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p1/preprocess/Breast_Cancer/clinical_data/TCGA.csv

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p1/preprocess/Breast_Cancer/code/GSE153316.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE153316"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE153316"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE153316.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE153316.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE153316.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # "Gene expression profiles" suggests it is indeed gene expression data.
+
+# 2) Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics, the 'trait' variable ("Breast_Cancer") is constant for all samples
+# (they're all mastectomy patients); hence it's not useful for association (only one unique value).
+trait_row = None
+
+# For age, row 2 has multiple distinct values like "age: 39", "age: 36", etc.
+age_row = 2
+
+# For gender, there is no relevant row in the dictionary.
+gender_row = None
+
+# 2.2 Define the data conversion functions.
+# Even if the variable is not used (trait_row = None, gender_row = None), we still define them per instructions.
+
+def convert_trait(x: str):
+    # The trait "Breast_Cancer" is constant, so we skip detailed parsing.
+    # Return None to indicate no meaningful variation.
+    return None
+
+def convert_age(x: str):
+    # Example format: "age: 39"
+    # Extract the part after the colon and convert to float if possible.
+    try:
+        val_str = x.split(":", 1)[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(x: str):
+    # No actual data available, but define a stub for completeness.
+    # Return None always in this dataset.
+    return None
+
+# 3) Save Metadata using initial filtering
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction: Skip because trait_row is None (no trait data available)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on visual inspection, these 'AFFX' prefixes are typically Affymetrix probe/control IDs rather than standard human gene symbols.
+# Therefore, they require mapping to standard gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Identify the columns for probe IDs and gene symbols based on the annotation preview.
+#    From inspection, "ID" in the annotation matches the probe ID in the expression data,
+#    and "SPOT_ID.1" contains the textual gene symbol information.
+
+# 2) Build the mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1')
+
+# 3) Convert the probe-level data to gene-level data using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print a small preview to confirm structure.
+print("Gene expression data (mapped) preview:")
+print(preview_df(gene_data))
+# STEP7
+
+# 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)
+
+# Since trait_row was None, there is no usable trait data.
+# Hence, it's not possible to perform final linking or bias checking for association studies.
+
+# 2. We record dataset metadata indicating that it lacks trait data (so it's not usable).
+validate_and_save_cohort_info(
+    is_final=False,  # We only do the initial validation because trait is unavailable
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False
+)
+
+# 3. Because there's no trait data, we skip linking, bias checking, and final saving.

p1/preprocess/Breast_Cancer/code/GSE207847.py

@@ -0,0 +1,176 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE207847"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE207847"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE207847.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE207847.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE207847.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset contains suitable gene expression data
+#    (based on the background: "We performed gene expression profile...")
+is_gene_available = True
+
+# 2. Variable Availability (trait, age, gender) and Data Type Conversions
+#    From the sample characteristics dictionary, we observe:
+#    - Row 0 has "disease state: luminal breast cancer" (only 1 unique value)
+#    - Row 1 has "gender: female" (only 1 unique value)
+#    - Row 2 has "tissue: primary luminal breast cancer" (only 1 unique value)
+#    - Row 3 has "loco-regional recurrence: LATE/EARLY/INTERMEDIATE" (not our target trait)
+#    => All data relevant to "Breast_Cancer" or "age" or "gender" shows no variation or is not provided.
+#    Hence, we conclude all three variables are unavailable for this dataset.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define the data type conversion functions (though they won't be used here since all rows are None)
+
+def convert_trait(value: str):
+    """
+    Convert trait data to binary or continuous.
+    Since 'trait_row' is None, this function will not be used.
+    In other contexts, we'd parse the value after the colon and map
+    known variants to desired data type. Here, return None as placeholder.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Convert age data to a continuous variable.
+    Since 'age_row' is None, this function will not be used.
+    In other contexts, we'd parse the value after the colon,
+    convert to float, handle unknown as None, etc.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert gender data to binary (female=0, male=1).
+    Since 'gender_row' is None, this function will not be used.
+    """
+    return None
+
+# 3. Conduct initial filtering and save metadata
+#    Trait data availability depends on whether trait_row is None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction - Skip because trait_row is None
+if is_trait_available:
+    # Would extract and save clinical features if available.
+    pass
+# 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. Identify the columns in the gene annotation dataframe
+#    - 'ID' matches the probe identifiers in the gene expression data
+#    - 'gene_assignment' contains the gene symbols (within the text)
+probe_col = 'ID'
+gene_symbol_col = 'gene_assignment'
+
+# 2. Get a gene mapping dataframe from the annotation
+mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)
+
+# 3. Convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For validation, print the shape of the mapped gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+import pandas as pd
+
+# STEP7
+
+# 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. Since no clinical data was extracted (trait_row=None in previous steps),
+#    define a placeholder clinical DataFrame so the code won't fail.
+selected_clinical_df = pd.DataFrame()
+
+# Attempt to link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# If the trait column isn't present after linking, it implies no trait data is available.
+if trait not in linked_data.columns:
+    # Provide a dummy DataFrame and set is_biased=False so is_final=True doesn't error out
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,      # Dummy value to satisfy function requirements
+        df=empty_df,          # Dummy DataFrame
+        note="No trait data available for this dataset."
+    )
+else:
+    # 3. Handle missing values systematically
+    linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+    # 4. Check for biased trait and remove any biased demographic features
+    trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+    # 5. Final quality 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. If dataset is usable, save the final linked data
+    if is_usable:
+        linked_data_final.to_csv(out_data_file)

p1/preprocess/Breast_Cancer/code/GSE208101.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE208101"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE208101"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE208101.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE208101.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE208101.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+# From the background info, it is clear this dataset uses a gene expression profiling platform (Clariom D),
+# so we consider gene expression data to be available.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+# Based on the sample characteristics dictionary, all samples have "gender: female" (only one unique value),
+# "tissue: primary luminal breast cancer" (only one unique value), "disease state: luminal breast cancer" (one unique value),
+# and "loco-regional recurrence" with three categories (EARLY, INTERMEDIATE, LATE), which does not reflect the trait
+# "Breast_Cancer" vs. "Non-Cancer", but rather time-to-recurrence categories. Therefore, no key actually
+# provides a varying "Breast_Cancer" trait, and there is also no key for age. Thus, each variable is effectively unavailable.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data conversion functions (they won't be used since all variables are unavailable),
+# but we provide them as placeholders to follow instructions.
+
+def convert_trait(val: str) -> int:
+    # Placeholder: Not used, but implemented for completeness.
+    # Suppose we parse after the colon, but since data is unavailable, return None.
+    return None
+
+def convert_age(val: str) -> float:
+    # Placeholder: Not used, but implemented for completeness.
+    return None
+
+def convert_gender(val: str) -> int:
+    # Placeholder: Not used, but implemented for completeness.
+    return None
+
+# 3) Save metadata using initial filtering
+# Trait data availability depends on whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# We only proceed if trait_row is not None. Otherwise, skip.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait="Breast_Cancer",
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of clinical data:", preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "TC0100006432.hg.1") are not standard HUGO gene symbols.
+# Therefore, gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1) Decide which columns match the gene expression data ID and the gene symbol.
+#    From our inspection, the "ID" column contains the probe IDs matching the gene_data index.
+#    The "gene_assignment" column contains text from which we can extract gene symbols.
+
+# 2) Get a gene mapping dataframe using the library's get_gene_mapping function.
+gene_mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',
+    gene_col='gene_assignment'
+)
+
+# 3) Apply the mapping to convert the probe-level expression data into gene-level data.
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+
+# To observe some information about the resulting gene_data, let's print its shape and a quick head.
+print("Mapped gene_data shape:", gene_data.shape)
+print("Head of the mapped gene_data:")
+print(gene_data.head())
+# STEP7
+
+import pandas as pd
+
+# 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)
+
+# Since we determined earlier that trait data is not available (trait_row = None),
+# "selected_clinical_df" was never created. We therefore have no clinical data to link,
+# and the dataset is not usable for trait analysis. We'll handle final validation accordingly.
+
+# 2-4. Skip linking, missing-value handling, and bias checking because trait data is unavailable
+# Prepare a minimal placeholder DataFrame for final validation.
+placeholder_df = pd.DataFrame()
+
+# 5. Conduct final quality validation and save relevant information about the linked cohort data
+# Since trait data is unavailable, is_trait_available=False, the dataset won't be usable.
+# However, validate_and_save_cohort_info requires a boolean for is_biased when is_final=True.
+# We'll set is_biased=True (forcing the dataset to be considered not usable).
+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=True,
+    df=placeholder_df,
+    note="Trait data not available; cannot link clinical and genetic data."
+)
+
+# 6. If the dataset is usable (which, given no trait, it won't be), save the final linked data
+if is_usable:
+    # Normally we would save the linked data, but here it will remain unavailable.
+    pass

p1/preprocess/Breast_Cancer/code/GSE225328.py

@@ -0,0 +1,69 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE225328"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE225328"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE225328.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE225328.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE225328.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+#    From the background ("Transcriptome profiling"), we consider this dataset as containing gene expression data
+is_gene_available = True
+
+# 2. Identify the corresponding rows for each variable in the sample characteristics dictionary
+#    Here, both 'disease' and 'Sex' have only one unique value ("early-stage luminal breast cancer" and "female"),
+#    so they offer no variability for association studies. Hence, we consider them unavailable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data conversion functions.
+# Since our identified rows are None, we won't actually use these functions,
+# but we still define them as requested.
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3. Conduct initial filtering of usability
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Because trait_row is None, we skip clinical feature extraction and saving.

p1/preprocess/Breast_Cancer/code/GSE234017.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE234017"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE234017"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE234017.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE234017.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE234017.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Decide if gene expression data is likely available
+is_gene_available = True  # Spatial transcriptomics indicates gene expression data
+
+# 2. Identify variable availability
+# From the sample characteristics dictionary, row 2 ("genotype: WT/BRCA1/BRCA2") 
+# best reflects the trait "Breast_Cancer" in a binary manner (WT vs BRCA-mutated)
+trait_row = 2
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversions
+def convert_trait(value: str):
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    # WT => 0, BRCA1 => 1, BRCA2 => 1
+    if val == "WT":
+        return 0
+    elif val in ["BRCA1", "BRCA2"]:
+        return 1
+    return None
+
+def convert_age(value: str):
+    # No age data is provided
+    return None
+
+def convert_gender(value: str):
+    # No gender data is provided
+    return None
+
+# 3. Save metadata with initial filtering
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+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_result = preview_df(selected_clinical_df, n=5, max_items=200)
+    print("Preview of selected clinical features:", preview_result)
+    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, they do not appear to be standard human gene symbols.
+# Thus, they likely require mapping to official gene symbols.
+print("They appear to be some form of platform-based IDs.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene annotation that contain the same IDs as in 'gene_data' 
+#    and the column that contains the gene symbols ("ID" for probe identifiers, "ORF" for gene symbols).
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# 2. Convert probe-level measurements to gene expression data using the mapping dataframe.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Print the shape and a small preview of the resulting gene-level expression dataframe.
+print("Gene data shape after mapping:", gene_data.shape)
+print("Preview of mapped gene data:", preview_df(gene_data, n=5, max_items=20))
+# STEP7
+
+# 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. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality 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. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Breast_Cancer/code/GSE236725.py

