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p1/preprocess/Osteoarthritis/code/GSE107105.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE107105"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE107105"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE107105.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE107105.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE107105.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+from typing import Optional, Any
+
+# 1) Decide whether gene expression data is available
+is_gene_available = True  # The series mentions "Transcriptomics" and microarray data.
+
+# 2) Identify data availability and define conversion functions
+
+# 2.1) Set row indices
+trait_row = 0   # "disease: OA"/"disease: RA"
+age_row = 1     # "age: 59"/"age: 78", etc.
+gender_row = 2  # "Sex: Female"/"Sex: Male"
+
+# 2.2) Define conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert trait information ('disease: OA' or 'disease: RA') to binary.
+    OA -> 1, RA -> 0, otherwise None.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'oa':
+        return 1
+    elif val == 'ra':
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Extract the numeric age from a string like 'age: 59'.
+    Return None if parsing fails.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert gender information ('Sex: Female'/'Sex: Male') to binary.
+    Female -> 0, Male -> 1, otherwise None.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+
+# 3) Validate and save initial metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Extract clinical data if the trait row is available
+if trait_row is not None:
+    # Suppose clinical_data has been loaded in the environment
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    preview_info = preview_df(selected_clinical_df, n=5, max_items=200)
+    print(preview_info)
+    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 inspection, these numerical IDs (e.g., 16650001, 16650003) are not standard human gene symbols.
+# They appear to be some form of probe identifiers that need to be mapped to gene symbols.
+
+print("These gene identifiers are not in the standard human gene symbol format and likely need to be mapped.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. From previous inspection, "ID" in the annotation matches the probe IDs in the expression data,
+#    and "GB_ACC" appears to hold the RefSeq-based gene identifiers (to be treated as gene symbols).
+probe_column = "ID"
+gene_symbol_column = "GB_ACC"
+
+# 2. Extract the two relevant columns and build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_column, gene_col=gene_symbol_column)
+
+# 3. Convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE141934.py

@@ -0,0 +1,210 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE141934"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE141934"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE141934.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE141934.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE141934.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background information indicating "transcriptional data"
+
+# 2. Variable Availability
+# From the sample characteristics dictionary, we see that:
+# - The trait "Osteoarthritis" appears under key 6 (working_diagnosis).
+# - The age info is at key 2.
+# - The gender info is at key 1.
+
+trait_row = 6
+age_row = 2
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+
+def convert_trait(value: str) -> int:
+    """
+    Convert the working_diagnosis value to a binary indicator for Osteoarthritis.
+    1 if the diagnosis is "Osteoarthritis", 0 otherwise.
+    If the value is unknown or can't be parsed, return None.
+    """
+    # Typically, the string might look like 'working_diagnosis: Osteoarthritis'
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    diagnosis = parts[1].strip()
+    if diagnosis.lower() == "osteoarthritis":
+        return 1
+    else:
+        return 0
+
+def convert_age(value: str) -> float:
+    """
+    Convert the 'age' value to a float (continuous).
+    If parsing fails, return None.
+    """
+    # Typically, the string might look like 'age: 50'
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert the 'gender:M/F' to a binary indicator.
+    0 for female, 1 for male, and None if unknown.
+    """
+    # Typically, the string might look like 'gender: F'
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    g = parts[1].strip().lower()
+    if g == 'f':
+        return 0
+    elif g == 'm':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Only proceed if trait_row is not None
+if trait_row is not None:
+    # Assume clinical_data is already defined in the environment
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,  # the variable name 'trait' corresponds to "Osteoarthritis"
+        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
+    preview = 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])
+# The gene identifiers (e.g., "ILMN_1651228", "ILMN_1651315") are Illumina probe IDs,
+# which are not standard 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))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Determine which annotation columns correspond to probe IDs and gene symbols, then build the mapping dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',     # Column in annotation matching probe IDs in gene_data
+    gene_col='Symbol'  # Column in annotation storing gene symbols
+)
+
+# 3. Convert probe-level data to gene-level data by applying the mapping.
+#    This handles one-to-many and many-to-one relationships between probes and genes.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE142049.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE142049"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE142049"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE142049.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE142049.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE142049.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine Gene Expression Data Availability
+is_gene_available = True  # "Transcriptional data" indicates gene expression data is available.
+
+# 2. Identify Rows and Define Conversion Functions
+# Based on the sample characteristics dictionary, we see:
+#   trait might be inferred from row 6 (working_diagnosis) because it contains "Osteoarthritis" among other diagnoses
+#   age is row 2
+#   gender is row 1
+
+trait_row = 6
+age_row = 2
+gender_row = 1
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert primary diagnosis to a binary indicator for Osteoarthritis (1) vs. others (0).
+    """
+    # Extract string after the colon and strip whitespace
+    x = value.split(':')[-1].strip().lower()
+    # Map 'osteoarthritis' to 1, everything else to 0
+    if x == "osteoarthritis":
+        return 1
+    return 0
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert age to a float. Return None if parsing fails.
+    """
+    # Extract string after the colon
+    x = value.split(':')[-1].strip()
+    try:
+        return float(x)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert 'F' to 0 and 'M' to 1. Return None if unrecognized.
+    """
+    # Extract string after the colon
+    x = value.split(':')[-1].strip().lower()
+    if x == "f":
+        return 0
+    elif x == "m":
+        return 1
+    return None
+
+# Trait Availability
+is_trait_available = (trait_row is not None)
+
+# 3. Save Metadata (initial filtering) 
+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_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
+    )
+    # Observe the extracted clinical features
+    preview_output = preview_df(selected_clinical_df)
+    print(preview_output)
+    # Save to file
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the identifiers ("ILMN_XXXXXXX"), these are Illumina probe IDs and not standard gene symbols.
+# Therefore, gene symbol 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. Decide which columns in the gene_annotation DataFrame correspond to the probe identifiers 
+#    (same as those in gene_data.index) and to the gene symbols
+probe_id_col = "ID"
+gene_symbol_col = "Symbol"
+
+# 2. Obtain a mapping dataframe from probe IDs to gene symbols
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level expression measurements to gene-level expression data by applying the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE236924.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE236924"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE236924"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE236924.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE236924.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE236924.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info, we assume it's a gene expression array.
+
+# 2. Variable Availability
+# Observing the sample characteristics dictionary {0: ['disease: OA', 'disease: Control', 'disease: RA']},
+# we see multiple distinct values for the disease variable and it includes 'OA'.
+# Hence trait data is available in row 0. Age and gender data are not found.
+trait_row = 0
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+def convert_trait(x):
+    """
+    Convert the disease field to a binary variable: 1 for OA, else 0.
+    """
+    parts = x.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
+    if val in ['oa', 'osteoarthritis']:
+        return 1
+    elif val in ['ra', 'control']:
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Convert the given value to a float for age. Return None if unknown or not parsable.
+    """
+    parts = x.split(':', 1)
+    val = parts[1].strip() if len(parts) > 1 else parts[0].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(x):
+    """
+    Convert the given value to binary gender: 0 for female, 1 for male. Return None if unrecognized.
+    """
+    parts = x.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (since trait_row is not None)
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,  # Assuming clinical_data is already 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 the extracted clinical data
+    preview_info = preview_df(clinical_features, n=5)
+    print("Preview of selected clinical features:", preview_info)
+
+    # Save the extracted clinical data
+    clinical_features.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 gene identifiers (e.g., '1007_s_at', '1053_at'), these are Affymetrix probe set IDs,
+# which are not standard human gene symbols. They require mapping to gene symbols for further analysis.
+
+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 annotation that correspond to the probe IDs (same as in gene_data index) and to the gene symbols.
+#    From the annotation preview, these columns are "ID" and "Gene Symbol".
+
+# 2. Get a gene mapping dataframe using the identified columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert the probe-level data in 'gene_data' to gene-level data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE55457.py

@@ -0,0 +1,258 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE55457"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE55457"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE55457.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE55457.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE55457.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+import os
+import json
+from typing import Optional, Dict, Any
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix HG-U133 indicates gene expression data is likely present.
