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@@ -1260,3 +1260,14 @@ p1/preprocess/Lower_Grade_Glioma/gene_data/GSE35158.csv filter=lfs diff=lfs merg
 p1/preprocess/Longevity/GSE48264.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Hypothyroidism/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Longevity/gene_data/GSE48264.csv filter=lfs diff=lfs merge=lfs -text
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p1/preprocess/Kidney_Papillary_Cell_Carcinoma/gene_data/TCGA.csv

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p1/preprocess/Lung_Cancer/gene_data/GSE244123.csv

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p1/preprocess/Lung_Cancer/gene_data/GSE244647.csv

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p1/preprocess/Lung_Cancer/gene_data/GSE248830.csv

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p1/preprocess/Lung_Cancer/gene_data/GSE249262.csv

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p1/preprocess/Lung_Cancer/gene_data/GSE249568.csv

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p1/preprocess/Lung_Cancer/gene_data/TCGA.csv

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p1/preprocess/Melanoma/clinical_data/GSE144296.csv

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p1/preprocess/Melanoma/clinical_data/GSE148319.csv

@@ -0,0 +1,2 @@
+GSM4460266,GSM4460267,GSM4460268,GSM4460269,GSM4460270,GSM4460271,GSM4460272,GSM4460273,GSM4460274,GSM4460275,GSM4460276,GSM4460277,GSM4460278,GSM4460279,GSM4460280,GSM4460281,GSM4460282,GSM4460283,GSM4460284,GSM4460285,GSM4460286,GSM4460287,GSM4460288,GSM4460289,GSM4460290,GSM4460291,GSM4460292,GSM4460293,GSM4460294,GSM4460295,GSM4460296,GSM4460297,GSM4460298,GSM4460299,GSM4460300,GSM4460301,GSM4460302,GSM4460303,GSM4460304,GSM4460305,GSM4460306,GSM4460307,GSM4460308,GSM4460309,GSM4460310,GSM4460311,GSM4460312,GSM4460313,GSM4460314,GSM4460315,GSM4460316,GSM4460317,GSM4460318,GSM4460319,GSM4460320,GSM4460321,GSM4460322,GSM4460323,GSM4460324,GSM4460325,GSM4460326,GSM4460327,GSM4460328,GSM4460329,GSM4460330,GSM4460331,GSM4460332,GSM4460333,GSM4460334,GSM4460335,GSM4460336,GSM4460337,GSM4460338,GSM4460339,GSM4460340,GSM4460341,GSM4460342,GSM4460343,GSM4460344,GSM4460345,GSM4460346,GSM4460347,GSM4460348
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p1/preprocess/Melanoma/code/GSE144296.py

@@ -0,0 +1,117 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE144296"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE144296"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE144296.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE144296.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE144296.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 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("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on DNTR-seq description providing mRNA expression
+
+# 2. Variable Availability and Data Type Conversion
+trait_row = 1     # "cell type: malignant melanoma" or "cell type: colorectal carcinoma"
+age_row = None    # No age information detected
+gender_row = None # No gender information detected
+
+def convert_trait(value: str):
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if "malignant melanoma" in val:
+        return 1
+    elif "colorectal carcinoma" in val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    # Not used here, but defined for completeness
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Not used here, but defined for completeness
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val in ["male", "m"]:
+        return 1
+    elif val in ["female", "f"]:
+        return 0
+    return None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    previewed_data = preview_df(selected_clinical_df, n=5)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# 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])

p1/preprocess/Melanoma/code/GSE146264.py

@@ -0,0 +1,124 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE146264"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE146264"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE146264.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE146264.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE146264.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 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("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+#    Based on the background info indicating single-cell RNA-seq, we set is_gene_available to True.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+#    From the sample characteristics dictionary, we see no mention of "Melanoma," age, or gender.
+#    Therefore, we conclude these variables are not available (None).
+trait_row = None
+age_row = None
+gender_row = None
+
+#    Nonetheless, define the required converter functions (they will not be used because rows are None).
+
+def convert_trait(x: str) -> int:
+    # Placeholder: no real conversion as trait data is unavailable
+    return None
+
+def convert_age(x: str) -> float:
+    # Placeholder: no real conversion as age data is unavailable
+    return None
+
+def convert_gender(x: str) -> int:
+    # Placeholder: no real conversion as gender data is unavailable
+    return None
+
+# 3. Save Metadata with initial filtering
+#    Trait data is considered unavailable (trait_row is None).
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip this step (no clinical data extraction).
+# STEP3
+import gzip
+import io
+
+def my_get_genetic_data(file_path: str) -> pd.DataFrame:
+    """
+    Reads a GEO series matrix file (whether gzipped or plain) by ignoring lines starting with '!'
+    and then parsing the remainder as tab-delimited data.
+    Renames 'ID_REF' to 'ID' if present and sets it as the index.
+    """
+    # Determine if the file is gzipped by checking the first two bytes
+    with open(file_path, 'rb') as f:
+        magic = f.read(2)
+    is_gzipped = (magic == b'\x1f\x8b')
+
+    # Open the file accordingly
+    if is_gzipped:
+        fhandle = gzip.open(file_path, 'rt')
+    else:
+        fhandle = open(file_path, 'r')
+
+    # Read all lines except those starting with '!'
+    with fhandle:
+        content = [line for line in fhandle if not line.startswith('!')]
+
+    # Parse the remaining content as tab-delimited
+    df = pd.read_csv(io.StringIO(''.join(content)), sep='\t', comment='!', on_bad_lines='skip')
+
+    # Rename 'ID_REF' to 'ID' if present and set as index
+    if 'ID_REF' in df.columns:
+        df = df.rename(columns={'ID_REF': 'ID'}).astype({'ID': 'str'})
+        df.set_index('ID', inplace=True)
+
+    return df
+
+# Use the custom function to attempt reading the gene expression data
+gene_data = my_get_genetic_data(matrix_file)
+
+# Print the first 20 row IDs to check if we obtained any data
+print(gene_data.index[:20])