@@ -0,0 +1,104 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE236725"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE236725"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE236725.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE236725.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE236725.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset likely contains gene expression data
+is_gene_available = True  # The study used Affymetrix microarrays, so it's gene expression data
+
+# 2. Check variable availability
+# The "disease state: breast cancer" field is constant (i.e., identical for all samples),
+# so it does not provide variability for association analysis. Age and gender are not present.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 3. Save metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. If trait_row were available, we would extract clinical features here, but it's None, so we skip.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "1007_s_at", "1053_at") are Affymetrix probe IDs, not standard gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns for probe IDs and gene symbols 
+#    ('ID' for probes, 'Gene Symbol' for gene symbols).
+
+# 2. Extract the mapping between probe IDs and gene symbols into a DataFrame.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene expression data using the mapping information.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7: Data Normalization and Partial Validation (No Trait Data)
+
+# 1. Normalize gene symbols in the obtained gene expression data using synonym information from the NCBI Gene database.
+#    Remove data of unrecognized gene symbols, and average the expression values of gene symbols that are mapped to the
+#    same standard symbol. Save the normalized data as a CSV file to out_gene_data_file.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since we do not have a trait (trait_row was None in previous steps), we cannot perform a final trait-based analysis.
+# Therefore, we record partial validation with is_final=False, so we do not need to provide df or is_biased.
+is_trait_available = False
+is_gene_available = True
+
+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
+)
+
+# There is no trait data to link or validate further, so we do not perform additional steps here.
+# is_usable is expected to be False, indicating we cannot proceed with final usage.

p1/preprocess/Breast_Cancer/code/GSE248830.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE248830"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE248830"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE248830.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE248830.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE248830.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine if the dataset likely contains gene expression data.
+#    From the background information, this dataset has "Targeted gene expression profiles ... using nCounter PanCancer IO 360™ Panel".
+#    Hence, set is_gene_available to True.
+is_gene_available = True
+
+# 2) Check availability of variables: trait, age, gender
+#    From the sample characteristics dictionary, we see:
+#    - Row 0: 'age at diagnosis: ...'
+#    - Row 1: 'Sex: female/male'
+#    - Row 2: 'histology: ...', which helps distinguish "adenocaricnoma" (lung) vs. "TNBC"/"ER"/"HER2"/"PR" (breast).
+trait_row = 2
+age_row = 0
+gender_row = 1
+
+# 2.2) Define data conversion functions.
+
+def convert_trait(x: str):
+    """
+    Convert histology to a binary indicator for 'Breast_Cancer': 
+      - 1 if the histology suggests breast cancer 
+      - 0 if it suggests lung adenocarcinoma
+      - None for unknown or unrecognized patterns
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'adenocaricnoma' in val:
+        return 0
+    if 'tnbc' in val or 'her2' in val or 'er' in val or 'pr' in val:
+        return 1
+    if 'unknown' in val:
+        return None
+    return None
+
+def convert_age(x: str):
+    """
+    Convert age at diagnosis to a continuous float. Return None if 'n.a.' or not a valid number.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'n.a.':
+        return None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(x: str):
+    """
+    Convert gender to a binary indicator: female -> 0, male -> 1, None otherwise.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'female':
+        return 0
+    if val == 'male':
+        return 1
+    return None
+
+# 3) Save metadata with initial filtering.
+#    Trait data is available if trait_row is not None.
+is_trait_available = (trait_row is not None)
+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) If trait data is available, extract and preview clinical features, then save to CSV.
+if is_trait_available:
+    selected_clinical_data = geo_select_clinical_features(
+        clinical_df=clinical_data,  # Assume 'clinical_data' is a DataFrame already loaded
+        trait=trait,                # "Breast_Cancer"
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview extracted clinical data
+    clinical_preview = preview_df(selected_clinical_data)
+    print("Clinical Data Preview:", clinical_preview)
+
+    # Save the clinical features to CSV
+    selected_clinical_data.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 provided gene identifiers (A2M, ACVR1C, ADAM12, ADGRE1, ADM, ADORA2A, AKT1, etc.),
+# they appear to be valid human gene symbols and do not require additional mapping.
+# Concluding answer:
+print("requires_gene_mapping = False")
+# STEP5
+
+# 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. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+# 3. Handle missing values systematically
+linked_data_processed = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Check for biased trait and remove any biased demographic features
+trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)
+
+# 5. Final quality 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. If dataset is usable, save the final linked data
+if is_usable:
+    linked_data_final.to_csv(out_data_file)

p1/preprocess/Breast_Cancer/code/GSE249377.py

@@ -0,0 +1,74 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE249377"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE249377"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE249377.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE249377.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE249377.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # From background info, this dataset provides transcriptomic (gene expression) data.
+
+# 2. Variable Availability and Data Type Conversion
+#    After reviewing the sample characteristics, none of the rows provide distinct "Breast_Cancer" statuses,
+#    nor do they provide "age" or "gender" information. The experiment uses only MCF7 (a breast cancer cell line),
+#    which does not vary among samples in a way that is useful for association studies.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions (they won't be used here, but we must still define them):
+def convert_trait(value: str) -> Optional[Union[float, int]]:
+    return None  # No trait row available, so always return None
+
+def convert_age(value: str) -> Optional[float]:
+    return None  # No age row available, so always return None
+
+def convert_gender(value: str) -> Optional[int]:
+    return None  # No gender row available, so always return None
+
+# 3. Save Metadata (initial filtering)
+#    If trait_row is None, is_trait_available should be 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=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we do not perform clinical feature extraction and 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])

