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p1/preprocess/Sjögrens_Syndrome/clinical_data/GSE84844.csv

@@ -0,0 +1,4 @@
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p1/preprocess/Sjögrens_Syndrome/clinical_data/GSE93683.csv

@@ -0,0 +1,2 @@
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p1/preprocess/Sjögrens_Syndrome/clinical_data/GSE94510.csv

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p1/preprocess/Sjögrens_Syndrome/code/GSE135809.py

@@ -0,0 +1,172 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE135809"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE135809"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE135809.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE135809.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE135809.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine whether gene expression data is available.
+is_gene_available = True  # Based on the background info ("Transcriptome data" and differential gene expression)
+
+# Step 2: Identify data availability and create conversion functions.
+
+# From the sample characteristics dictionary, the only row that clearly distinguishes
+# patients (pSS) from healthy controls (HC) is row 1 via "subject id: HC-..." or "subject id: pSS-...".
+trait_row = 1  # We can parse 'HC' => 0, 'pSS' => 1
+age_row = None  # No age information is provided
+gender_row = None  # No gender information is provided
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert 'subject id: HC-1' or 'subject id: pSS-1' to binary trait:
+    0 for healthy control (HC), 1 for pSS.
+    """
+    # Extract substring after the colon
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+
+    # Check prefix
+    if val.startswith("HC"):
+        return 0
+    elif val.startswith("pSS"):
+        return 1
+    else:
+        return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Placeholder function. No age data available, so this always returns None.
+    """
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Placeholder function. No gender data available, so this always returns None.
+    """
+    return None
+
+# Step 3: Conduct initial filtering and save metadata.
+# Trait data availability is determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+dataset_passed_filter = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: If trait data is available, extract clinical features.
+if trait_row is not None:
+    # Assume 'clinical_data' DataFrame exists in the environment from previous step.
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview the extracted clinical features
+    preview_dict = preview_df(selected_clinical_df, n=5, max_items=200)
+    print("Preview of selected clinical features:", preview_dict)
+
+    # Save the clinical data to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The gene identifiers shown (e.g., '1007_s_at', '1053_at') are Affymetrix probe IDs, not standard gene symbols.
+# Therefore, they require mapping to human gene symbols.
+print("\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify which columns in the gene annotation dataframe correspond to the probe identifiers 
+#    (same IDs as in gene expression data) and which correspond to gene symbols.
+#    From the preview, the "ID" column matches the probe IDs (like '1007_s_at' etc.),
+#    and "Gene Symbol" contains the gene symbols (e.g., 'DDR1 // MIR4640' etc.).
+
+# 2. Extract and build a mapping dataframe (probe -> gene symbol).
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Apply the mapping to convert probe-level data into gene-level data, dividing shared probe values
+#    equally among target genes and summing where probes map to the same gene.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For inspection, print out the shape and a sample of the new index (gene symbols).
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 genes in gene_data index:", list(gene_data.index[:20]))
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/GSE140161.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE140161"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE140161"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE140161.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE140161.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE140161.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/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
+#    Based on background info, this dataset uses Affymetrix chips on whole blood transcriptome,
+#    which strongly suggests gene expression data is available.
+is_gene_available = True
+
+# 2. Identify availability of trait, age, and gender data from the sample characteristics dictionary.
+#    From the dictionary:
+#      0: ['tissue: Whole blood'] (irrelevant or constant)
+#      1: ['Sex: female', 'Sex: male'] (2 unique values => available; we'll treat as gender)
+#      2: ['antissa status: Positive', 'antissa status: Negative']
+#      3: ['antissb status: Negative', 'antissb status: Positive']
+#      4: ['disease state: Sjögren’s syndrome'] (only one unique value => effectively not available)
+#
+#    Thus:
+trait_row = None   # Only one unique value for the disease state => not a usable variable
+age_row = None     # No age info in the dictionary
+gender_row = 1     # "Sex: female"/"Sex: male" => usable
+
+# 2.2. Define data conversion functions
+def convert_trait(value: str):
+    # Not used in this dataset (trait_row=None), but defined for completeness
+    val = value.split(':')[-1].strip().lower()
+    # A generic conversion if there were varied trait statuses:
+    if val == "sjögren’s syndrome":
+        return 1
+    elif val == "":
+        return None
+    return None
+
+def convert_age(value: str):
+    # Not used in this dataset (age_row=None), but defined for completeness
+    val = value.split(':')[-1].strip()
+    # Attempt numeric conversion
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    val = value.split(':')[-1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+
+# 3. Save initial metadata using validate_and_save_cohort_info
+#    The trait is considered available only if trait_row is not None.
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. If trait data is available (trait_row != None), then extract clinical data. Here, trait_row = None, so 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])
+# Based on the observed IDs (e.g., '23064070'), they do not match standard human gene symbols.
+# Hence, they likely require a mapping step to convert these IDs to recognized gene symbols.
+print("The given gene identifiers appear to be probe IDs or numeric identifiers, not 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 which columns in the gene_annotation DataFrame correspond to the probe IDs in gene_data
+#    and which columns contain the gene symbols. From the preview, column "ID" in gene_annotation
+#    stores probe identifiers (e.g., "TC0100006437.hg.1"), and "SPOT_ID.1" contains text that includes
+#    the gene symbol (e.g., "ISG15", "KLHL17", etc.).
+
+# 2. Create a gene mapping dataframe by extracting these two columns and naming them properly.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SPOT_ID.1')
+
+# 3. Convert the probe-level expression data (gene_data) into gene-level data using this mapping.
+#    This will distribute the expression values across multiple genes if a probe maps to multiple symbols,
+#    and sum up values for genes covered by multiple probes.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7
+
+# Since trait_row is None, we do NOT have trait data available. Thus, we cannot link clinical data
+# or perform trait-based missing-value handling or bias analysis. We will, however, normalize gene
+# symbols and then finalize the dataset's metadata accordingly.
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Since trait data is unavailable (trait_row=None), we skip linking with clinical data
+#    and subsequent steps requiring trait information.
+
+# 3. Perform final quality validation and record metadata.
+#    Because is_final=True, we must provide a DataFrame and a boolean is_biased value.
+#    Even though trait is unavailable, we pass normalized_gene_data as df and set is_biased=False.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=normalized_gene_data,
+    note="No trait data available; final gene data saved alone."
+)
+
+# 4. Since trait data is unavailable, we do not produce or save any linked data for association studies.