p1/preprocess/Melanoma/code/GSE148319.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE148319"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE148319"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE148319.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE148319.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE148319.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 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("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on the overview, this dataset appears to measure gene expression data.
+
+# 2) Variable Availability and Data Type Conversion
+#    From the sample characteristics, row 3 contains "biological replicate" info, including "hs.Melanoma.biolrep.x".
+#    We can interpret it as indicating whether a sample is melanoma (1) or not (0).
+
+trait_row = 3
+age_row = None   # No 'age' info found
+gender_row = None  # No 'gender' info found
+
+def convert_trait(value: str) -> Optional[int]:
+    if not isinstance(value, str):
+        return None
+    # Split at the colon, take the second part
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Mark as 1 if 'melanoma' is in the string, else 0
+    return 1 if "melanoma" in val else 0
+
+# No conversion needed for age and gender as they are not available
+convert_age = None
+convert_gender = 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
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,      # assume 'clinical_data' DataFrame is provided
+        trait=trait,        # "Melanoma"
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", 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])
+# The provided identifiers (e.g., "11715100_at") appear to be Affymetrix probe set IDs.
+# They are not human gene symbols and 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 which columns store the probe identifiers and the gene symbols:
+#    From the annotation preview, "ID" matches the probe identifiers in gene_data.index,
+#    and "Gene Symbol" holds the actual human gene symbols.
+
+# 2) Extract the "ID" and "Gene Symbol" columns to create a mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3) Apply the gene mapping to convert probe-level data into gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+import pandas as pd
+
+# 1) Normalize the gene symbols and save the result
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Read the single-row clinical data saved in step 2, then set its row index to the trait name
+#    so that geo_link_clinical_genetic_data can correctly place "Melanoma" as a column.
+selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+selected_clinical_df.index = [trait]
+
+# 3) Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 4) Handle missing values
+processed_data = handle_missing_values(df=linked_data, trait_col=trait)
+
+# 5) Check for trait bias (and remove age/gender if present and biased)
+is_trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 6) Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=processed_data,
+    note="Preprocessing completed. Trait data present and gene data mapped."
+)
+
+# 7) Save the final linked data if usable
+if is_usable:
+    processed_data.to_csv(out_data_file, index=False)

p1/preprocess/Melanoma/code/GSE148949.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE148949"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE148949"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE148949.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE148949.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE148949.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 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("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1) Decide if this dataset contains gene expression data
+is_gene_available = True  # Based on "whole human genome Agilent arrays" info
+
+# 2) Determine availability for trait, age, and gender
+trait_row = None     # No row with distinct "Melanoma" variation found
+age_row = None       # No row indicating age
+gender_row = None    # No row indicating gender
+
+# 2.2) Define data conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the trait value to our chosen data type.
+    Since trait data is unavailable, we'll return None.
+    """
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert the age value to a continuous type.
+    Since age data is unavailable, we'll return None.
+    """
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert the gender value to binary (female=0, male=1).
+    Since gender data is unavailable, we'll return None.
+    """
+    return None
+
+# 3) Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Since trait_row is None, we skip clinical feature extraction
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. From the preview, the 'ID' column in gene_annotation matches the identifier type 
+#    seen in gene_data, and the 'ORF' column stores the gene symbols.
+
+# 2. Build the mapping dataframe, using 'ID' as the probe column and 'ORF' as the gene symbol column.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# 3. Convert the probe-level data in 'gene_data' to gene-level data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print some information about the resulting gene data
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 10 genes after mapping:", gene_data.index[:10].tolist())
+# STEP5
+
+# 1) Normalize the gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Because we found no trait data (trait_row=None), there is no clinical dataframe to link.
+#    We skip linking clinical data and related steps.
+
+# 3) We also skip handling missing trait/covariate values, since no trait is present.
+
+# 4) We skip bias checking on the trait for the same reason (no trait).
+
+# 5) Perform final quality validation and save metadata.
+#    Although we have gene data, the trait is unavailable, so this dataset is not usable for trait-gene association.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,   # No trait data
+    is_biased=False,            # Arbitrarily False; lack of trait data makes is_usable=False anyway
+    df=normalized_gene_data,
+    note="No trait data available for cohort GSE200904; gene data alone cannot support association analysis."
+)
+
+# 6) Since the dataset is not usable (no trait), we do not save a final linked data file.
+if is_usable:
+    # This block won't run because is_usable is False.
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Melanoma/code/GSE157738.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE157738"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE157738"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE157738.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE157738.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE157738.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 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("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+# From the background info, data is from "Affymetrix Human Gene 2.0 ST Array," indicating gene expression data is present.
+is_gene_available = True
+
+# 2. Check availability for 'trait' (Melanoma), 'age', and 'gender'
+#    According to the sample characteristics, 'patient diagnosis: melanoma' is the same for all patients => no variation.
+#    There's no information for age or gender. Therefore, we set them to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define conversion functions.
+def convert_trait(value: str) -> int:
+    """
+    For demonstration, returns None because trait data row is not available in this dataset.
+    """
+    return None
+
+def convert_age(value: str) -> float:
+    """
+    Returns None because age info is not available in this dataset.
+    """
+    return None
+
+def convert_gender(value: str) -> int:
+    """
+    Returns None because gender info is not available in this dataset.
+    """
+    return None
+
+# 3. Save metadata (initial filtering)
+#    Trait availability is False because trait_row = 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 or save clinical data.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# According to the provided indices (e.g., '16650001', '16650003', etc.), they do not look like standard human gene symbols.
+# They appear to be probe/array IDs that would 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 6: Gene Identifier Mapping (Corrected)
+
+# Explanation:
+# We attempted to map probes to gene symbols using the annotation dataframe, but there is no overlap
+# between the row IDs in 'gene_data' and the 'ID' column in 'gene_annotation'. Consequently, the mapped
+# DataFrame ended up empty. Per the reviewer's suggestion, we will detect this mismatch and, if no
+# overlap is found, we will keep the original probe-level expression data in 'gene_data' (skipping the
+# apply_gene_mapping step). This retains the numeric probe IDs as final identifiers.
+
+common_ids = gene_data.index.intersection(gene_annotation['ID'].astype(str).unique())
+if len(common_ids) == 0:
+    # No overlap found; use probe IDs as final identifiers
+    print("No overlap found between 'gene_data' row IDs and gene_annotation 'ID'.")
+    print("Retaining probe-level data as the final gene expression DataFrame.")
+    # Rename the index from "ID" to "Gene" for consistency
+    gene_data.index.rename("Gene", inplace=True)
+else:
+    # If partial or full overlap is detected, proceed with actual mapping
+    mapping_df = get_gene_mapping(
+        annotation=gene_annotation,
+        prob_col='ID',
+        gene_col='GB_ACC'
+    )
+    gene_data = apply_gene_mapping(gene_data, mapping_df)
+    print("Applied gene mapping. Mapped gene expression data shape:", gene_data.shape)
+
+# Print a quick summary
+print("Final gene_data shape:", gene_data.shape)
+print("Final gene_data index name:", gene_data.index.name)
+print("First 5 rows:")
+print(gene_data.head(5))
+# STEP5
+
+# 1) Normalize the gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Because we found no trait data (trait_row=None), there is no clinical dataframe to link.
+#    We skip linking clinical data and related steps.
+
+# 3) We also skip handling missing trait/covariate values, since no trait is present.
+
+# 4) We skip bias checking on the trait for the same reason (no trait).
+
+# 5) Perform final quality validation and save metadata.
+#    Although we have gene data, the trait is unavailable, so this dataset is not usable for trait-gene association.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,   # No trait data
+    is_biased=False,            # Arbitrarily False; lack of trait data makes is_usable=False anyway
+    df=normalized_gene_data,
+    note="No trait data available for cohort GSE200904; gene data alone cannot support association analysis."
+)
+
+# 6) Since the dataset is not usable (no trait), we do not save a final linked data file.
+if is_usable:
+    # This block won't run because is_usable is False.
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Melanoma/code/GSE189631.py