p1/preprocess/Breast_Cancer/code/GSE270721.py

@@ -0,0 +1,131 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE270721"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE270721"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE270721.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE270721.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE270721.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # HTA 2.0 microarrays indicate gene expression data
+
+# 2. Variable Availability and Data Type Conversion
+#    Based on the sample characteristics, only 'age' has multiple non-constant values.
+trait_row = None   # Not found or constant (all are breast cancer patients)
+age_row = 2        # Key with age information
+gender_row = None  # No gender information found
+
+def convert_trait(value: str):
+    # No trait data row, so not applicable in this cohort
+    return None
+
+def convert_age(value: str):
+    # The format seems to be "age: 78.00" or "age: not available"
+    # Extract the substring after ':'
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()
+    if val_str == "not available":
+        return None
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # No gender data row, so not applicable
+    return None
+
+# 3. Save Metadata (initial filtering)
+#    Trait is considered unavailable since trait_row is None
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Skip if trait_row is None
+if trait_row is not None:
+    # We would perform clinical data extraction here, but trait_row is None in this case.
+    pass
+# 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 given identifiers (e.g., TC01000001.hg.1) are not recognizable standard human gene symbols.
+# They likely need mapping to official 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 relevant columns in gene_annotation for probe IDs and gene symbols.
+#    From the preview, "ID" matches the row IDs in our gene_data, and "gene_assignment" holds gene symbol info.
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")
+
+# 2. Convert probe-level measurements to gene-level by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP8
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Because trait data is not available (trait_row was None), we skip linking clinical data and trait-based analyses.
+
+# 2. Perform final validation and save cohort info.
+#    Per the library requirements, we must provide 'df' and 'is_biased' even though trait is unavailable.
+#    Setting 'is_biased=False' does not indicate the trait is balanced; rather, we are forced to supply a boolean.
+#    The function will mark the dataset as unusable because is_trait_available=False.
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    df=normalized_gene_data,
+    is_biased=False,
+    note="No trait or demographic data is available for association analysis."
+)
+
+# 3. Since the dataset is not usable for trait-based analysis, we do not save any final linked data.

p1/preprocess/Breast_Cancer/code/GSE283522.py

@@ -0,0 +1,145 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+cohort = "GSE283522"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Breast_Cancer"
+in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE283522"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/GSE283522.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/GSE283522.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/GSE283522.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import re
+
+# 1. Gene Expression Data Availability
+# Based on the background describing RNA-sequencing (mFISHseq), this dataset likely contains gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Conversions
+
+# 2.1 Identify rows in the Sample Characteristics Dictionary
+#    Trait: row 1 (contains "isolate: breast cancer patient", "isolate: healthy individual", etc.)
+trait_row = 1
+
+#    Age: row 2 (contains "age: 55 - 59", "age: 70 - 74", etc.)
+age_row = 2
+
+#    Gender: row 5 (contains "Sex: female", "Sex: male", etc.)
+gender_row = 5
+
+# 2.2 Define data type conversions
+def convert_trait(value: str):
+    """
+    Convert the value in row 1 into a binary indicator for breast cancer.
+    'isolate: breast cancer patient' -> 1
+    'isolate: healthy individual' -> 0
+    otherwise -> None
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    v = parts[1].strip().lower()
+    if 'breast cancer patient' in v:
+        return 1
+    elif 'healthy individual' in v:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    """
+    Convert the value in row 2 into a continuous numeric age.
+    Example: 'age: 55 - 59' -> 57 (midpoint), 'age: not applicable' -> None
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    range_str = parts[1].strip().lower()
+    if 'not applicable' in range_str:
+        return None
+    # Attempt to extract numeric values:
+    digits = re.findall(r'\d+', range_str)
+    if len(digits) == 2:
+        low, high = map(int, digits)
+        return (low + high) / 2
+    elif len(digits) == 1:
+        return int(digits[0])
+    else:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert the value in row 5 into a binary indicator for gender.
+    'Sex: female' -> 0
+    'Sex: male' -> 1
+    otherwise -> None
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    v = parts[1].strip().lower()
+    if v == 'female':
+        return 0
+    elif v == 'male':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata with initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Observe the extracted clinical dataframe
+    preview = preview_df(selected_clinical)
+    print("Preview of selected clinical features:", preview)
+
+    # Save clinical data to CSV
+    selected_clinical.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])

p1/preprocess/Breast_Cancer/code/TCGA.py

@@ -0,0 +1,100 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Breast_Cancer"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Breast_Cancer/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Breast_Cancer/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Breast_Cancer/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Breast_Cancer/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1) Identify the subdirectory for "Breast_Cancer"
+cohort_name = "TCGA_Breast_Cancer_(BRCA)"  # Found by matching "Breast_Cancer" with the list of directories
+cohort_dir = os.path.join(tcga_root_dir, cohort_name)
+
+# 2) Identify the paths to clinical and genetic files
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# 3) Load the files as Pandas 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')
+
+# 4) Print the column names of the clinical DataFrame
+print(clinical_df.columns.tolist())
+# 1) Identify the candidate columns
+candidate_age_cols = ['Age_at_Initial_Pathologic_Diagnosis_nature2012', 'age_at_initial_pathologic_diagnosis']
+candidate_gender_cols = ['Gender_nature2012', 'gender']
+
+# Print them in the specified format
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# 2) Extract and preview the candidate columns from the clinical data
+age_subset = clinical_df[candidate_age_cols] if candidate_age_cols else pd.DataFrame()
+gender_subset = clinical_df[candidate_gender_cols] if candidate_gender_cols else pd.DataFrame()
+
+if not age_subset.empty:
+    print("Age subset preview:")
+    print(preview_df(age_subset, n=5))
+
+if not gender_subset.empty:
+    print("Gender subset preview:")
+    print(preview_df(gender_subset, n=5))
+# Based on the previews, we see that the second candidate age column ('age_at_initial_pathologic_diagnosis')
+# contains valid age values, while the first only has NaN. Similarly, the second candidate gender column ('gender')
+# contains valid gender values, while the first only has NaN.
+
+age_col = "age_at_initial_pathologic_diagnosis"
+gender_col = "gender"
+
+print("Selected age_col:", age_col)
+print("Selected gender_col:", 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)

