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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE235307"
# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE235307"
# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE235307.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE235307.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE235307.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
# From background info, this is a blood gene expression study
is_gene_available = True
# 2.1 Data Row Identification
trait_row = 5 # cardiac rhythm after 1 year follow-up
age_row = 2 # age is available
gender_row = 1 # gender is available
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
"""Convert AF status to binary: 1 for AF, 0 for sinus rhythm"""
if value is None:
return None
value = value.split(': ')[-1].lower().strip()
if 'atrial fibrillation' in value:
return 1
elif 'sinus rhythm' in value:
return 0
return None
def convert_age(value: str) -> Optional[float]:
"""Convert age to float"""
if value is None:
return None
try:
return float(value.split(': ')[-1])
except:
return None
def convert_gender(value: str) -> Optional[int]:
"""Convert gender to binary: 0 for female, 1 for male"""
if value is None:
return None
value = value.split(': ')[-1].lower().strip()
if value == 'female':
return 0
elif value == 'male':
return 1
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. Clinical Feature Extraction
if trait_row is not None:
clinical_features = 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 features
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save to CSV
clinical_features.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]))
# The gene identifiers in this dataset appear to be simple numeric values,
# which are not standard human gene symbols.
# Standard gene symbols would be like "BRCA1", "TP53", etc
# Therefore mapping is required to convert these to proper gene symbols.
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)
# 1. From the preview, 'ID' contains numeric identifiers matching gene expression data,
# and 'GENE_SYMBOL' contains human gene symbols
# 2. Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
# 3. Apply mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Normalize gene symbols in the gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
# Preview the mapped gene data
print("\nFirst 20 genes after mapping:")
print(list(gene_data.index[:20]))
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, 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="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
)
# 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) |