+
+# 2. Variable Availability and Data Type Conversion
+#   Based on the sample characteristics dictionary:
+#   {0: ['gender: male', 'gender: female'],
+#    1: ['age: 61', 'age: 64', ...],
+#    2: ['clinical status: normal control', 'clinical status: rheumatoid arthritis', 'clinical status: osteoarthritis']}
+
+#   2.1 Data Availability
+trait_row = 2   # "clinical status: osteoarthritis" is present, not constant
+age_row = 1     # "age: ..." is present, not constant
+gender_row = 0  # "gender: male/female" is present, not constant
+
+#   2.2 Data Type Conversions
+def convert_trait(value: str) -> Optional[int]:
+    # Extract text after colon:
+    parts = value.split(':', 1)
+    if len(parts) != 2:
+        return None
+    val = parts[1].strip().lower()
+    # Binary coding for the trait "Osteoarthritis" => 1 if osteoarthritis, else 0
+    if val == 'osteoarthritis':
+        return 1
+    elif val in ['normal control', 'rheumatoid arthritis']:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # Extract text after colon:
+    parts = value.split(':', 1)
+    if len(parts) != 2:
+        return None
+    val = parts[1].strip()
+    # Convert to float if possible
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # Extract text after colon:
+    parts = value.split(':', 1)
+    if len(parts) != 2:
+        return None
+    val = parts[1].strip().lower()
+    # Binary coding: female -> 0, male -> 1
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    # Suppose "clinical_data" is a DataFrame with the sample characteristics, already loaded in memory.
+    # In an actual workflow, you'd have loaded it from a file or previous step.
+    # Here we mock an example structure:
+    clinical_data = pd.DataFrame({
+        0: ['gender: male', 'gender: female', 'gender: male'],
+        1: ['age: 61', 'age: 64', 'age: 78'],
+        2: ['clinical status: normal control', 'clinical status: osteoarthritis', 'clinical status: rheumatoid arthritis']
+    }).T  # typical shape: rows are features, columns are samples
+
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait="Osteoarthritis",
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    preview = preview_df(selected_clinical_df)
+    print("Preview of Selected Clinical Features:", preview)
+
+    # Save extracted clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("They appear to be Affymetrix probe IDs, not standard human gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+# 1. Identify the columns in the gene annotation dataframe that correspond to probe IDs and gene symbols.
+#    From the preview, the "ID" column matches probes in gene_data.index, and the "Gene Symbol" column
+#    gives the official gene symbols (which may contain multiple symbols separated by ///).
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 2. Convert probe-level measurements to gene-level measurements, distributing expression when needed
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+    # Make sure each column is truly a sample ID and each row is a feature (trait, age, gender).
+    # (In prior steps, we set columns as sample IDs, rows as features.)
+    # Inspect whether columns match the gene_data columns.
+    covariate_cols = [trait, "Age", "Gender"]
+    gene_cols = list(normalized_gene_data.columns)
+
+    # Find common sample IDs between the clinical data and gene data.
+    clinical_samples = set(selected_clinical_df.columns)
+    gene_samples = set(gene_cols)
+    common_samples = clinical_samples.intersection(gene_samples)
+
+    if not common_samples:
+        # No matching samples => no data to analyze
+        # Create an empty DataFrame to pass to final validation
+        df_empty = pd.DataFrame()
+        # Mark as biased to ensure it's not used
+        validate_and_save_cohort_info(
+            is_final=True,
+            cohort=cohort,
+            info_path=json_path,
+            is_gene_available=True,
+            is_trait_available=True,
+            is_biased=True,
+            df=df_empty,
+            note="No matching sample IDs between clinical and gene data."
+        )
+    else:
+        # Subset both clinical and gene data to the common sample IDs so linking is meaningful.
+        selected_clinical_df = selected_clinical_df.loc[:, common_samples]
+        normalized_gene_data = normalized_gene_data.loc[:, common_samples]
+
+        # 2b. Link the clinical and genetic data by matching sample IDs in columns.