@@ -0,0 +1,45 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE189631"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE189631"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE189631.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE189631.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE189631.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Attempt to identify the paths to the SOFT file and the matrix file
+try:
+    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+except AssertionError:
+    print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
+    soft_file, matrix_file = None, None
+
+if soft_file is None or matrix_file is None:
+    print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
+else:
+    # 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("\nSample Characteristics Dictionary:")
+    print(sample_characteristics_dict)

p1/preprocess/Melanoma/code/GSE200904.py

@@ -0,0 +1,106 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE200904"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE200904"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE200904.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE200904.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE200904.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset measures gene expression (73 endogenous genes).
+
+# 2. Variable Availability
+trait_row = None    # No column providing melanoma vs. other states in the sample characteristics.
+age_row = None      # No age-related information detected.
+gender_row = None   # No gender-related information detected.
+
+# 2.2 Data Type Conversion
+
+def convert_trait(value: str):
+    # Since trait data is unavailable, return None
+    return None
+
+def convert_age(value: str):
+    # Since age data is unavailable, return None
+    return None
+
+def convert_gender(value: str):
+    # Since gender data is unavailable, return None
+    return None
+
+# 3. Initial Filtering and Metadata Saving
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. 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])
+# Based on the gene identifiers observed, they are standard human gene symbols.
+# Therefore, gene mapping is not required.
+print("requires_gene_mapping = False")
+# STEP5
+
+# 1) Normalize the gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Because we found no trait data (trait_row=None), there is no clinical dataframe to link.
+#    We skip linking clinical data and related steps.
+
+# 3) We also skip handling missing trait/covariate values, since no trait is present.
+
+# 4) We skip bias checking on the trait for the same reason (no trait).
+
+# 5) Perform final quality validation and save metadata.
+#    Although we have gene data, the trait is unavailable, so this dataset is not usable for trait-gene association.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,   # No trait data
+    is_biased=False,            # Arbitrarily False; lack of trait data makes is_usable=False anyway
+    df=normalized_gene_data,
+    note="No trait data available for cohort GSE200904; gene data alone cannot support association analysis."
+)
+
+# 6) Since the dataset is not usable (no trait), we do not save a final linked data file.
+if is_usable:
+    # This block won't run because is_usable is False.
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Melanoma/code/GSE202806.py

@@ -0,0 +1,110 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE202806"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE202806"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE202806.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE202806.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE202806.csv"
+json_path = "./output/preprocess/1/Melanoma/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 "RNA analysis of 770 genes" description
+
+# 2) Variable Availability and Data Type Conversion
+#    From the sample characteristics:
+#    0: ['tissue: Melanoma'] -> single value, no variation -> treat as not available
+#    1: ['nf1 status: WT', 'nf1 status: MUT'] -> not the trait "Melanoma", nor age/gender
+trait_row = None    # Entire dataset is Melanoma; no variation for trait
+age_row = None      # No age data found
+gender_row = None   # No gender data found
+
+def convert_trait(value: str) -> Optional[int]:
+    # This function won't be used because trait_row is None;
+    # but we define it as required by the instructions.
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # This function won't be used because age_row is None;
+    # but we define it as required by the instructions.
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # This function won't be used because gender_row is None;
+    # but we define it as required by the instructions.
+    return None
+
+# 3) Save Metadata (initial filtering)
+#    Trait data is unavailable: is_trait_available=False
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False
+)
+
+# 4) Clinical Feature Extraction
+#    Skip 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("The listed identifiers match known human gene symbols.\nrequires_gene_mapping = False")
+# STEP5
+
+# 1) Normalize the gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Because we found no trait data (trait_row=None), there is no clinical dataframe to link.
+#    We skip linking clinical data and related steps.
+
+# 3) We also skip handling missing trait/covariate values, since no trait is present.
+
+# 4) We skip bias checking on the trait for the same reason (no trait).
+
+# 5) Perform final quality validation and save metadata.
+#    Although we have gene data, the trait is unavailable, so this dataset is not usable for trait-gene association.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,   # No trait data
+    is_biased=False,            # Arbitrarily False; lack of trait data makes is_usable=False anyway
+    df=normalized_gene_data,
+    note="No trait data available for cohort GSE202806; gene data alone cannot support association analysis."
+)
+
+# 6) Since the dataset is not usable (no trait), we do not save a final linked data file.
+if is_usable:
+    # This block won't run because is_usable is False.
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Melanoma/code/GSE215868.py

@@ -0,0 +1,119 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE215868"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE215868"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE215868.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE215868.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE215868.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Based on "Baseline gene expression profiling..." in the background
+
+# Step 2.1: Determine data availability
+# The entire dataset is advanced melanoma patients, so there's no variation for the "trait".
+# Hence, trait_row is None.
+trait_row = None
+
+# Age information is found at key 0
+age_row = 0
+
+# No information about gender is found
+gender_row = None
+
+# Step 2.2: Define conversion functions
+def convert_trait(x: str) -> int:
+    # Not used in this dataset since trait_row is None, but provided for completeness
+    # If it were used, parse the value after the colon and map to 0/1 or None for unknown
+    val = x.split(':')[-1].strip().lower()
+    # Example: if val == "melanoma": return 1
+    return None
+
+def convert_age(x: str) -> float:
+    # Parse out the integer after "age:"
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip()
+    if value.isdigit():
+        return float(value)
+    return None
+
+def convert_gender(x: str) -> int:
+    # Not used (gender_row is None), but provided for completeness
+    val = x.split(':')[-1].strip().lower()
+    if val in ["male", "m"]:
+        return 1
+    elif val in ["female", "f"]:
+        return 0
+    return None
+
+# Step 3: Save metadata with initial filtering
+# trait_row is None => trait not available
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Skip clinical feature extraction because trait_row is None
+# (No code for geo_select_clinical_features is needed here since trait data is not available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# After observing the provided gene identifiers (A2M, ABCF1, ACVR1C, etc.), they match known official human gene symbols.
+# Therefore, no additional gene symbol mapping is required.
+
+print("requires_gene_mapping = False")
+# STEP 5
+
+# 1. Normalize the obtained gene expression data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# The dataset lacks trait data (trait_row is None), so we cannot proceed with linking or final validation.
+# Instead, we perform an initial-style validation to record the dataset as not usable for association analysis.
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,    # Gene data is indeed available
+    is_trait_available=False,  # Trait is absent
+    note="Trait data is not available. Gene expression data alone is not enough for analysis."
+)
+
+# Since trait data is unavailable, we skip linking clinical data, missing value handling, or final validation.
+# No further action is needed, as the dataset is determined unusable for trait-based studies.

p1/preprocess/Melanoma/code/GSE244984.py

@@ -0,0 +1,164 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE244984"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE244984"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE244984.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE244984.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE244984.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on background ("Molecular patterns ..."), we assume there's gene expression data.
+
+# 2) Variable Availability and Data Type Conversion
+
+# After checking the sample characteristics dictionary,
+# we see only:
+#   0 => ["patient: Pat..."]
+#   1 => ["resistance: CTLA4res", "resistance: PD1res"]
+# There's no mention of "Melanoma" (the trait), age, or gender.
+# Also, the entire cohort is already known to be melanoma, so it's effectively a constant in this dataset.
+# Therefore, these variables are considered "not available" for analysis.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define the conversion functions. Though we have no data rows for them,
+# we must still define them to fulfill the instructions.
+
+def convert_trait(x: str):
+    """
+    Convert trait data to a desired format (if it existed).
+    Extract the substring after the colon, handle unknowns as None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Hypothetical conversion if available
+    # But in this case, there's no trait data row anyway
+    return None  # Because we don't have actual trait info
+
+def convert_age(x: str):
+    """
+    Convert age data to continuous numeric (if it existed).
+    Extract the substring after the colon, handle unknowns as None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    # We don't have an age row. Return None for demonstration.
+    return None
+
+def convert_gender(x: str):
+    """
+    Convert gender data to binary (0=female, 1=male) if it existed.
+    Extract the substring after the colon, handle unknowns as None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # We don't have a gender row. Return None for demonstration.
+    return None
+
+# 3) Save Metadata with initial filtering
+# Trait data availability is determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+# Record dataset usability info
+is_usable_initial = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Because trait_row is None, we skip feature extraction entirely.
+# (No additional steps are needed.)
+# 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 observed identifiers are recognized human gene symbols.
+print("requires_gene_mapping = False")
+# STEP 5
+
+# 1) Normalize the obtained gene data using the NCBI Gene synonym database
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Check whether trait data is available (set in a previous step).
+#    If trait data is not available, we skip linking and subsequent steps
+#    because there's no clinical data to merge with gene data for association analysis.
+if not is_trait_available:
+    # Provide a dummy DataFrame and a placeholder boolean (e.g., False for is_biased)
+    # to satisfy the function signature for final validation.
+    import pandas as pd
+    dummy_df = pd.DataFrame()
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=dummy_df,
+        note="Trait not available, skipping linking step."
+    )
+else:
+    # If the trait is available, link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3) Handle missing values using trait as the key
+    linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4) Assess bias and remove biased demographic features
+    trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+    # 5) Final validation and metadata saving
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data_final,
+        note="Dataset processed with GEO pipeline. Checked for missing values and bias."
+    )
+
+    # 6) Save final linked data if usable
+    if is_usable:
+        linked_data_final.to_csv(out_data_file)

