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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Melanoma"
cohort = "GSE148319"
# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE148319"
# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE148319.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE148319.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE148319.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene expression data availability
# Based on background info, this is a study with gene expression data from tumor xenografts
is_gene_available = True
# 2.1 Data availability
# For trait - can be inferred from cell line info in row 8
trait_row = 8
# Age & gender not recorded in the sample characteristics
age_row = None
gender_row = None
# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
"""Convert cell line info to binary melanoma status"""
if "melanoma" in value.lower():
return 1
elif "oral carcinoma" in value.lower():
return 0
return None
def convert_age(value: str) -> float:
return None
def convert_gender(value: str) -> int:
return None
# 3. Save metadata
_ = validate_and_save_cohort_info(is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=trait_row is not None)
# 4. Extract clinical features since 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 processed clinical data
print(preview_df(selected_clinical_df))
# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs to examine data type
print("First 20 row IDs:")
print(list(genetic_data.index)[:20])
# After examining the IDs and confirming this is gene expression data:
is_gene_available = True
# Save updated metadata
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=(trait_row is not None)
)
genetic_data.to_csv(out_gene_data_file)
# The pattern of identifiers shows these are probe IDs from an Affymetrix microarray platform
# (format: number_at, number_s_at, number_x_at)
# They need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Extract gene mapping from annotation data
# ID column contains probe IDs that match gene expression data
# Gene Symbol column contains standardized human gene symbols
mapping_df = get_gene_mapping(gene_metadata, "ID", "Gene Symbol")
# Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=True,
is_biased=trait_biased,
df=linked_data,
note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
)
# 6. Save linked data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |