene_data_file = "./output/preprocess/1/Thymoma/gene_data/GSE29695.csv"
+out_clinical_data_file = "./output/preprocess/1/Thymoma/clinical_data/GSE29695.csv"
+json_path = "./output/preprocess/1/Thymoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # from background info (whole genome gene expression analysis mentioned)
+
+# 2) Variable Availability and Data Type Conversion
+# Looking at the sample characteristics dictionary, we do not see any row that contains valid/variable
+# trait data specific to "Thymoma" (all samples are thymoma-related; no variation is apparent),
+# nor do we see 'age' or 'gender' information. Hence all are set to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions (they won't be used here because all rows are None).
+def convert_trait(x: str):
+    # No valid trait data available; return None.
+    return None
+
+def convert_age(x: str):
+    # No valid age data available; return None.
+    return None
+
+def convert_gender(x: str):
+    # No valid gender data available; return None.
+    return None
+
+# 3) Save Metadata for Initial Filtering
+is_trait_available = (trait_row is not None)  # This will be 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) Clinical Feature Extraction
+# Since trait_row is None (trait not available), we skip clinical data extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the gene identifiers (ILMN_...), these are likely Illumina probe IDs instead of standard gene symbols.
+# Thus, they will 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. Identify the columns in gene_annotation that match the gene expression identifiers and the gene symbols
+probe_id_col = "ID"     # This corresponds to the ILMN_... ids, matching the row IDs in gene_data
+gene_symbol_col = "Symbol"  # This column holds the gene symbols
+
+# 2. Extract the gene mapping dataframe using the library function
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+import pandas as pd
+
+# Check if clinical data (and thus the trait) was actually extracted in a previous step
+# by testing whether "selected_clinical_df" exists.
+try:
+    selected_clinical_df
+    trait_data_available = True
+except NameError:
+    trait_data_available = False
+
+if not trait_data_available:
+    # Since there's no clinical DataFrame, we finalize the dataset as unusable for trait analysis
+    # (missing trait), and skip further processing.
+    empty_df = pd.DataFrame()
+    # Mark trait as biased (or simply unusable) to ensure the final validation flags it as not usable
+    is_trait_biased = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,   # We do have gene data, but no trait data
+        is_trait_available=False,
+        is_biased=is_trait_biased,
+        df=empty_df,
+        note="No trait data found; dataset is not usable for trait-based analysis."
+    )
+else:
+    # 1. Normalize the obtained gene data and save
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and gene expression data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically using the trait column name in the 'trait' variable
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features (trait, age, gender) using the same trait column name
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note=f"Preprocessed with trait column named '{trait}'."
+    )
+
+    # 6. If usable, save linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Thymoma/code/GSE42977.py