p1/preprocess/Melanoma/code/GSE261347.py

@@ -0,0 +1,114 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+cohort = "GSE261347"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Melanoma"
+in_cohort_dir = "../DATA/GEO/Melanoma/GSE261347"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/GSE261347.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/GSE261347.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/GSE261347.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine gene expression availability
+is_gene_available = True  # The dataset contains transcriptomic profiling data.
+
+# 2. Identify data availability and define conversion functions
+#    No trait/age/gender keys found; set their row to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data type conversion functions.
+# Even though data is not available, we still provide these for code completeness.
+
+def convert_trait(value: str):
+    # Example parse: get substring after colon
+    val = value.split(':')[-1].strip().lower()
+    # If data were available, map recognized trait values to 1, else None
+    if "melanoma" in val:
+        return 1
+    return None
+
+def convert_age(value: str):
+    # Example parse: get substring after colon
+    val = value.split(':')[-1].strip().lower()
+    # Convert to float if possible, else None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Example parse: get substring after colon
+    val = value.split(':')[-1].strip().lower()
+    if val in ["male", "m"]:
+        return 1
+    elif val in ["female", "f"]:
+        return 0
+    return None
+
+# 3. Perform initial filtering and save metadata
+#    Trait availability is false because trait_row is None.
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction
+#    Skip because trait_row is None (trait not available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# After reviewing the gene identifiers, they appear to be standard human gene symbols.
+print("requires_gene_mapping = False")
+# STEP5
+# Since trait data is not available, we cannot perform a final validation (is_final=True) because
+# 'df' and 'is_biased' are required. Instead, we will perform a partial validation (is_final=False)
+# to record that the dataset lacks trait data.
+
+# 1. Normalize the obtained gene data using the NCBI Gene synonym database.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Perform partial validation and save metadata that trait data is not available.
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,      # We do have transcriptomic data
+    is_trait_available=False     # Trait data remains unavailable
+)
+
+# 3. Since trait data is not available, we cannot link or bias-check, so we stop here.

p1/preprocess/Melanoma/code/TCGA.py

@@ -0,0 +1,116 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Melanoma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Melanoma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Melanoma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Melanoma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Melanoma/cohort_info.json"
+
+import os
+import pandas as pd
+
+# Step 1: Search directories in tcga_root_dir for "most specific" matches to "Melanoma".
+# We define a simple priority-based approach: "skcm" is highest priority, then "melanoma", then "skin".
+search_terms_priority = {
+    "skcm": 0,
+    "melanoma": 1,
+    "skin": 2
+}
+
+dir_list = os.listdir(tcga_root_dir)
+best_dir = None
+best_rank = None
+
+for d in dir_list:
+    d_lower = d.lower()
+    # Determine if this directory name contains any search term
+    matched_terms = [term for term in search_terms_priority if term in d_lower]
+    if matched_terms:
+        # Pick the highest priority (lowest numerical value)
+        rank = min(search_terms_priority[t] for t in matched_terms)
+        if best_rank is None or rank < best_rank:
+            best_rank = rank
+            best_dir = d
+
+if best_dir is None:
+    # No matching directory found. Mark the dataset as skipped.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # 2. Identify the clinicalMatrix and PANCAN files
+    cohort_dir = os.path.join(tcga_root_dir, best_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load both data files
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print the column names of the clinical data
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+extracted_columns = candidate_age_cols + candidate_gender_cols
+extracted_data = clinical_df[extracted_columns]
+print("candidate_age_cols =", candidate_age_cols)
+print("candidate_gender_cols =", candidate_gender_cols)
+print(preview_df(extracted_data, n=5))
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print("The selected age_col is:", age_col)
+print("The selected gender_col is:", gender_col)
+# 1) Extract and standardize clinical features (trait, age, gender) from the TCGA data
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols in the gene expression data
+genetic_df_normalized = normalize_gene_symbols_in_index(genetic_df)
+genetic_df_normalized.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+gene_expr_t = genetic_df_normalized.T
+linked_data = selected_clinical_df.join(gene_expr_t, how='inner')
+
+# 4) Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Determine whether the trait and some demographic features are severely biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6) Validate and save cohort information
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Prostate Cancer data from TCGA."
+)
+
+# 7) If usable, save the final linked data, including clinical and genetic features
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    # Save clinical subset if present
+    clinical_cols = [col for col in [trait, "Age", "Gender"] if col in linked_data.columns]
+    if clinical_cols:
+        linked_data[clinical_cols].to_csv(out_clinical_data_file)

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p1/preprocess/Mesothelioma/clinical_data/GSE68950.csv

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p1/preprocess/Mesothelioma/code/GSE107754.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE107754"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE107754"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE107754.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE107754.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE107754.csv"
+json_path = "./output/preprocess/1/Mesothelioma/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
+#    According to the series summary, this dataset uses whole human genome microarrays, so it likely contains gene expression data.
+is_gene_available = True
+
+# 2) Variable Availability
+#    From the sample characteristics dictionary:
+#    - trait (Mesothelioma) can be identified in row 2 via "tissue: Malignant Mesothelioma" among multiple tissue types.
+#    - age data is not provided in any row.
+#    - gender data is provided in row 0 ("gender: Male"/"gender: Female").
+trait_row = 2
+age_row = None
+gender_row = 0
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(x: str):
+    """
+    Convert the tissue type into a binary indicator for Mesothelioma vs non-Mesothelioma.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip().lower()
+    return 1 if value == 'malignant mesothelioma' else 0
+
+def convert_age(x: str):
+    """
+    Age is not actually available (age_row=None), but define a placeholder if needed.
+    """
+    return None
+
+def convert_gender(x: str):
+    """
+    Convert gender strings to binary: Female -> 0, Male -> 1.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        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
+#    Only proceed if trait_row is not None.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    print(preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on common GEO microarray conventions, these IDs (e.g., A_23_P100001) appear to be probe identifiers rather than human gene symbols.
+# Therefore, 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 6: Gene Identifier Mapping
+# 1) Identify the matching columns in the annotation DataFrame:
+#    - 'ID' holds the same probe identifiers as gene_data.index
+#    - 'GENE_SYMBOL' stores the gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2) Convert probe-level measurements to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional preview of the resulting gene_data
+print("Mapped gene expression data preview:")
+print(preview_df(gene_data))
+
+# Save the mapped gene data
+gene_data.to_csv(out_gene_data_file, index=True)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE112154.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE112154"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE112154"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE112154.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE112154.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE112154.csv"
+json_path = "./output/preprocess/1/Mesothelioma/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 if gene expression data is available
+is_gene_available = True  # Based on the series summary stating "Gene expression profiling..."
+
+# 2) Identify availability and rows for trait, age, gender
+#    From the sample characteristics dictionary, we only have key 0 with multiple values:
+#       ['sample type: normal peritoneum', 'sample type: DMPM cell line', 'sample type: DMPM frozen tumor specimen']
+#    We will use this key to derive the disease trait, which can be mapped to binary Mesothelioma vs. normal.
+trait_row = 0
+age_row = None    # No age info
+gender_row = None # No gender info
+
+# 2) Data type conversion functions
+def convert_trait(value: str):
+    # Extract substring after the colon
+    if ':' in value:
+        value = value.split(':', 1)[1].strip().lower()
+    else:
+        value = value.strip().lower()
+    # Binary encoding: normal -> 0, DMPM (mesothelioma) -> 1
+    if 'normal' in value:
+        return 0
+    elif 'dmpm' in value:
+        return 1
+    return None
+
+def convert_age(value: str):
+    # No data, just return None
+    return None
+
+def convert_gender(value: str):
+    # No data, just return None
+    return None
+
+# 3) Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Extract clinical features if trait_row is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted clinical features
+    preview_dict = preview_df(selected_clinical_df, n=5, max_items=200)
+    print("Preview of selected clinical features:", preview_dict)
+
+    # Save clinical data to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., ILMN_1343291), they are Illumina probe IDs, not standard human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Identify columns in gene_annotation that match probe IDs in gene_data and gene symbols:
+#    From the annotation preview, "ID" corresponds to probe IDs like "ILMN_...", and "Symbol" corresponds to gene symbols.
+prob_col = "ID"
+gene_col = "Symbol"
+
+# 2) Get a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3) Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# (gene_data is now the mapped gene expression dataframe.)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE117668.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE117668"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE117668"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE117668.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE117668.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE117668.csv"
+json_path = "./output/preprocess/1/Mesothelioma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset likely contains gene expression data
+is_gene_available = True  # Based on the description, it is microarray gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics:
+#   Row 1 => "diagnosis: healthy" or "diagnosis: malignant pleural mesothelioma"
+# That can serve as the trait variable with two unique values.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Converters
+def convert_trait(value: str):
+    """
+    Convert the trait data after the colon into a binary format:
+    - healthy -> 0
+    - malignant pleural mesothelioma -> 1
+    - otherwise -> None
+    """
+    parts = value.split(':')
+    val = parts[-1].strip() if len(parts) > 1 else value.strip()
+    val_lower = val.lower()
+    if 'healthy' in val_lower:
+        return 0
+    elif 'mesothelioma' in val_lower:
+        return 1
+    else:
+        return None
+
+# Since age and gender are not available, we'll set them to None
+convert_age = None
+convert_gender = None
+
+# 3. Initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction if trait is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the resulting dataframe
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:\n", preview_result)
+
+    # Save the clinical features to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# 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., '100009613_at', '100009676_at') are Affymetrix probe IDs, not human gene symbols.
+# Therefore, we need to map them 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 & 2. Decide which columns correspond to the probe IDs and the gene symbols for mapping
+#    Based on the preview, "ID" holds the probe identifier (matches gene_data.index),
+#    and "Description" contains text describing the gene (to extract gene symbols).
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Description")
+
+# 3. Apply the mapping to convert probe-level measurements into gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE131027.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE131027"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE131027"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE131027.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE131027.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE131027.csv"
+json_path = "./output/preprocess/1/Mesothelioma/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 "investigation of expression features" context
+
+# 2. Variable Availability and Data Type Conversion
+#    We see "Mesothelioma" among multiple cancer types in key=1, so:
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Define converters. For rows that are None, we will not use the converter.
+def convert_trait(raw_value: str):
+    parts = raw_value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Convert Mesothelioma to 1, others to 0
+    return 1 if val == "mesothelioma" else 0
+
+convert_age = None
+convert_gender = None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_data = 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_info = preview_df(selected_clinical_data)
+    print("Clinical data preview:", preview_info)
+    selected_clinical_data.to_csv(out_clinical_data_file)
+# 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 appear to be Affymetrix probe set IDs, not standard human gene symbols.
+# Hence, gene symbol mapping will be required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6 - Gene Identifier Mapping
+
+# 1. The key in 'gene_annotation' that corresponds to the gene expression data's identifiers is "ID".
+#    The key that holds the gene symbols is "Gene Symbol".
+
+# 2. Create a gene mapping dataframe by extracting those two 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)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE163720.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE163720"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE163720"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE163720.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE163720.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE163720.csv"
+json_path = "./output/preprocess/1/Mesothelioma/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 background info mentioning microarray gene expression analysis
+
+# 2) Determine data availability and define row indices
+#    Check the sample characteristics dictionary:
+#    {0: ['organ: Tumor'], 1: ['compartment: Tissue'], 2: ['Sex: F', 'Sex: M']}
+#    There is no row for the trait (Mesothelioma) or age information, but there is one for 'Sex'.
+trait_row = None     # No row indicates "Mesothelioma"
+age_row = None       # No age info found
+gender_row = 2       # 'Sex: F' or 'Sex: M'
+
+# 2) Data Type Conversion Functions
+def convert_trait(value: str):
+    # No trait data is available
+    return None
+
+def convert_age(value: str):
+    # No age data is available
+    return None
+
+def convert_gender(value: str):
+    # Parse "Sex: F" or "Sex: M" -> F -> 0, M -> 1
+    val = value.split(':')[-1].strip().lower()
+    if val == 'f':
+        return 0
+    elif val == 'm':
+        return 1
+    return None
+
+# 3) Save Metadata (Initial filtering)
+is_trait_available = (trait_row is not None)  # If we don't have a trait row, then no trait data
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+#    (No action needed as trait is not available)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers appear to be numeric probe IDs rather than standard human gene symbols.
+# Therefore, 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Decide which columns store gene IDs and gene symbols.
+#    From the preview, "ID" in the annotation matches the numeric probe IDs.
+#    The "gene_assignment" column contains text referencing gene symbols.
+
+prob_col = "ID"
+gene_col = "gene_assignment"
+
+# 2. Get a gene mapping dataframe from the annotation df.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, print the shape of the resulting dataframe and the first few gene symbols in the index.
+print("Gene data shape:", gene_data.shape)
+print("First 10 gene symbols in the mapped dataframe index:", list(gene_data.index[:10]))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE163721.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE163721"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE163721"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE163721.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE163721.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE163721.csv"
+json_path = "./output/preprocess/1/Mesothelioma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene expression data availability
+is_gene_available = True  # Based on the background info, this is a microarray dataset for gene expression.
+
+# 2. Variable Availability
+# The sample characteristics contain only one key (0) with a single unique value: "tissue type: Tumor".
+# This is constant and does not provide useful variation for trait, age, or gender. Thus, they are not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+# Even though these variables are not available, we define the required converter functions as placeholders.
+def convert_trait(x: str):
+    return None
+
+def convert_age(x: str):
+    return None
+
+def convert_gender(x: str):
+    return None
+
+# 3. Save Metadata (initial filtering)
+# We treat the dataset as if trait data is unavailable because 'trait_row' is None (no variation).
+# This will mark the dataset as not usable at this stage but still record the metadata.
+is_trait_available = 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, 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 given index ['1','2','3', ...], these appear to be numeric IDs rather than standard human gene symbols
+# Therefore, they likely need to be mapped to actual 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 correspond to the gene expression IDs and the gene symbols.
+#    From the preview, 'ID' matches the probe IDs (1,2,3...) in our gene data, and 'Gene Symbol' contains the gene symbols.
+
+# 2. Get the gene mapping dataframe.
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE163722.py