p1/preprocess/Sjögrens_Syndrome/code/GSE143153.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE143153"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE143153"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE143153.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE143153.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE143153.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/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) Set is_gene_available based on dataset examination
+is_gene_available = True  # Since the data is from "Agilent Whole Human Genome" microarray
+
+# 2.1) Determine data availability for trait, age, and gender
+# From the sample characteristics dictionary, we've identified:
+# - 'aecg disease classification' at row 1 for trait
+# - 'age' at row 2
+# - 'sex' at row 3
+trait_row = 1
+age_row = 2
+gender_row = 3
+
+# 2.2) Define data type conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Converts trait data ('Primary SS', 'non-SS') to binary (1 or 0).
+    Unrecognized or empty values become None.
+    """
+    # Extract the substring after the colon
+    parts = value.split(':')
+    label = parts[-1].strip().lower() if len(parts) >= 2 else None
+    
+    if label == 'primary ss':
+        return 1
+    elif label == 'non-ss':
+        return 0
+    else:
+        return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Converts age data to a continuous numeric value.
+    Unrecognized or empty values become None.
+    """
+    parts = value.split(':')
+    label = parts[-1].strip() if len(parts) >= 2 else None
+    
+    try:
+        return float(label)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Converts gender data ('M' or 'F') to binary (1 for male, 0 for female).
+    Unrecognized or empty values become None.
+    """
+    parts = value.split(':')
+    label = parts[-1].strip().lower() if len(parts) >= 2 else None
+    
+    if label == 'm':
+        return 1
+    elif label == 'f':
+        return 0
+    else:
+        return None
+
+# 3) Conduct initial filtering with validate_and_save_cohort_info
+# Trait availability is determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) If trait data is available, extract clinical features
+#    (the 'clinical_data' dataframe is assumed to be available in the environment)
+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 = preview_df(selected_clinical_df)
+    print("Clinical Data Preview:", preview)
+
+    # Save the selected clinical features
+    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 index shown (numeric IDs rather than standard human gene symbols),
+# these identifiers are not human 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) From the preview, the "ID" column in gene_annotation corresponds to the numeric probe IDs
+#    in the gene expression data, and "GeneName" holds the associated gene symbols.
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='GeneName')
+
+# 2) Apply the probe-to-gene mapping to convert the probe-level expression data into gene-level expression data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# For observation, print the shape of the mapped gene_data and the first few gene symbols
+print("Mapped gene_data shape:", gene_data.shape)
+print("Example gene symbols in the index:", list(gene_data.index[:10]))
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/GSE40611.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE40611"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE40611"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE40611.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE40611.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE40611.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if the dataset likely contains gene expression data
+# Based on the background info (Affymetrix U133 plus 2.0 microarray), we set:
+is_gene_available = True
+
+# Step 2.1: Identify availability of trait, age, and gender data in the sample characteristics
+# The sample characteristics dictionary has:
+# {0: ['disease status: Control', 'disease status: pSS', 'disease status: Sicca'],
+#  1: ['batch: 1', 'batch: 2', 'batch: 3']}
+# We see that row 0 contains "pSS", "Control", and "Sicca", useful for the 'trait'. No rows correspond to age or gender.
+trait_row = 0
+age_row = None
+gender_row = None
+
+# Step 2.2: Define conversion functions for trait, age, and gender.
+
+def convert_trait(value: str):
+    """
+    Convert raw disease status (after the last colon) to a binary indicator.
+    'pSS' -> 1, 'Control' or 'Sicca' -> 0, Others -> None
+    """
+    if not value:
+        return None
+    val = value.split(':')[-1].strip().lower()
+    if 'pss' in val:
+        return 1
+    elif 'control' in val or 'sicca' in val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    """
+    This dataset does not provide age information, so return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    This dataset does not provide gender information, so return None.
+    """
+    return None
+
+# Step 3: Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: If trait data is available, extract clinical features, preview, and save
+if trait_row is not None:
+    df_clinical = 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(df_clinical)
+    print("Clinical Data Preview:", preview_result)
+
+    df_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])
+# These are Affymetrix probe IDs, not human gene symbols, so they need to be mapped
+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))
+# Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation that match the probe IDs and the gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Use the clinical dataframe from STEP 2 (df_clinical) instead of the undefined selected_clinical_df
+clinical_selected_df = df_clinical
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(clinical_selected_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/GSE51092.py

@@ -0,0 +1,167 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE51092"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE51092"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE51092.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE51092.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE51092.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+# Based on the background info mentioning gene expression data, we set:
+is_gene_available = True
+
+# Step 2: Assess availability of trait, age, and gender variables
+# From the sample characteristics dictionary, only one key (0) exists, which records disease state.
+# We map it as the trait row because it has two distinct values ("none" and "Sjögrens syndrome").
+# No other keys exist for age or gender.
+trait_row = 0
+age_row = None
+gender_row = None
+
+# Data type conversion functions
+
+def convert_trait(value: str):
+    """
+    Convert the trait field to binary:
+      'disease state: none' -> 0
+      'disease state: Sjögrens syndrome' -> 1
+    Otherwise, return None.
+    """
+    # Extract the substring after the first colon if it exists
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw_val = parts[1].strip().lower()
+    if "none" in raw_val:
+        return 0
+    elif "sjögrens syndrome" in raw_val:
+        return 1
+    return None
+
+def convert_age(value: str):
+    # Age data is not available in this dataset, so we return None
+    return None
+
+def convert_gender(value: str):
+    # Gender data is not available, so return None
+    return None
+
+# Step 3: Save metadata with initial filtering
+# Trait availability is determined by trait_row != None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: If trait data is available, extract clinical features
+if trait_row is not None:
+    # 'clinical_data' is the DataFrame with sample characteristics obtained in a previous step
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the resulting clinical data
+    print(preview_df(selected_clinical_df))
+    # Save the clinical data
+    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 appear to be Illumina probe IDs and not direct human gene symbols.
+# Therefore, mapping to gene symbols is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the corresponding columns in the annotation DataFrame
+#    The column "ID" in the annotation matches the probe IDs in the gene expression dataset,
+#    and "Symbol" is the column storing the gene symbols.
+prob_col = "ID"
+gene_col = "Symbol"
+
+# 2. Build the mapping dataframe from 'gene_annotation'
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert the probe-level measurements in 'gene_data' to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Replace the undefined variable with the correct name from previous steps
+clinical_selected_df = selected_clinical_df
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(clinical_selected_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/GSE66795.py

@@ -0,0 +1,150 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE66795"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE66795"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE66795.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE66795.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE66795.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset uses whole genome microarray, so it's likely to have gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+# Based on the sample characteristics:
+# - trait_row=2, because "patient group: Control/Patient" maps to presence/absence of Sjögrens_Syndrome.
+# - age_row=None, as age data is not identified.
+# - gender_row=None, because the dataset appears to have only females (constant feature).
+trait_row = 2
+age_row = None
+gender_row = None
+
+def convert_trait(x: str):
+    """
+    Convert the variable in row 2 to binary for Sjögrens_Syndrome.
+    The raw string format is typically "patient group: Control" or "patient group: Patient".
+    """
+    parts = x.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "control":
+        return 0
+    elif val == "patient":
+        return 1
+    return None
+
+# No age or gender data available, so we won't define conversions for them.
+convert_age = None
+convert_gender = None
+
+# 3. Perform initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview
+    preview_result = preview_df(selected_clinical_df)
+    # Save the clinical data
+    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 'ILMN_' identifiers are Illumina probe IDs, not standard gene symbols.
+# Therefore, they need to be mapped to proper gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Decide which columns in the gene_annotation dataframe correspond to
+# the probe identifiers and the gene symbols. Then create a mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Replace the undefined variable with the correct name from previous steps
+clinical_selected_df = selected_clinical_df
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(clinical_selected_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/GSE84844.py

