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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Psoriasis"
cohort = "GSE226244"
# Input paths
in_trait_dir = "../DATA/GEO/Psoriasis"
in_cohort_dir = "../DATA/GEO/Psoriasis/GSE226244"
# Output paths
out_data_file = "./output/preprocess/3/Psoriasis/GSE226244.csv"
out_gene_data_file = "./output/preprocess/3/Psoriasis/gene_data/GSE226244.csv"
out_clinical_data_file = "./output/preprocess/3/Psoriasis/clinical_data/GSE226244.csv"
json_path = "./output/preprocess/3/Psoriasis/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
matrix_file,
prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)
# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)
# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")
# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
print(f"Feature: {feature}")
print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Based on the background info mentioning "microarray analysis", this dataset contains gene expression data
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# For trait (psoriasis status):
# Feature 0 contains disease state info
trait_row = 0
def convert_trait(x):
if not isinstance(x, str):
return None
val = x.split(': ')[-1].strip()
if val == 'Psoriasis':
return 1
elif val == 'Control':
return 0
return None
# Age and gender not available in sample characteristics
age_row = None
gender_row = None
def convert_age(x):
return None
def convert_gender(x):
return None
# 3. Save metadata
# Initial filtering based on gene and trait availability
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
# Since trait_row is not None, extract clinical features
clinical_df = geo_select_clinical_features(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_df))
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])
# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
lines = []
for i, line in enumerate(f):
if "!series_matrix_table_begin" in line:
# Get the next 5 lines after the marker
for _ in range(5):
lines.append(next(f).strip())
break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
print(line)
# Based on the format of gene IDs (e.g., '1007_s_at', '1053_at', '117_at'), these appear to be
# Affymetrix probe IDs rather than standard human gene symbols.
# They will need to be mapped to gene symbols for downstream analysis.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Preview the annotation data
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract gene mapping data
# 'ID' column in the gene annotation contains probe IDs matching gene expression data
# 'Gene Symbol' column contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Map probe-level data to gene expressions
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 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)
# 2-6. Handle clinical data, linking and saving
if clinical_df is not None and not clinical_df.empty:
# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# Check for bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# Validate and save info
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=True,
is_biased=is_biased,
df=linked_data
)
# Save if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file)
else:
# Record that clinical data is not available
validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=None,
df=gene_data,
note="Contains gene expression data but lacks valid clinical information needed for trait association studies."
) |