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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE190042"
# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE190042"
# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE190042.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE190042.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE190042.csv"
json_path = "./output/preprocess/3/Cardiovascular_Disease/cohort_info.json"
# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each feature (row) in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Yes - Dataset uses Affymetrix PrimeView array for transcriptome profiling
is_gene_available = True
# 2.1 Data Availability
# Row 7 has mixed data including treatment response and chromosome info
# Not suitable for consistent trait analysis
trait_row = None
# Age data available in row 2
age_row = 2
# Gender data available in row 1
gender_row = 1
# 2.2 Data Type Conversion Functions
def convert_trait(x):
return None # Not used since trait_row is None
def convert_age(x):
try:
# Extract age value after colon and convert to float
age = float(x.split(': ')[1])
return age
except:
return None
def convert_gender(x):
try:
gender = x.split(': ')[1].strip().upper()
if gender == 'F':
return 0
elif gender == 'M':
return 1
return None
except:
return None
# 3. Save initial metadata
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=False
)
# 4. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)
# Preview the DataFrame structure
print("DataFrame shape:", genetic_df.shape)
print("\nFirst few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Print first few lines from the matrix file to inspect format
print("\nRaw file preview:")
with gzip.open(matrix_file, 'rt') as f:
for i, line in enumerate(f):
if i > 30 and i < 35: # Print a few lines around where data starts
print(line.strip())
# Looking at the identifiers like "11715100_at", "11715101_s_at", these are probe IDs from an Affymetrix microarray
# They need to be mapped to human gene symbols for consistency and interpretability
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Preview column names and first few values
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# Get gene mapping (probe ID -> gene symbol)
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Apply mapping to convert probe measurements to gene expression
gene_data = apply_gene_mapping(genetic_df, mapping_df)
# Save gene expression data to file
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
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)
# Since trait data is missing, skip clinical data preprocessing
# and just record that this dataset cannot be used for association studies
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False
) |