@@ -0,0 +1,170 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Thymoma"
+cohort = "GSE42977"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Thymoma"
+in_cohort_dir = "../DATA/GEO/Thymoma/GSE42977"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Thymoma/GSE42977.csv"
+out_gene_data_file = "./output/preprocess/1/Thymoma/gene_data/GSE42977.csv"
+out_clinical_data_file = "./output/preprocess/1/Thymoma/clinical_data/GSE42977.csv"
+json_path = "./output/preprocess/1/Thymoma/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 "microarray analysis" mention
+
+# 2. Variable Availability and Data Type Conversion
+#    From the sample characteristics, only one key (0) is present,
+#    which includes "tissue: Thymoma" among many other tissue types.
+#    Thus, "trait" data is available in row 0, but age and gender data
+#    are not provided.
+
+trait_row = 0
+
+def convert_trait(value: str) -> int:
+    # Extract the substring after the colon and convert to lowercase
+    val = value.split(':')[-1].strip().lower()
+    # Binary conversion: 1 if it's Thymoma (including "metastatic thymoma"), else 0
+    return 1 if "thymoma" in val else 0
+
+age_row = None
+convert_age = None  # No age data found, so no conversion function needed
+
+gender_row = None
+convert_gender = None  # No gender data found, so no conversion function needed
+
+# 3. Initial Filtering and Saving Metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save the clinical data
+    print(preview_df(selected_clinical_df, n=5))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The gene identifiers shown (e.g., "ILMN_10000", "ILMN_100000") are Illumina probe IDs,
+# not standard human gene symbols. They need to be mapped to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify the columns in gene_annotation that correspond to the probe IDs and gene symbols.
+#    From the preview, 'ID' matches the ILMN_ identifiers (same as gene_data index),
+#    and 'Symbol' stores the gene symbols.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3. Map the probe-level data to gene-level data by distributing probe expression values to mapped genes
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Optionally, inspect the resulting gene_data
+print("Gene data shape after mapping:", gene_data.shape)
+print("First 5 gene symbols after mapping:", gene_data.index[:5].tolist())
+# STEP7
+
+import pandas as pd
+
+# Check if clinical data (and thus the trait) was actually extracted in a previous step
+# by testing whether "selected_clinical_df" exists.
+try:
+    selected_clinical_df
+    trait_data_available = True
+except NameError:
+    trait_data_available = False
+
+if not trait_data_available:
+    # Since there's no clinical DataFrame, we finalize the dataset as unusable for trait analysis
+    # (missing trait), and skip further processing.
+    empty_df = pd.DataFrame()
+    # Mark trait as biased (or simply unusable) to ensure the final validation flags it as not usable
+    is_trait_biased = True
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,   # We do have gene data, but no trait data
+        is_trait_available=False,
+        is_biased=is_trait_biased,
+        df=empty_df,
+        note="No trait data found; dataset is not usable for trait-based analysis."
+    )
+else:
+    # 1. Normalize the obtained gene data and save
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and gene expression data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically using the trait column name in the 'trait' variable
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for biased features (trait, age, gender) using the same trait column name
+    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final quality validation and record metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=linked_data,
+        note=f"Preprocessed with trait column named '{trait}'."
+    )
+
+    # 6. If usable, save linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Thymoma/code/TCGA.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Thymoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Thymoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Thymoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Thymoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Thymoma/cohort_info.json"
+
+# Step 1: Initial Data Loading
+
+import os
+import pandas as pd
+
+# List subdirectories (as already given in the environment)
+subdirectories = [
+    'TCGA-LGG', '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)'
+]
+
+# We want to find a folder relevant to "Thymoma"
+trait_synonyms = ["thymoma", "thym"]
+
+relevant_folder = None
+
+for folder in subdirectories:
+    folder_lower = folder.lower()
+    if any(syn in folder_lower for syn in trait_synonyms):
+        relevant_folder = folder
+        break
+
+if not relevant_folder:
+    # No suitable directory found, mark as skipped
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print(f"No suitable directory found for trait {trait}. Skipping this trait.")
+else:
+    # If a relevant directory is found, load the files
+    folder_path = os.path.join(tcga_root_dir, relevant_folder)
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(folder_path)
+
+    clinical_data = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_data = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+    print("Clinical data columns:", clinical_data.columns.tolist())
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+if candidate_age_cols:
+    preview_age = preview_df(clinical_data[candidate_age_cols], n=5)
+    print("Age Columns Preview (first 5 rows):")
+    print(preview_age)
+
+if candidate_gender_cols:
+    preview_gender = preview_df(clinical_data[candidate_gender_cols], n=5)
+    print("Gender Columns Preview (first 5 rows):")
+    print(preview_gender)
+# Based on the previews, 'age_at_initial_pathologic_diagnosis' directly provides ages in years,
+# and 'gender' seems to consistently provide gender information.
+
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print("Chosen age column:", age_col)
+print("Chosen gender column:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_data,
+    trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2. Normalize gene symbols in the genetic data and save the result
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data on sample IDs
+linked_data = selected_clinical_df.join(normalized_gene_data.T, how='inner')
+
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Determine whether the trait (and other features) are severely biased; remove biased age or gender if needed
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Standard STAD pipeline complete."
+)
+
+# 7. If usable, save the fully linked and processed data
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Thymoma/cohort_info.json

@@ -0,0 +1 @@
+{"GSE42977": {"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": 117, "note": "Preprocessed with trait column named 'Thymoma'."}, "GSE29695": {"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 found; dataset is not usable for trait-based analysis."}, "GSE131027": {"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": 92, "note": "Preprocessed with trait column named 'Thymoma'."}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 122, "note": "Standard STAD pipeline complete."}}

p1/preprocess/Thymoma/gene_data/GSE131027.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8582008fe6fc5b90879aea7d12ca4ea58cc0bbe03209acb333425d229be6e33
+size 24379939

p1/preprocess/Thymoma/gene_data/GSE42977.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ee4aba8ed3943a5e7f98022325bcf90252f7fb056f558e0e99dd7f361acd56f8
+size 24576182