p1/preprocess/Breast_Cancer/gene_data/GSE153316.csv

@@ -0,0 +1,3 @@
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+oid sha256:b74b8b29c100349de2d065624634bb6cff9b7a004f27a4bdcf2f507d6c3022e2
+size 25366182

p1/preprocess/Breast_Cancer/gene_data/GSE207847.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed74109382222388995dbe48a728358ec835e919d70e3353f340bde074f2cead
+size 19341634

p1/preprocess/Breast_Cancer/gene_data/GSE208101.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b6bbfce7e5baad7fca87fdf54418256cab91f6804c4ea537ce5c03bffeb36a6e
+size 16473481

p1/preprocess/Breast_Cancer/gene_data/GSE234017.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:296c382d24e27fb1e1883f8a52119ede309dcf1157c4bf058d35abcfb81fb607
+size 19977227

p1/preprocess/Breast_Cancer/gene_data/GSE236725.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5488367b7a47796a22d68c801b9730aef40192d3d35f8092aa392c8f983de083
+size 16504806

p1/preprocess/Brugada_Syndrome/code/GSE136992.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Brugada_Syndrome"
+cohort = "GSE136992"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Brugada_Syndrome"
+in_cohort_dir = "../DATA/GEO/Brugada_Syndrome/GSE136992"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Brugada_Syndrome/GSE136992.csv"
+out_gene_data_file = "./output/preprocess/1/Brugada_Syndrome/gene_data/GSE136992.csv"
+out_clinical_data_file = "./output/preprocess/1/Brugada_Syndrome/clinical_data/GSE136992.csv"
+json_path = "./output/preprocess/1/Brugada_Syndrome/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  # This dataset uses mRNA expression arrays, so it's suitable.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify rows for trait, age, and gender
+#    We have no row for "Brugada_Syndrome", so trait is not available.
+trait_row = None
+
+#    Age is in row 2 with multiple unique values.
+age_row = 2
+
+#    Gender is in row 3 with multiple unique values.
+gender_row = 3
+
+# 2.2 Define data conversion functions
+def convert_trait(value: str):
+    # Since trait data ("Brugada_Syndrome") is not actually provided, return None
+    return None
+
+def convert_age(value: str):
+    # Typical format: "age: 24 weeks"
+    try:
+        # Split by ':', take the latter part (e.g. "24 weeks"), strip, then split by space
+        part = value.split(":", 1)[1].strip()  # e.g. "24 weeks"
+        num_str = part.split()[0]  # e.g. "24"
+        return float(num_str)      # Convert to float
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Typical format: "gender: male" or "gender: female"
+    try:
+        gender_str = value.split(":", 1)[1].strip().lower()  # e.g. "male" or "female"
+        if gender_str == "male":
+            return 1
+        elif gender_str == "female":
+            return 0
+        else:
+            return None
+    except:
+        return None
+
+# 3. Save Metadata: Perform initial filtering
+#    Trait data availability is determined by whether trait_row is None
+is_trait_available = (trait_row is not None)
+
+# Call the function from the library
+usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Because trait_row is None, we skip clinical data extraction for the trait.
+# 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("Based on the naming convention 'ILMN_#####', these appear to be Illumina probe IDs, not typical human gene symbols.\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns to use for the probe IDs (matching gene_data.index) and the gene symbols.
+#    From the annotation preview, "ID" holds the ILMN_##### identifiers, 
+#    and "Symbol" holds the gene symbols.
+prob_col = "ID"
+gene_col = "Symbol"
+
+# 2. Get the gene mapping between probe IDs and gene symbols.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene expression data using the many-to-many mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    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=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/Brugada_Syndrome/code/TCGA.py

@@ -0,0 +1,50 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Brugada_Syndrome"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Brugada_Syndrome/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Brugada_Syndrome/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Brugada_Syndrome/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Brugada_Syndrome/cohort_info.json"
+
+import os
+import pandas as pd
+
+# Step 1: Check directories in tcga_root_dir for anything relevant to "Brugada_Syndrome"
+search_terms = ["brugada", "syndrome"]
+dir_list = os.listdir(tcga_root_dir)
+matching_dir = None
+
+for d in dir_list:
+    d_lower = d.lower()
+    if any(term in d_lower for term in search_terms):
+        matching_dir = d
+        break
+
+if matching_dir is None:
+    # No matching directory found. Mark the dataset as skipped for Brugada_Syndrome.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA_Brugada_Syndrome",
+        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, matching_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())

p1/preprocess/Brugada_Syndrome/cohort_info.json

@@ -0,0 +1 @@
+{"GSE136992": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "No trait data found for GSE136992, final metadata recorded."}, "TCGA_Bone_Density": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA_Brugada_Syndrome": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p1/preprocess/Brugada_Syndrome/gene_data/GSE136992.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ed81b86668c87698a45486ae46c9cf45f0ddb2f1f27a6dc91aef2b9c912199a4
+size 15435596

p1/preprocess/Canavan_Disease/clinical_data/GSE41445.csv

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p1/preprocess/Canavan_Disease/code/GSE41445.py