@@ -0,0 +1,186 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE84844"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE84844"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE84844.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE84844.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE84844.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+# Based on the background info (whole-blood transcriptome), we set:
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# From the sample characteristics dictionary, we see:
+# - Row 0 has "disease: Healthy control" and "disease: primary Sjogren's syndrome" => trait
+# - Row 2 has "age: <value>" => age
+# - Row 3 has "gender: Female" and "gender: Male" => gender
+trait_row = 0
+age_row = 2
+gender_row = 3
+
+# 2.2 Data Type Conversion
+def convert_trait(value: str):
+    """
+    Convert trait to binary:
+    - healthy -> 0
+    - primary Sjogren's syndrome -> 1
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    v = parts[-1].strip().lower()
+    if "sjogren" in v or "sjögren" in v:
+        return 1
+    elif "healthy" in v:
+        return 0
+    return None
+
+def convert_age(value: str):
+    """
+    Convert age to continuous (int).
+    If parsing fails, return None.
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    v = parts[-1].strip()
+    try:
+        return float(v)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert gender to binary:
+    - female -> 0
+    - male -> 1
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    v = parts[-1].strip().lower()
+    if v == "female":
+        return 0
+    elif v == "male":
+        return 1
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction and Preview (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview_output = preview_df(selected_clinical_df)
+    print("Preview of Selected Clinical Features:", preview_output)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the identifiers (e.g., '1007_s_at'), they appear to be Affymetrix probe IDs, not human gene symbols.
+# Therefore, gene symbol mapping is required.
+requires_gene_mapping = True
+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))
+# Gene Identifier Mapping
+
+# 1) Identify the column in the gene_annotation dataframe that matches the probe IDs in gene_data: "ID"
+#    Also identify the column that stores the gene symbols: "Gene Symbol"
+
+# 2) Extract the two columns to build the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3) Apply the gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print a small preview to confirm the transformation
+print("Mapped gene expression data (head):")
+print(gene_data.head())
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Replace the undefined variable with the correct name from previous steps
+clinical_selected_df = selected_clinical_df
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(clinical_selected_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/GSE93683.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE93683"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE93683"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE93683.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE93683.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE93683.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+is_gene_available = True  # Genome-wide transcriptome arrays indicate gene expression data.
+
+# 2. Identify rows for trait, age, and gender based on sample characteristics
+#    and define conversion functions.
+trait_row = 0  # 'disease state' row has 'HC' and 'pSS'
+age_row = None  # No age data found
+gender_row = None  # Only one unique value (Female), not useful for association
+
+def convert_trait(value: str):
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    v = parts[1].strip().lower()
+    if v == "pss":
+        return 1
+    elif v == "hc":
+        return 0
+    return None
+
+def convert_age(value: str):
+    return None  # Not used, since age_row is None
+
+def convert_gender(value: str):
+    return None  # Not used, since gender_row is None
+
+# 3. Initial filtering and metadata recording
+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. If trait data exists, extract clinical features and preview/save them
+if trait_row is not None:
+    extracted_clinical_data = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview_result = preview_df(extracted_clinical_data)
+    print(preview_result)
+    extracted_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])
+# The identifiers (e.g., '1007_s_at') are typical Affymetrix probe IDs rather than human gene symbols,
+# hence they require 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify columns for gene identifier and gene symbol in the gene_annotation DataFrame.
+#    The 'ID' column in gene_annotation matches our expression data index, and 'Gene Symbol' contains the gene symbols.
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Get a gene mapping dataframe using the library function get_gene_mapping.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level measurements to gene expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Define a local reference to the clinical dataframe we extracted in step 2
+# (assuming "extracted_clinical_data" still exists in the environment).
+clinical_selected_df = extracted_clinical_data
+
+# 2. Link clinical and gene expression data on sample IDs
+linked_data = geo_link_clinical_genetic_data(clinical_selected_df, normalized_gene_data)
+
+# 3. Handle missing values systematically using the trait column name in the 'trait' variable
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features (trait, age, gender) using the same trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note=f"Preprocessed with trait column named '{trait}'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/GSE94510.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+cohort = "GSE94510"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
+in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE94510"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/GSE94510.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/GSE94510.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/GSE94510.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on the Omni/transcriptome details, this dataset likely has gene expression data.
+
+# 2) Variable Availability
+#    After examining the sample characteristics dictionary, we found:
+#      trait data is under key=0 with 2 unique values ("disease: pSS" or "disease: HC"), so trait_row=0.
+#      age data is not available, so age_row=None.
+#      gender data is constant ("Female"), so it is not usable for association studies, hence gender_row=None.
+trait_row = 0
+age_row = None
+gender_row = None
+
+# 2.2) Data Type Conversion Functions
+def convert_trait(value: str):
+    """
+    Convert 'disease: pSS' to 1 and 'disease: HC' to 0.
+    Return None if unknown.
+    """
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "pss":
+        return 1
+    elif val == "hc":
+        return 0
+    return None
+
+def convert_age(value: str):
+    """
+    No age data available, so always return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    No usable gender variation in this dataset, so always return None.
+    """
+    return None
+
+# 3) Save Metadata (Initial Filtering)
+#    Trait data is available if trait_row is not None
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+#    Perform this step only if trait_row is not None.
+if trait_row is not None:
+    clinical_selected_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # 'clinical_data' is assumed to be in the environment
+        trait='Disease',
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted clinical features
+    preview_result = preview_df(clinical_selected_df, n=5, max_items=200)
+    print("Preview of extracted clinical features:", preview_result)
+    
+    # Save the clinical features to CSV
+    clinical_selected_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])
+print("They appear to be Affymetrix probe set identifiers, not standard human gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the column in 'gene_annotation' that matches the gene expression dataset's "ID" column
+#    and the column storing the gene symbols. From the preview, these appear to be 'ID' and 'Gene Symbol'.
+probe_column = "ID"
+symbol_column = "Gene Symbol"
+
+# 2. Obtain the mapping dataframe using the get_gene_mapping function
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_column, gene_col=symbol_column)
+
+# 3. Convert probe-level data to gene-level data by applying the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+print("Gene-level expression data shape:", gene_data.shape)
+# STEP7
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and gene expression data (use the correct clinical variable name: clinical_selected_df)
+linked_data = geo_link_clinical_genetic_data(clinical_selected_df, normalized_gene_data)
+
+# 3. Handle missing values systematically.
+#    The trait column in clinical_selected_df is named "Disease", not the environment variable "trait".
+linked_data = handle_missing_values(linked_data, "Disease")
+
+# 4. Check for biased features (trait, age, gender) using "Disease" as the trait column name
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, "Disease")
+
+# 5. Final quality validation and record metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note="Preprocessed with trait column named 'Disease'."
+)
+
+# 6. If usable, save linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)