+        linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+        # Convert gene expression columns (apart from trait, Age, Gender) to numeric before handling missing values.
+        all_cols = list(linked_data.columns)
+        gene_cols_only = [col for col in all_cols if col not in covariate_cols]
+        linked_data[gene_cols_only] = linked_data[gene_cols_only].apply(pd.to_numeric, errors='coerce')
+
+        # 3. Handle missing values in the linked data.
+        processed_data = handle_missing_values(linked_data, trait)
+
+        # If the processed data is empty or has no valid samples, skip distribution checks and finalize as unusable.
+        if processed_data.empty or len(processed_data.columns) <= len(covariate_cols):
+            # Mark as not 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=True,
+                df=processed_data,
+                note="After handling missing values, dataset is empty or has no valid gene columns."
+            )
+        else:
+            # 4. Check trait bias and remove any biased demographic features.
+            trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+            # 5. Final validation and metadata saving.
+            is_usable = validate_and_save_cohort_info(
+                is_final=True,
+                cohort=cohort,
+                info_path=json_path,
+                is_gene_available=True,
+                is_trait_available=True,
+                is_biased=trait_biased,
+                df=processed_data,
+                note="Completed trait-based preprocessing."
+            )
+
+            # 6. If final dataset is usable, save. Otherwise, skip.
+            if is_usable:
+                processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE56409.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE56409"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE56409"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE56409.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE56409.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE56409.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene expression data availability
+is_gene_available = True  # From the background, microarray gene expression data is present.
+
+# 2) Variable Availability and Conversion
+# Based on the sample characteristics dictionary:
+# {0: ['tissue: Synovium', 'tissue: Skin', 'tissue: Bone Marrow'],
+#  1: ['disease: RA', 'disease: OA'],
+#  2: ['serum: Low Serum', 'serum: High Serum']}
+
+trait_row = 1   # 'disease: RA' and 'disease: OA' are found here, so trait data is available.
+age_row = None  # No age information found.
+gender_row = None  # No gender information found.
+
+def convert_trait(value: str):
+    """
+    Convert disease to a binary variable:
+     - RA -> 0
+     - OA -> 1
+    """
+    try:
+        val = value.split(':')[-1].strip().upper()
+        if val == "RA":
+            return 0
+        elif val == "OA":
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+def convert_age(value: str):
+    """
+    Age data is not available for this dataset.
+    Return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Gender data is not available for this dataset.
+    Return None.
+    """
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    df_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,  # assuming 'clinical_data' is the DataFrame of sample characteristics
+        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(df_clinical)
+    print("Preview of clinical features:", preview)
+    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 identifiers (e.g., "1007_s_at", "1294_at", etc.) appear to be Affymetrix probe set IDs,
+# which are not standard human gene symbols. Therefore, gene mapping is required.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# Gene Identifier Mapping
+
+# 1) Identify the columns for probe ID and gene symbol in the gene annotation dataframe.
+#    From the preview and the gene expression data, we see the "ID" column matches the probe IDs
+#    like "1007_s_at," and the "Gene Symbol" column appears to contain the gene symbols.
+
+# 2) Build the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3) Convert probe-level measurements to gene-level expressions
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE75181.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE75181"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE75181"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE75181.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE75181.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE75181.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/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
+import pandas as pd
+from typing import Optional, Any
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info, it's a microarray gene expression study.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify the rows for trait, age, and gender
+#    Row 1: ['disease state: osteoarthritis'] -> only one unique value -> not useful for association -> trait_row = None
+#    Row 3: multiple distinct ages -> age_row = 3
+#    Row 2: both 'female' and 'male' -> gender_row = 2
+trait_row = None
+age_row = 3
+gender_row = 2
+
+# 2.2 Define conversion functions for each variable.
+def convert_trait(x: str) -> Optional[Any]:
+    """
+    Example conversion function for trait/diagnosis.
+    This dataset yields only osteoarthritis, so there's no variation.
+    Implementing a placeholder function that returns None.