@@ -0,0 +1,86 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE163722"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE163722"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE163722.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE163722.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE163722.csv"
+json_path = "./output/preprocess/1/Mesothelioma/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
+#    The study is titled "Association of RERG Expression to Female Survival Advantage..."
+#    This suggests that gene-level expression was measured. Therefore:
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+#    From the sample characteristics dictionary {0: ['tissue type: Tumor']},
+#    we see no row providing diverse values for 'Mesothelioma' status, age, or gender.
+#    Thus, all three are unavailable (constant or missing).
+
+trait_row = None
+age_row = None
+gender_row = None
+
+#    Define the conversion functions. Although no data is available, we still write them.
+
+def convert_trait(value: str):
+    """
+    Convert the trait value to the chosen data type. 
+    Since 'trait' is unavailable in this dataset, we will simply return None.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Convert the age value to the chosen data type (normally continuous).
+    Since 'age' is unavailable, we return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert the gender value to the chosen data type (binary).
+    Since 'gender' is unavailable, we return None.
+    """
+    return None
+
+# 3. Save Metadata (Initial filtering)
+#    If 'trait_row' is None, set is_trait_available=False
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since 'trait_row' is None, skip extracting clinical features.

p1/preprocess/Mesothelioma/code/GSE248514.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE248514"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE248514"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE248514.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE248514.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE248514.csv"
+json_path = "./output/preprocess/1/Mesothelioma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background information that mRNA gene expression profiling was performed,
+# we set is_gene_available to True.
+is_gene_available = True
+
+# --------------------------------------------------------------------------------
+# 2. Variable Availability
+
+# For our trait "Mesothelioma", the dictionary solely shows "tissue: mesothelioma" at row 2,
+# which has only one unique value (no variability). Hence, treat it as not available (None).
+trait_row = None
+
+# No row in the dictionary indicates age. Hence, age is not available.
+age_row = None
+
+# Row 3 has two distinct values: "gender: Male" and "gender: Female". So gender is available.
+gender_row = 3
+
+# --------------------------------------------------------------------------------
+# 2.2 Data Type Conversion
+
+def convert_trait(x: str):
+    """
+    Convert trait values to a chosen data type.
+    Since trait is not available, we keep a placeholder that always returns None.
+    """
+    return None
+
+def convert_age(x: str):
+    """
+    Convert age values to continuous type if available.
+    Since age is not available, this is a placeholder returning None.
+    """
+    return None
+
+def convert_gender(x: str):
+    """
+    Convert gender values into binary values:
+      - Female -> 0
+      - Male -> 1
+    Any unmapped or unknown value -> None
+    """
+    val = x.split(":", 1)[-1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    else:
+        return None
+
+# --------------------------------------------------------------------------------
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# --------------------------------------------------------------------------------
+# 4. Clinical Feature Extraction
+# Skip this step because trait_row is None (trait data is not available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# After reviewing the provided gene identifiers (A2M, ABCF1, ACVR1C, etc.),
+# they appear to be standard human gene symbols that do not require mapping.
+print("\nrequires_gene_mapping = False")
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE64738.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE64738"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE64738"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE64738.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE64738.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE64738.csv"
+json_path = "./output/preprocess/1/Mesothelioma/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 microarray description, it likely contains gene expression data.
+
+# 2. Variable Availability
+#    From the sample characteristics, all samples are mesothelioma (no variation in disease status).
+#    There's no mention of age or gender. Therefore, set all rows to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+#    Define the conversion functions (they won't be used here, but we must still define them).
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Save Metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip the clinical extraction step.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the given identifiers (e.g., "1007_s_at", "1053_at", etc.),
+# these appear to be Affymetrix probe set IDs, not standard human gene symbols.
+# Therefore, we conclude that gene mapping to official gene symbols is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns to use for probe IDs and gene symbols
+#    From the annotation preview, "ID" matches the probe identifiers
+#    and "Gene Symbol" contains the gene symbols.
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Get a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/GSE68950.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+cohort = "GSE68950"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mesothelioma"
+in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE68950"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/GSE68950.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/GSE68950.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/GSE68950.csv"
+json_path = "./output/preprocess/1/Mesothelioma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # "Assay Type: Gene Expression" is indicated in the background
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# After checking row #1's values, we see none of the samples actually mention "mesothelioma".
+# So the trait would be constant (all 0). Thus we treat it as unavailable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+def convert_trait(x: str) -> Optional[int]:
+    # No usable trait data. Always return None.
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    # No age data, return None.
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    # No gender data, return None.
+    return None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip this step.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+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 & 2. Determine the correct columns in gene_annotation and extract the mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Mesothelioma/code/TCGA.py

@@ -0,0 +1,132 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mesothelioma"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Mesothelioma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Mesothelioma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Mesothelioma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Mesothelioma/cohort_info.json"
+
+import os
+import pandas as pd
+
+# List of subdirectories provided in the instructions:
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Synonyms or keywords related to Mesothelioma
+meso_synonyms = ["mesothelioma", "meso"]
+
+selected_subdirectory = None
+for subdir in subdirectories:
+    subdir_lower = subdir.lower()
+    if any(syn in subdir_lower for syn in meso_synonyms):
+        selected_subdirectory = subdir
+        break
+
+if not selected_subdirectory:
+    # If no matching directory is found, mark dataset as unavailable
+    is_final = False
+    is_gene_available = False
+    is_trait_available = False
+    _ = validate_and_save_cohort_info(
+        is_final=is_final,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+    print(f"No suitable directory found for '{trait}'. Skipped this trait.")
+else:
+    # Step 2: Identify clinicalMatrix file and PANCAN file
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as dataframes
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # Step 4: Print the column names of the clinical data
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))
+# Step 1: Identify candidate columns
+candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "asbestos_exposure_age", "asbestos_exposure_age_last", "days_to_birth"]
+candidate_gender_cols = ["gender"]
+
+# Print them in the required format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Step 2: Extract and preview data
+if candidate_age_cols:
+    age_df = clinical_df[candidate_age_cols]
+    print("Age columns preview:", preview_df(age_df, n=5))
+
+if candidate_gender_cols:
+    gender_df = clinical_df[candidate_gender_cols]
+    print("Gender columns preview:", preview_df(gender_df, n=5))
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print("Chosen age_col:", age_col)
+print("Chosen gender_col:", gender_col)
+# 1. Extract and standardize the clinical features
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2. Normalize gene symbols in the expression data and save
+gene_df = normalize_gene_symbols_in_index(genetic_df)
+gene_df.to_csv(out_gene_data_file)
+
+# 3. Link clinical and genetic data
+#    The genetic data has samples as columns, so transpose before joining
+linked_data = selected_clinical_df.join(gene_df.T, how='inner')
+
+# 4. Handle missing values
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine and remove biased features
+biased_trait, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 6. Final quality validation
+gene_cols = [col for col in processed_data.columns if col not in [trait, "Age", "Gender"]]
+is_gene_available = len(gene_cols) > 0
+is_trait_available = trait in processed_data.columns
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=biased_trait,
+    df=processed_data,
+    note="Final data processing for Kidney_Chromophobe"
+)
+
+# 7. Save linked data if usable
+if is_usable:
+    processed_data.to_csv(out_data_file)