@@ -0,0 +1,204 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Canavan_Disease"
+cohort = "GSE41445"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Canavan_Disease"
+in_cohort_dir = "../DATA/GEO/Canavan_Disease/GSE41445"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Canavan_Disease/GSE41445.csv"
+out_gene_data_file = "./output/preprocess/1/Canavan_Disease/gene_data/GSE41445.csv"
+out_clinical_data_file = "./output/preprocess/1/Canavan_Disease/clinical_data/GSE41445.csv"
+json_path = "./output/preprocess/1/Canavan_Disease/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)
+# Step 1: Determine if gene expression data is available
+is_gene_available = True  # Based on the series summary, it's a gene expression dataset
+
+# Step 2: Identify rows and define conversion functions
+
+# After examining the sample characteristics, "disease" is in row 2 and mentions "possible Canavan disease".
+# We'll treat that row as the trait data (binary: 1 if mentions "canavan", otherwise 0).
+trait_row = 2
+
+# There is no age information in the dictionary, so age_row is None
+age_row = None
+
+# Row 0 is gender data (male/female)
+gender_row = 0
+
+def convert_trait(value: str) -> Optional[int]:
+    # Extract the content after the colon
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()
+    # If "canavan" is mentioned, output 1, otherwise 0
+    return 1 if 'canavan' in val_str else 0
+
+# Since no age data is available, we won't define a convert_age function
+convert_age = None
+
+def convert_gender(value: str) -> Optional[int]:
+    # Extract the content after the colon
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()
+    if val_str == 'male':
+        return 1
+    elif val_str == 'female':
+        return 0
+    else:
+        return None
+
+# Step 3: Initial filtering and metadata saving
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: If trait data is available, extract clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,      # Assumes clinical_data is available in the environment
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview and save the clinical data
+    preview_result = preview_df(selected_clinical_df, n=5, max_items=200)
+    print("Preview of selected clinical data:", preview_result)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These gene identifiers (e.g., "1007_s_at") are Affymetrix probe IDs, not human gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Determine which columns in the annotation dataframe correspond to the probe IDs (matching gene_data.index)
+#    and which columns contain the gene symbols. From the preview, we see:
+#    - probe identifier column: "ID"
+#    - gene symbol column: "Gene Symbol"
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Get a gene mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)
+
+# 3. Convert probe-level expression data to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Display some basic info for verification
+print("Gene expression data shape:", gene_data.shape)
+print("Gene expression data index preview:", gene_data.index[:10])
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    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=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/Canavan_Disease/code/TCGA.py

@@ -0,0 +1,50 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Canavan_Disease"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Canavan_Disease/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Canavan_Disease/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Canavan_Disease/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Canavan_Disease/cohort_info.json"
+
+import os
+import pandas as pd
+
+# Step 1: Check directories in tcga_root_dir for anything relevant to "Canavan_Disease"
+search_terms = ["canavan", "canavan_disease"]
+dir_list = os.listdir(tcga_root_dir)
+matching_dir = None
+
+for d in dir_list:
+    d_lower = d.lower()
+    if any(term in d_lower for term in search_terms):
+        matching_dir = d
+        break
+
+if matching_dir is None:
+    # No matching directory found, so mark the dataset as skipped.
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA_Canavan_Disease",
+        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, matching_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())

p1/preprocess/Canavan_Disease/cohort_info.json

@@ -0,0 +1 @@
+{"GSE41445": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": true, "sample_size": 63, "note": "Final check on GSE41445 with Canavan_Disease."}, "TCGA_COVID-19": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA_Canavan_Disease": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p1/preprocess/Canavan_Disease/gene_data/GSE41445.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b1d74671fcace613f1f0519347569cd0283108e01c48d4979d5fa47d8f64c4a3
+size 12214185

p1/preprocess/Cardiovascular_Disease/code/GSE182600.py

@@ -0,0 +1,215 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cardiovascular_Disease"
+cohort = "GSE182600"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
+in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE182600"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE182600.csv"
+out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE182600.csv"
+out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE182600.csv"
+json_path = "./output/preprocess/1/Cardiovascular_Disease/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)
+# Step 1: Determine if the dataset likely contains gene expression data
+is_gene_available = True  # Based on the background info "genome-wide gene expression"
+
+# Step 2: Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary, all subjects have some form of cardiovascular disease.
+# Therefore, there is no variation for the trait "Cardiovascular_Disease." We mark it as not available.
+trait_row = None
+
+# Age is found at key=1 with multiple distinct values
+age_row = 1
+
+# Gender is found at key=2 with both F and M
+gender_row = 2
+
+# Data type conversions
+def convert_trait(value: str):
+    # Not used because trait_row = None
+    return None
+
+def convert_age(value: str):
+    # Example value: "age: 33.4"
+    # Parse the substring after the colon and convert to float
+    try:
+        val_str = value.split(":", 1)[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Example value: "gender: F"
+    # Parse and convert "F" to 0 and "M" to 1
+    try:
+        val_str = value.split(":", 1)[1].strip().upper()
+        if val_str.startswith("F"):
+            return 0
+        elif val_str.startswith("M"):
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# Step 3: Conduct initial filtering on dataset usability and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Since trait_row is None, we skip clinical feature extraction
+# STEP3
+# Attempt to read gene expression data; if the library function yields an empty DataFrame,
+# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
+# place actual expression rows under lines that begin with '!').
+
+gene_data = get_genetic_data(matrix_file)
+if gene_data.empty:
+    print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
+    import gzip
+
+    # Locate the marker line first
+    skip_rows = 0
+    with gzip.open(matrix_file, 'rt') as file:
+        for i, line in enumerate(file):
+            if "!series_matrix_table_begin" in line:
+                skip_rows = i + 1
+                break
+
+    # Read the data again, this time not treating '!' as comment
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression="gzip",
+        skiprows=skip_rows,
+        delimiter="\t",
+        on_bad_lines="skip"
+    )
+    gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
+    gene_data.set_index("ID", inplace=True)
+
+# Print the first 20 row IDs to confirm data structure
+print(gene_data.index[:20])
+# The gene identifiers shown (ILMN_XXXXXXX) appear to be Illumina probe IDs, not standard human gene symbols.
+print("They appear to be Illumina probe IDs (ILMN identifiers), not standard gene symbols.\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns from the annotation that correspond to the probe IDs vs. the actual gene symbols.
+#    From the preview, the 'ID' column in 'gene_annotation' matches the same ILMN_xxxx IDs in gene_data,
+#    and the 'Symbol' column provides the gene symbol for each probe.
+prob_col = "ID"
+gene_col = "Symbol"
+
+# 2. Get the gene mapping DataFrame by extracting the two relevant columns from the gene annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene expression values using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Verify the shape or preview the first few rows if needed
+print("Gene expression data after mapping:", gene_data.shape)
+print(gene_data.head(5))
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    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=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/Cardiovascular_Disease/code/GSE190042.py