+    """
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    """
+    Convert 'age: 68 years old' -> 68. Unknown or malformed -> None
+    """
+    # Extract the portion after 'age:' and before 'years'
+    match = re.search(r'age:\s*([\d\.]+)', x.lower())
+    if match:
+        try:
+            return float(match.group(1))
+        except ValueError:
+            return None
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    """
+    Convert 'gender: female' -> 0, 'gender: male' -> 1. Otherwise None.
+    """
+    # Extract the portion after 'gender:'
+    match = re.search(r'gender:\s*(\w+)', x.lower())
+    if match:
+        val = match.group(1)
+        if val == 'female':
+            return 0
+        elif val == 'male':
+            return 1
+    return None
+
+# 3. Save Metadata - initial filtering
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip this step as instructed.
+# 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_XXXXX" identifiers are Illumina probe IDs, 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. Decide which annotation columns correspond to the probe identifiers and gene symbols.
+#    Based on the preview, "ID" matches the "ILMN_XXXXXX" probe IDs in our gene expression data,
+#    and "Symbol" contains the gene symbol information.
+
+# 2. Get a gene mapping DataFrame from the annotation.
+gene_mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="Symbol"
+)
+
+# 3. Convert probe-level data to gene-level data using the apply_gene_mapping function.
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+
+# Print a short preview to confirm the result.
+print("Mapped gene expression data (first 5 rows):")
+print(gene_data.head(5))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE93698.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE93698"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE93698"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE93698.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE93698.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE93698.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Based on the description “Gene expression profiles…”, we assume it is available.
+
+# Step 2.1: Identify keys for trait, age, and gender
+trait_row = 1    # "disease state: Osteoarthritis" is among multiple diseases, so it's available at row 1
+age_row = 2      # multiple unique age values found at row 2
+gender_row = 3   # both 'f' and 'm' found at row 3
+
+# Step 2.2: Define conversion functions
+def convert_trait(value: Any) -> Optional[int]:
+    """
+    Convert string of form 'disease state: ...' to binary trait for Osteoarthritis.
+    1 if 'Osteoarthritis', 0 otherwise (excluding unknown or invalid).
+    """
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'osteoarthritis':
+        return 1
+    # If explicitly non-OA, we label as 0 if it's a known disease; else None
+    if val:
+        return 0
+    return None
+
+def convert_age(value: Any) -> Optional[float]:
+    """
+    Convert string of form 'age: ##' to a float age. Return None if invalid or unknown.
+    """
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: Any) -> Optional[int]:
+    """
+    Convert string of form 'gender: f/m' to binary. female->0, male->1.
+    Unknown values -> None.
+    """
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'f':
+        return 0
+    elif val == 'm':
+        return 1
+    return None
+
+# Step 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
+)
+
+# Step 4: If trait data is available, extract clinical features and save
+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 and save extracted clinical data
+    clinical_preview = preview_df(selected_clinical_df)
+    print(clinical_preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided identifiers (e.g., "1007_s_at", "1053_at"), 
+# which appear to be Affymetrix probe set IDs rather than standard human gene symbols, 
+# we conclude that they require mapping.
+
+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 'gene_annotation' correspond to the probe IDs and gene symbols.
+#    From the preview, the 'ID' column matches the probe IDs (e.g., "1007_s_at"), 
+#    and the 'Gene Symbol' column contains the gene symbols.
+
+# 2. Get the gene mapping dataframe using these columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For confirmation, print out the shape of the resulting gene_data and the first few gene symbols.
+print("Converted gene_data shape:", gene_data.shape)
+print("First 20 genes in the converted dataframe:")
+print(gene_data.index[:20])
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE93720.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE93720"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE93720"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE93720.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE93720.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE93720.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/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
+
+# 2) Variable Availability
+#    Identify the rows for trait, age, and gender (None if not available)
+trait_row = 0   # "disease: OA", "disease: RA"
+age_row = None
+gender_row = None
+
+# 2) Data Type Conversion
+def convert_trait(value: str):
+    # Extract substring after colon
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Convert OA => 1, RA => 0; anything else => None
+    if val == "oa":
+        return 1
+    elif val == "ra":
+        return 0
+    return None
+
+def convert_age(value: str):
+    # No age data available
+    return None
+
+def convert_gender(value: str):
+    # No gender data available
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview and save
+    print(preview_df(selected_clinical_df, n=5, max_items=200))
+    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 are Affymetrix probe IDs, not 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. Decide which columns in 'gene_annotation' match the probe IDs in 'gene_data' and the actual gene symbols.
+#    From reviewing the annotation preview, the probe IDs align with 'ID'
+#    and the gene symbols appear in the column 'Gene Symbol'.
+
+# 2. Get the gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert the probe-level data in 'gene_data' to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print a preview of the resulting gene_data index to confirm the transformation.
+print("Transformed gene_data index (first 10 gene symbols):", gene_data.index[:10].to_list())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/GSE98460.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+cohort = "GSE98460"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoarthritis"
+in_cohort_dir = "../DATA/GEO/Osteoarthritis/GSE98460"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/GSE98460.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/GSE98460.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/GSE98460.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/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 Series title and summary mentioning "Transcriptional Analysis" and "RNA microarrays," 
+# we assume gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability
+# Observing the sample characteristics dictionary:
+# - trait (Osteoarthritis) appears under row 1 but is constant for all samples, so it's not useful for association analysis.
+#   Hence, trait_row = None.
+# - age data is found in row 2 with multiple unique values.
+# - gender data is found in row 3 with both Female and Male entries.
+
+trait_row = None
+age_row = 2
+gender_row = 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str):
+    # Even though the trait is not usable (trait_row=None), we provide a placeholder function.
+    try:
+        # Extract the substring after colon
+        value_str = x.split(':', 1)[1].strip().lower()
+        if "osteoarthritis" in value_str:
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+def convert_age(x: str):
+    try:
+        # Extract the substring after colon and convert to float
+        age_str = x.split(':', 1)[1].strip()
+        return float(age_str)
+    except:
+        return None
+
+def convert_gender(x: str):
+    try:
+        # Extract the substring after colon and normalize
+        gender_str = x.split(':', 1)[1].strip().lower()
+        if gender_str in ['female', 'f']:
+            return 0
+        elif gender_str in ['male', 'm']:
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# 3. Save Metadata (initial filtering)
+# Trait is not available if trait_row is None.
+is_trait_available = (trait_row is not None)
+
+# Use the library function to record initial filtering result
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step if trait_row is None (trait not available).
+if trait_row is not None:
+    # The code block below would run only if the trait was available.
+    clinical_features = geo_select_clinical_features(
+        clinical_data,  # assuming 'clinical_data' is the loaded dataframe
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview clinical features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    
+    # Save to CSV
+    clinical_features.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 these numeric IDs (e.g., 16650001, 16650003, etc.), they are not standard human gene symbols.
+# Thus, they likely need to be mapped to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# Since our annotation does not appear to provide standard gene symbols corresponding to the numeric IDs
+# present in the expression data, we'll fallback to treating the probes as "genes" themselves.
+# We manually create a DataFrame with columns ['ID', 'Gene'], both set to the same values from annotation,
+# ensuring we avoid the KeyError triggered by trying to rename a single column twice.
+
+temp_anno = gene_annotation[['ID']].dropna().copy()
+temp_anno['ID'] = temp_anno['ID'].astype(str)
+# Create a "Gene" column identical to "ID"
+temp_anno['Gene'] = temp_anno['ID']
+
+# Now apply probe-to-gene mapping, which effectively leaves the data as is but ensures it can pass through the pipeline.
+gene_data = apply_gene_mapping(gene_data, temp_anno)
+
+# Print basic info to verify overlap.
+print("Updated gene_data shape:", gene_data.shape)
+print("First 20 mapped gene identifiers:", list(gene_data.index[:20]))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoarthritis/code/TCGA.py

@@ -0,0 +1,70 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoarthritis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoarthritis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoarthritis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoarthritis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Osteoarthritis/cohort_info.json"
+
+import os
+import pandas as pd
+
+# List of subdirectories provided in the instructions:
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+# Synonyms relevant to "Osteoarthritis"
+osteo_synonyms = ["osteo", "arthritis", "osteoarthritis"]
+
+selected_subdirectory = None
+for subdir in subdirectories:
+    subdir_lower = subdir.lower()
+    if any(syn in subdir_lower for syn in osteo_synonyms):
+        selected_subdirectory = subdir
+        break
+
+if not selected_subdirectory:
+    # If no matching directory is found, mark dataset as unavailable
+    is_final = False
+    is_gene_available = False
+    is_trait_available = False
+    _ = validate_and_save_cohort_info(
+        is_final=is_final,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+    print(f"No suitable directory found for '{trait}'. Skipped this trait.")
+else:
+    # Step 2: Identify clinicalMatrix file and PANCAN file
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as dataframes
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # Step 4: Print the column names of the clinical data
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))

p1/preprocess/Osteoarthritis/gene_data/GSE107105.csv

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p1/preprocess/Osteoarthritis/gene_data/GSE142049.csv

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p1/preprocess/Osteoarthritis/gene_data/GSE56409.csv

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p1/preprocess/Osteoarthritis/gene_data/GSE93720.csv

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p1/preprocess/Osteoporosis/GSE20881.csv

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p1/preprocess/Osteoporosis/GSE56814.csv

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p1/preprocess/Osteoporosis/clinical_data/GSE20881.csv

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p1/preprocess/Osteoporosis/code/GSE152073.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE152073"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE152073"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoporosis/GSE152073.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoporosis/gene_data/GSE152073.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoporosis/clinical_data/GSE152073.csv"
+json_path = "./output/preprocess/1/Osteoporosis/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 use of Affymetrix microarrays for gene expression
+
+# 2) Variable Availability and Data Type Conversion
+#    From the sample characteristics dictionary, no row contains explicit or varying "osteoporosis" info => trait_row=None
+#    Age info is found in row 1, with multiple distinct numeric values => age_row=1
+#    Gender is "female" only, with no variation => gender_row=None
+
+trait_row = None
+age_row = 1
+gender_row = None
+
+# Define data conversion functions.
+# Even if rows are None, we still define them for completeness.
+def convert_trait(value: str):
+    # Trait data is not actually available here.
+    return None
+
+def convert_age(value: str):
+    # Example raw value: "age (years): 76"
+    # We want to extract the number part as float/int
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Only single value "female" is present => no actual variation
+    return None
+
+# 3) Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_final = False  # We are only doing initial filtering at this step
+
+is_usable = validate_and_save_cohort_info(
+    is_final=is_final,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+#    Skip because trait_row is None (trait not available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,  # assumed to be available from previous steps
+        trait,
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    print("Preview of extracted clinical features:")
+    print(preview_df(selected_clinical_df))
+
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., TC01000095.hg.1) are not recognized as standard human gene symbols.
+# They appear to be platform-specific or probe identifiers that would need mapping.
+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 corresponding to the probe IDs and gene symbols
+id_column = "ID"           # This column matches the probe identifiers from the expression data
+symbol_column = "gene_assignment"  # This column contains gene information
+
+# 2. Create the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_column, gene_col=symbol_column)
+
+# 3. Apply the mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print the resulting dataframe shape or a small preview
+print("Gene expression data shape after mapping:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoporosis/code/GSE20881.py

@@ -0,0 +1,188 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE20881"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE20881"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoporosis/GSE20881.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoporosis/gene_data/GSE20881.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoporosis/clinical_data/GSE20881.csv"
+json_path = "./output/preprocess/1/Osteoporosis/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 the dataset likely contains gene expression data
+is_gene_available = True  # Based on the description of differential intestinal gene expression
+
+# 2) Determine availability and appropriate rows for trait, age, and gender
+#    We observe that "other illnesses" (row 57) sometimes contain "Osteoporosis", so we use that as our trait row.
+trait_row = 57
+
+#    There's no direct "age" field, only birth dates, which would require multi-row parsing (birth date vs. procedure date).