p1/preprocess/Thyroid_Cancer/GSE151179.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d8a00b1cdaa951ccf347dc554e3e8b1ea163868f9950d06ffc59a67bbad5128
+size 18389228

p1/preprocess/Thyroid_Cancer/GSE80022.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9a4a1510359a0679f76dd7798a706daeecc1fbfa0481d594dfd91d016f9dd7e
+size 14562176

p1/preprocess/Thyroid_Cancer/GSE82208.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b37d20eadfbf260e3ad50b28e2cbd9ccacc6a3a80cb45ffd0898aeb6b52b834
+size 13702981

p1/preprocess/Thyroid_Cancer/clinical_data/GSE104005.csv

@@ -0,0 +1,4 @@
+GSM2787612,GSM2787613,GSM2787614,GSM2787615,GSM2787616,GSM2787617,GSM2787618,GSM2787619,GSM2787620,GSM2787621,GSM2787622,GSM2787623,GSM2787624,GSM2787625,GSM2787626,GSM2787627,GSM2787628,GSM2787629,GSM2787630,GSM2787631,GSM2787632,GSM2787633,GSM2787634,GSM2787635,GSM2787636,GSM2787637,GSM2787638,GSM2787639,GSM2787640,GSM2787641,GSM2787642,GSM2787643,GSM2787644,GSM2787645
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0
+74.0,74.0,67.0,72.0,74.0,38.0,50.0,41.0,51.0,73.0,52.0,48.0,59.0,58.0,39.0,37.0,33.0,36.0,70.0,26.0,46.0,57.0,44.0,44.0,35.0,42.0,61.0,38.0,35.0,35.0,38.0,49.0,52.0,51.0
+1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0

p1/preprocess/Thyroid_Cancer/clinical_data/GSE104006.csv

@@ -0,0 +1,4 @@
+GSM2787513,GSM2787514,GSM2787515,GSM2787516,GSM2787517,GSM2787518,GSM2787519,GSM2787520,GSM2787521,GSM2787522,GSM2787523,GSM2787524,GSM2787525,GSM2787526,GSM2787527,GSM2787528,GSM2787529,GSM2787530,GSM2787531,GSM2787532,GSM2787533,GSM2787534,GSM2787535,GSM2787536,GSM2787537,GSM2787538,GSM2787539,GSM2787540,GSM2787541,GSM2787542,GSM2787543,GSM2787544,GSM2787545,GSM2787546
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0
+74.0,74.0,72.0,74.0,38.0,50.0,41.0,51.0,73.0,52.0,48.0,59.0,58.0,39.0,37.0,33.0,36.0,70.0,26.0,46.0,57.0,44.0,35.0,42.0,47.0,61.0,38.0,35.0,35.0,38.0,49.0,56.0,52.0,51.0
+1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0

p1/preprocess/Thyroid_Cancer/clinical_data/GSE107754.csv

@@ -0,0 +1,3 @@
+GSM2878070,GSM2878071,GSM2878072,GSM2878073,GSM2878074,GSM2878075,GSM2878076,GSM2878077,GSM2878078,GSM2878079,GSM2878080,GSM2878081,GSM2878082,GSM2891194,GSM2891195,GSM2891196,GSM2891197,GSM2891198,GSM2891199,GSM2891200,GSM2891201,GSM2891202,GSM2891203,GSM2891204,GSM2891205,GSM2891206,GSM2891207,GSM2891208,GSM2891209,GSM2891210,GSM2891211,GSM2891212,GSM2891213,GSM2891214,GSM2891215,GSM2891216,GSM2891217,GSM2891218,GSM2891219,GSM2891220,GSM2891221,GSM2891222,GSM2891223,GSM2891224,GSM2891225,GSM2891226,GSM2891227,GSM2891228,GSM2891229,GSM2891230,GSM2891231,GSM2891232,GSM2891233,GSM2891234,GSM2891235,GSM2891236,GSM2891237,GSM2891238,GSM2891239,GSM2891240,GSM2891241,GSM2891242,GSM2891243,GSM2891244,GSM2891245,GSM2891246,GSM2891247,GSM2891248,GSM2891249,GSM2891250,GSM2891251,GSM2891252,GSM2891253,GSM2891254,GSM2891255,GSM2891256,GSM2891257,GSM2891258,GSM2891259,GSM2891260,GSM2891261,GSM2891262,GSM2891263,GSM2891264
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0