@@ -0,0 +1,223 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cardiovascular_Disease"
+cohort = "GSE190042"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
+in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE190042"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE190042.csv"
+out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE190042.csv"
+out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE190042.csv"
+json_path = "./output/preprocess/1/Cardiovascular_Disease/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  # From the background info, gene expression (mRNA) data is present
+
+# 2) Variable Availability and Conversion
+#    The trait is "Cardiovascular_Disease", which is not present in the dictionary, so trait_row = None
+trait_row = None
+
+#    'age' is found in key=2 in the dictionary
+age_row = 2
+
+#    'gender' is found in key=1 in the dictionary
+gender_row = 1
+
+# 2.2 Define conversion functions.
+def convert_trait(value: str) -> int:
+    """
+    Convert trait (Cardiovascular_Disease) to 0/1 if data were present.
+    This dataset does not contain the trait, so this function is just a placeholder.
+    """
+    # No actual data to parse, return None or 0
+    return None  # Always returns None, because no trait info is available
+
+def convert_age(value: str) -> float:
+    """
+    Convert age from string format e.g., 'age: 56' to a float.
+    Unknown or invalid values return None.
+    """
+    # Split on colon and take the second part
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert gender from format e.g., 'gender: M' or 'gender: F' to 1 or 0.
+    M -> 1, F -> 0, unknown -> None.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    if val == 'M':
+        return 1
+    elif val == 'F':
+        return 0
+    else:
+        return None
+
+# 3) Save Metadata (initial filtering)
+#    trait is unavailable because trait_row is None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction: Skip because trait_row is None (trait data not available).
+#    Hence, no geo_select_clinical_features call here.
+# STEP3
+# Attempt to read gene expression data; if the library function yields an empty DataFrame,
+# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
+# place actual expression rows under lines that begin with '!').
+
+gene_data = get_genetic_data(matrix_file)
+if gene_data.empty:
+    print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
+    import gzip
+
+    # Locate the marker line first
+    skip_rows = 0
+    with gzip.open(matrix_file, 'rt') as file:
+        for i, line in enumerate(file):
+            if "!series_matrix_table_begin" in line:
+                skip_rows = i + 1
+                break
+
+    # Read the data again, this time not treating '!' as comment
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression="gzip",
+        skiprows=skip_rows,
+        delimiter="\t",
+        on_bad_lines="skip"
+    )
+    gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
+    gene_data.set_index("ID", inplace=True)
+
+# Print the first 20 row IDs to confirm data structure
+print(gene_data.index[:20])
+# Based on the given identifiers (e.g., "11715100_at", "11715101_s_at"), these are Affymetrix probe IDs 
+# rather than standard human gene symbols, so mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Identify the columns in 'gene_annotation' that correspond to the probe IDs in the gene expression data
+#    and the gene symbols. From the preview, they appear to be 'ID' (probe IDs) and 'Gene Symbol' (gene symbols).
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 2) Convert probe-level measurements to gene expression data using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, print the resulting dataframe dimensions and preview
+print("Mapped gene_data dimensions:", gene_data.shape)
+print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    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=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/Cardiovascular_Disease/code/GSE228783.py

@@ -0,0 +1,78 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cardiovascular_Disease"
+cohort = "GSE228783"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
+in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE228783"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE228783.csv"
+out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE228783.csv"
+out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE228783.csv"
+json_path = "./output/preprocess/1/Cardiovascular_Disease/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)
+# Step 1: Decide if the dataset likely contains gene expression data
+is_gene_available = True  # Based on the transcriptome context
+
+# Step 2: Determine variable availability
+trait_row = None      # No cardiovascular disease info in sample characteristics
+age_row = None        # No age info found
+gender_row = None     # No gender info found
+
+# Prepare conversion functions. Though not used when the rows are None, we must define them.
+def convert_trait(x: str) -> Optional[float]:
+    # Not used in this dataset
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    # Not used in this dataset
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    # Not used in this dataset
+    return None
+
+# Step 3: Conduct initial filtering and save to metadata
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Since trait_row is None, skip clinical feature extraction

