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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Eczema"
cohort = "GSE61225"
# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE61225"
# Output paths
out_data_file = "./output/preprocess/3/Eczema/GSE61225.csv"
out_gene_data_file = "./output/preprocess/3/Eczema/gene_data/GSE61225.csv"
out_clinical_data_file = "./output/preprocess/3/Eczema/clinical_data/GSE61225.csv"
json_path = "./output/preprocess/3/Eczema/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
# Based on the background information, this is a gene expression study using Illumina BeadChip
is_gene_available = True
# 2.1 Data Availability
# trait_row is None since this dataset is about swimming exposure, not eczema
trait_row = None
# age and gender data are available in rows 6 and 5 respectively
age_row = 6
gender_row = 5
# 2.2 Data Type Conversion Functions
def convert_trait(x):
return None # Not applicable since trait data not available
def convert_age(x):
try:
# Extract numeric value after colon and convert to float
return float(x.split(': ')[1])
except:
return None
def convert_gender(x):
try:
# Extract value after colon and convert to binary
gender = x.split(': ')[1].lower()
if gender == 'female':
return 0
elif gender == 'male':
return 1
return None
except:
return None
# 3. Save Metadata
# Initial filtering - trait data is not available
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)
# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])
print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# The identifiers starting with "ILMN_" are Illumina probe IDs, which need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file)
# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. In gene expression data, identifiers start with 'ILMN_', and in gene annotation data,
# 'ID' column stores same format IDs and 'UCSC_GENES' column stores gene symbols
probe_col = 'ID'
gene_col = 'UCSC_GENES'
# 2. Extract probe-gene mapping from annotation data
mapping_df = get_gene_mapping(gene_metadata, probe_col, gene_col)
# 3. Convert probe measurements to gene expression data using the mapping
gene_data = apply_gene_mapping(genetic_df, mapping_df)
# Preview result
print("Gene expression data shape:", gene_data.shape)
print("\nFirst 5 genes and their first 5 measurements:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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)
# 2-6. Since this is not an Eczema study, mark it as non-available in initial filtering
validate_and_save_cohort_info(
is_final=False, # Initial filtering since trait data is not available
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False
) |