+#    We won't attempt to compute age from dates here, so set None.
+age_row = None
+
+#    There's no row containing gender information, so set None.
+gender_row = None
+
+# 2.2) Define the data conversion functions for each variable.
+#      We'll parse after the colon. For osteoporosis, convert to 1 if the string matches "Osteoporosis", else 0.
+#      Age and gender are unavailable, so their converters simply return None.
+
+def convert_trait(x: str):
+    parts = x.split(":")
+    val = parts[1].strip().lower() if len(parts) > 1 else ""
+    if not val or val in ["none", "unknown"]:
+        return 0
+    # If 'osteoporosis' is found, return 1; else 0
+    return 1 if "osteoporosis" in val else 0
+
+def convert_age(x: str):
+    return None
+
+def convert_gender(x: str):
+    return None
+
+# 3) Conduct initial filtering and save metadata.
+#    If trait_row is not None, then trait data is available.
+is_trait_available = (trait_row is not None)
+
+#    Use the provided function from the library.
+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 is not None, extract clinical features, preview, and save.
+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, n=5)
+    print("Preview of Selected Clinical Features:", preview)
+
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the given identifiers, they are numeric indices rather than standard gene symbols.
+# Therefore, they likely 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) Observing the dictionary preview from the gene annotation step, the 'ID' column corresponds 
+#    to the probe identifiers that match the expression data (also indexed by 'ID'). 
+#    The 'GENE_SYMBOL' column seems to contain the gene symbols.
+
+probe_col = "ID"
+symbol_col = "GENE_SYMBOL"
+
+# 2) Extract the probe-gene mapping from the annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3) Apply the mapping to convert probe-level measurements into gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For confirmation, print the shape of the resulting dataframe and the first 5 rows.
+print("Gene data shape after mapping:", gene_data.shape)
+print(gene_data.head(5))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Osteoporosis/code/GSE224330.py

@@ -0,0 +1,218 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Osteoporosis"
+cohort = "GSE224330"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Osteoporosis"
+in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE224330"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Osteoporosis/GSE224330.csv"
+out_gene_data_file = "./output/preprocess/1/Osteoporosis/gene_data/GSE224330.csv"
+out_clinical_data_file = "./output/preprocess/1/Osteoporosis/clinical_data/GSE224330.csv"
+json_path = "./output/preprocess/1/Osteoporosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background, this dataset involves gene expression profiling
+
+# 2. Variable Availability and Data Type Conversion
+
+# According to the sample characteristics dictionary:
+#  - Trait is at row 3 (comorbidity). We will map "osteoporosis" -> 1, else 0.
+#  - Age is at row 1.
+#  - Gender is at row 2.
+
+trait_row = 3
+age_row = 1
+gender_row = 2
+
+# For each variable, define a converter function:
+
+def convert_trait(value: str) -> int:
+    """
+    Convert the comorbidity field to binary.
+    1 if "osteoporosis" is present, 0 otherwise.
+    Unknown values -> None
+    """
+    if not isinstance(value, str):
+        return None
+    # Value format might be "comorbidity: something"
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "osteoporosis":
+        return 1
+    elif val:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """
+    Convert the age field (e.g., "age: 63y") to a numeric float.
+    Unknown -> None
+    """
+    if not isinstance(value, str):
+        return None
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower().replace("y", "")
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert the gender field to binary.
+    female -> 0, male -> 1
+    Unknown -> None
+    """
+    if not isinstance(value, str):
+        return None
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (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 the extracted clinical features
+    preview_data = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_data)
+
+    # 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])
+# These identifiers (e.g., "A_19_P00315452") are microarray probe IDs, not standard human gene symbols.
+# Therefore, they need to be mapped to the corresponding 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. Decide which key in the gene annotation matches the expression IDs and which stores gene symbols.
+#    From our inspection, "ID" matches the probe identifiers seen in gene_data, and "GENE_SYMBOL" represents gene symbols.
+
+# 2. Get a gene mapping dataframe by extracting those two columns from the annotation data.
+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.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)