p1/preprocess/Sjögrens_Syndrome/code/TCGA.py

@@ -0,0 +1,66 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sjögrens_Syndrome"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sjögrens_Syndrome/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Sjögrens_Syndrome/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Sjögrens_Syndrome/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Sjögrens_Syndrome/cohort_info.json"
+
+import os
+
+# List of subdirectories from the TCGA root directory
+subdirectories = [
+    'TCGA-LGG', 'CrawlData.ipynb', '.DS_Store',
+    'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)',
+    'TCGA_Thymoma_(THYM)', 'TCGA_Testicular_Cancer_(TGCT)',
+    'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)',
+    'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)', 'TCGA_Pancreatic_Cancer_(PAAD)',
+    'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)',
+    'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)', 'TCGA_Lung_Cancer_(LUNG)',
+    'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)',
+    'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)', 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)',
+    'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)',
+    'TCGA_Endometrioid_Cancer_(UCEC)', 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)',
+    'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)',
+    'TCGA_Bile_Duct_Cancer_(CHOL)', 'TCGA_Adrenocortical_Cancer_(ACC)',
+    'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+relevant_folder = None
+for folder in subdirectories:
+    folder_lower = folder.lower()
+    # Searching for terms like 'sjogren' or 'sicca' to match "Sjögrens_Syndrome"
+    if "sjogren" in folder_lower or "sicca" in folder_lower:
+        relevant_folder = folder
+        break
+
+if not relevant_folder:
+    # No suitable directory found, we skip this trait
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable directory found for trait Sjögrens_Syndrome. Skipping this trait.")
+else:
+    clinical_file, genetic_file = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, relevant_folder))
+
+    clinical_data = pd.read_csv(clinical_file, index_col=0, sep='\t')
+    genetic_data = pd.read_csv(genetic_file, index_col=0, sep='\t')
+
+    print("Clinical data columns:", clinical_data.columns.tolist())

p1/preprocess/Sjögrens_Syndrome/gene_data/GSE135809.csv

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+size 12770430

p1/preprocess/Sjögrens_Syndrome/gene_data/GSE40611.csv

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+size 10808851

p1/preprocess/Sjögrens_Syndrome/gene_data/GSE51092.csv

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+size 27673057

p1/preprocess/Sjögrens_Syndrome/gene_data/GSE84844.csv

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+size 15946036

p1/preprocess/Sjögrens_Syndrome/gene_data/GSE93683.csv

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+size 12769411

p1/preprocess/Stomach_Cancer/clinical_data/GSE208099.csv

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+1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0
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p1/preprocess/Stomach_Cancer/code/GSE118916.py

