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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE143924"
# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE143924"
# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE143924.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE143924.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE143924.csv"
json_path = "./output/preprocess/3/Atrial_Fibrillation/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 information, this is a transcriptome analysis study from tissue biopsies
is_gene_available = True
# 2.1 Data Availability
# From sample characteristics, trait data is in row 1 (POAF vs SR)
trait_row = 1
# Age and gender are not available in the sample characteristics
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if not isinstance(value, str):
return None
value = value.split(": ")[-1].lower()
if "postoperative atrial fibrillation" in value or "poaf" in value:
return 1
elif "sinus rhythm" in value:
return 0
return None
def convert_age(value):
return None # Not used since age data not available
def convert_gender(value):
return None # Not used since gender data not available
# 3. Save Initial Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available
)
# 4. Extract Clinical Features
if trait_row is not None:
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 of processed clinical data:")
print(preview_df(clinical_df))
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# These appear to be human gene symbols - they follow standard HUGO nomenclature with accepted formats like:
# - Standard gene symbols (e.g. AACS, AADAC)
# - Antisense RNA annotations (-AS1 suffix)
# - Pseudogene annotations (P suffix)
# No mapping needed as they are already in the desired format
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
genetic_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_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="Sample size adequate. Gene expression data quality good. Trait is postoperative atrial fibrillation vs sinus rhythm."
)
# 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) |