p1/preprocess/Cardiovascular_Disease/code/GSE235307.py

@@ -0,0 +1,217 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cardiovascular_Disease"
+cohort = "GSE235307"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
+in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE235307"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE235307.csv"
+out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE235307.csv"
+out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE235307.csv"
+json_path = "./output/preprocess/1/Cardiovascular_Disease/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
+is_gene_available = True  # "Gene expression" is explicitly mentioned in the dataset title/summary
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify keys for trait, age, and gender
+# The entire study population consists of heart failure patients, i.e., they all have the trait
+# "Cardiovascular_Disease". Thus, there's no variation for our trait of interest in this dataset.
+trait_row = None
+
+# Age data is present in dictionary key=2, which has multiple distinct values
+age_row = 2
+
+# Gender data is present in dictionary key=1, which has multiple distinct values (Male/Female)
+gender_row = 1
+
+# 2.2 Define conversion functions
+def convert_trait(raw_value: str):
+    # Not used because trait_row is None, but defined for completeness
+    return None
+
+def convert_age(raw_value: str):
+    # Example raw_value: "age: 63"
+    # Extract the substring after the colon and parse as integer
+    parts = raw_value.split(':')
+    if len(parts) < 2:
+        return None
+    try:
+        return int(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(raw_value: str):
+    # Example raw_value: "gender: Male" or "gender: Female"
+    parts = raw_value.split(':')
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == 'male':
+        return 1
+    elif gender_str == 'female':
+        return 0
+    return None
+
+# 3. Save metadata using 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 because trait_row is None
+# (No action needed when trait is not available)
+# STEP3
+# Attempt to read gene expression data; if the library function yields an empty DataFrame,
+# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
+# place actual expression rows under lines that begin with '!').
+
+gene_data = get_genetic_data(matrix_file)
+if gene_data.empty:
+    print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
+    import gzip
+
+    # Locate the marker line first
+    skip_rows = 0
+    with gzip.open(matrix_file, 'rt') as file:
+        for i, line in enumerate(file):
+            if "!series_matrix_table_begin" in line:
+                skip_rows = i + 1
+                break
+
+    # Read the data again, this time not treating '!' as comment
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression="gzip",
+        skiprows=skip_rows,
+        delimiter="\t",
+        on_bad_lines="skip"
+    )
+    gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
+    gene_data.set_index("ID", inplace=True)
+
+# Print the first 20 row IDs to confirm data structure
+print(gene_data.index[:20])
+# Observing these identifiers, they appear to be numeric and not standard human gene symbols, so mapping is needed.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Based on our observation:
+#    - The gene expression dataframe is indexed by numeric IDs like '4', '5', '6', etc.
+#    - In the gene annotation, the column 'ID' contains matching numeric IDs.
+#    - The column 'GENE_SYMBOL' contains the corresponding gene symbols.
+
+# 2. Create the mapping dataframe using the 'get_gene_mapping' function.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Apply the mapping to the gene expression dataframe, handling many-to-many relations.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print out some information for verification.
+print("Mapped gene_data shape:", gene_data.shape)
+print("First few rows of mapped gene_data:")
+print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    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=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.

p1/preprocess/Cardiovascular_Disease/code/GSE256539.py

@@ -0,0 +1,171 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cardiovascular_Disease"
+cohort = "GSE256539"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
+in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE256539"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE256539.csv"
+out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE256539.csv"
+out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE256539.csv"
+json_path = "./output/preprocess/1/Cardiovascular_Disease/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. Check if the dataset likely contains suitable gene expression data
+#    According to the background, it's a digital spatial transcriptomics dataset, so:
+is_gene_available = True
+
+# 2. Determine the availability of trait, age, and gender data in the sample characteristics
+#    The sample characteristics dictionary does not show any explicit or inferred fields for
+#    the trait (Cardiovascular_Disease), age, or gender. Hence all are set to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data conversion functions that return None since no relevant data is available
+def convert_trait(value: str) -> Optional[float]:
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    return None
+
+# 3. Initial filtering and saving metadata
+#    Here, 'is_trait_available' depends on whether we found a valid row for the trait.
+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
+# Attempt to read gene expression data; if the library function yields an empty DataFrame,
+# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
+# place actual expression rows under lines that begin with '!').
+
+gene_data = get_genetic_data(matrix_file)
+if gene_data.empty:
+    print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
+    import gzip
+
+    # Locate the marker line first
+    skip_rows = 0
+    with gzip.open(matrix_file, 'rt') as file:
+        for i, line in enumerate(file):
+            if "!series_matrix_table_begin" in line:
+                skip_rows = i + 1
+                break
+
+    # Read the data again, this time not treating '!' as comment
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression="gzip",
+        skiprows=skip_rows,
+        delimiter="\t",
+        on_bad_lines="skip"
+    )
+    gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
+    gene_data.set_index("ID", inplace=True)
+
+# Print the first 20 row IDs to confirm data structure
+print(gene_data.index[:20])
+# The identifiers appear to be standard human gene symbols (e.g., A2M, A4GALT, AAAS, etc.).
+# Therefore, no mapping to gene symbols is necessary.
+requires_gene_mapping = False
+import os
+import pandas as pd
+
+# STEP7: Data Normalization and Linking
+
+# 1) Normalize the gene symbols in the previously obtained gene_data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2) Load clinical data only if it exists and is non-empty
+if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
+    # Read the file
+    clinical_temp = pd.read_csv(out_clinical_data_file)
+
+    # Adjust row index to label the trait, age, and gender properly
+    if clinical_temp.shape[0] == 3:
+        clinical_temp.index = [trait, "Age", "Gender"]
+    elif clinical_temp.shape[0] == 2:
+        clinical_temp.index = [trait, "Gender"]
+    elif clinical_temp.shape[0] == 1:
+        clinical_temp.index = [trait]
+
+    # 2) Link the clinical and normalized genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Check for severe bias in the trait; remove biased demographic features if present
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note=f"Final check on {cohort} with {trait}."
+    )
+
+    # 6) If the linked data is usable, save it
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+else:
+    # If no valid clinical data file is found, finalize metadata indicating trait unavailability
+    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=True,  # Force a fallback so that it's flagged as unusable
+        df=pd.DataFrame(),
+        note=f"No trait data found for {cohort}, final metadata recorded."
+    )
+    # Per instructions, do not save a final linked data file when trait data is absent.