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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Mesothelioma"
cohort = "GSE131027"
# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE131027"
# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE131027.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE131027.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE131027.csv"
json_path = "./output/preprocess/3/Mesothelioma/cohort_info.json"
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene Expression Data Availability
is_gene_available = False # This dataset appears to focus on genetic mutations/variants rather than gene expression
# 2. Clinical Data Availability and Conversion Functions
# 2.1 Row numbers for data extraction
trait_row = 1 # Cancer type information is in row 1
age_row = None # Age information not available
gender_row = None # Gender information not available
# 2.2 Data Type Conversion Functions
def convert_trait(x):
"""Convert cancer type to binary for Mesothelioma"""
if pd.isna(x):
return None
if isinstance(x, str):
x = x.split(": ")[-1] # Get value after colon
if x == "Mesothelioma":
return 1
else:
return 0
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
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
)
# Preview the processed data
print("Preview of processed clinical data:")
print(preview_df(selected_clinical_df))
# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The IDs like "1007_s_at", "1053_at" etc. appear to be Affymetrix probe IDs
# These need to be mapped to standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping using probe ID and gene symbol columns
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
# Convert probe-level measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview the converted data
print("Preview of mapped gene expression data:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and information saving
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=True,
is_biased=trait_biased,
df=linked_data,
note=""
)
# 6. Save linked data only if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |