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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "COVID-19"
cohort = "GSE227080"
# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE227080"
# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE227080.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE227080.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE227080.csv"
json_path = "./output/preprocess/3/COVID-19/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
is_gene_available = True # Yes, contains immunological gene expression data from NanoString nCounter
# 2.1 Data Availability
trait_row = 2 # Severity information
age_row = 1 # Age information
gender_row = 0 # Gender information
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert COVID-19 severity to binary: 1 for positive cases (MILD or MOD_SEV), 0 for negative"""
if not value or ':' not in value:
return None
severity = value.split(':')[1].strip().upper()
if severity == 'NEG':
return 0
elif severity in ['MILD', 'MOD_SEV']:
return 1
return None
def convert_age(value: str) -> float:
"""Convert age string to float"""
if not value or ':' not in value:
return None
try:
return float(value.split(':')[1].strip())
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary: 0 for female, 1 for male"""
if not value or ':' not in value:
return None
gender = value.split(':')[1].strip().upper()
if gender == 'F':
return 0
elif gender == 'M':
return 1
return None
# 3. Save 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. Clinical Feature Extraction
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
age_row, convert_age,
gender_row, convert_gender)
# Preview the processed clinical data
preview_df(clinical_df)
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)
# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
# Verify this is gene expression data and check identifiers
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)
)
# Save gene expression data
genetic_data.to_csv(out_gene_data_file)
# The identifiers in the index appear to be standard human gene symbols (e.g. ABCB1, ABL1, ADA)
# so no mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data.T)
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="Dataset contains immunological gene expression data from 60 COVID-19 positive cases (mild and moderate/severe) and 59 COVID-negative controls."
)
# 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) |