@@ -0,0 +1,116 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Stomach_Cancer"
+cohort = "GSE118916"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Stomach_Cancer"
+in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE118916"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Stomach_Cancer/GSE118916.csv"
+out_gene_data_file = "./output/preprocess/1/Stomach_Cancer/gene_data/GSE118916.csv"
+out_clinical_data_file = "./output/preprocess/1/Stomach_Cancer/clinical_data/GSE118916.csv"
+json_path = "./output/preprocess/1/Stomach_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. Check if the dataset likely contains gene expression data
+is_gene_available = True  # Based on the background info, it's microarray gene expression
+
+# 2.1 Identify the data availability for each variable
+trait_row = None   # No row found for trait (Stomach_Cancer)
+age_row = None     # No row found for age
+gender_row = 0     # Row 0 has 2 distinct values ["gender: female", "gender: male"]
+
+# 2.2 Define conversion functions for each variable
+def convert_trait(value: str):
+    # No trait data found, so always return None
+    return None
+
+def convert_age(value: str):
+    # No age data found, so always return None
+    return None
+
+def convert_gender(value: str):
+    # Extract the part after the colon
+    val = value.split(':')[-1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    else:
+        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. Since trait_row is None, skip clinical feature extraction
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These probe set IDs (e.g., "11715100_at") are typically Affymetrix array probe identifiers,
+# not standard human gene symbols. Therefore, they require mapping to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Determine which columns in the gene_annotation dataframe contain
+#    the same identifiers as the gene_data's index and which contain the gene symbols.
+# From the preview, "ID" matches the probe identifiers and "Gene Symbol" contains gene symbols.
+
+# 2) Build a gene mapping dataframe by extracting these two columns from gene_annotation.
+mapping_df = get_gene_mapping(gene_annotation, "ID", "Gene Symbol")
+
+# 3) Convert probe-level measurements to gene-level measurements.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+# Per the instructions and reviewer's feedback, this dataset does not actually have a trait column.
+# Therefore, we cannot proceed with final validation or linking clinical data based on a non-existent trait.
+
+# 1. Normalize the obtained gene data and save.
+#    (We can still provide normalized gene expression data even though no trait is available.)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since the trait is not available, we skip final linking, missing-value handling, and final validation steps.
+# We set is_trait_available=False to reflect the actual dataset status. 
+# No final validation or saving of linked data will be performed here.
+
+is_trait_available = False
+print("Trait is not available => skipping linking, quality checks, and final validation.")

p1/preprocess/Stomach_Cancer/code/GSE128459.py

@@ -0,0 +1,140 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Stomach_Cancer"
+cohort = "GSE128459"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Stomach_Cancer"
+in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE128459"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Stomach_Cancer/GSE128459.csv"
+out_gene_data_file = "./output/preprocess/1/Stomach_Cancer/gene_data/GSE128459.csv"
+out_clinical_data_file = "./output/preprocess/1/Stomach_Cancer/clinical_data/GSE128459.csv"
+json_path = "./output/preprocess/1/Stomach_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background info ("Expression profiling..." in the GEO record), we consider gene expression data present.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Inspecting the sample characteristics shows only the following rows:
+# 0 -> ['tissue: Gastric Cancer']
+# 1 -> ['sample type: Cells', 'sample type: Organoids', 'sample type: PR', 'sample type: PRX']
+# All samples appear to be cancer samples, so there's no variation for the trait.
+# No age or gender data is found either.
+trait_row = None   # No variation for trait => treat as not available
+age_row = None     # No age info available
+gender_row = None  # No gender info available
+
+# Define data conversion functions as placeholders.
+def convert_trait(value: str):
+    """
+    Convert trait data to a binary format.
+    Since the data is not actually available, we simply parse but ultimately return None here.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Convert age data to a continuous format.
+    Since no age data is available, return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert gender data to a binary format: female -> 0, male -> 1.
+    Since no gender data is available, return None.
+    """
+    return None
+
+# 3. Save Metadata (initial filtering)
+# Trait is not available (trait_row is None), so is_trait_available = False.
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip clinical data extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These are Illumina probe IDs, not 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))
+# Gene Identifier Mapping
+
+# 1) Identify the columns in the gene annotation that match the probe IDs in the expression data 
+#    and the gene symbols, respectively.
+id_column = "ID"
+symbol_column = "Symbol"
+
+# 2) Get a gene mapping dataframe from the annotation dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_column, gene_col=symbol_column)
+
+# 3) Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+
+# Since the dataset has no trait data (trait_row was None), we cannot link clinical data
+# or perform trait-based analysis. However, we should still normalize the gene data
+# and record metadata indicating that the dataset is not usable for trait association.
+
+# 1) Normalize the obtained gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# 2) Save the normalized data as a CSV file
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 3) Perform final validation to record the dataset's unusability for trait analysis
+# We set is_trait_available=False, so it will be deemed not usable.
+# We must still pass is_biased (boolean) and a dataframe. Here we set is_biased=False
+# for consistency, because the absence of the trait is what makes it unusable, not bias.
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=normalized_gene_data,
+    note="No trait data available. Only normalized gene data."
+)
+
+print("No trait data available; only normalized gene data is saved. Final metadata recorded.")