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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Stroke"
cohort = "GSE37587"
# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE37587"
# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE37587.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE37587.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE37587.csv"
json_path = "./output/preprocess/3/Stroke/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# 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
# Yes, this dataset contains gene expression data from peripheral blood samples
is_gene_available = True
# 2.1 Data Availability
# Trait data is in Feature 6 "disease state"
trait_row = 6
# Age data is in Feature 0
age_row = 0
# Gender data is in Feature 4
gender_row = 4
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# Binary: 1 for stroke, 0 for control
# But all samples are stroke cases, so this is not useful
return None
def convert_age(x):
# Continuous
try:
return int(x.split(': ')[1])
except:
pass
return None
def convert_gender(x):
# Binary: 0 for female, 1 for male
try:
gender = x.split(': ')[1].lower()
if gender == 'female':
return 0
elif gender == 'male':
return 1
except:
pass
return None
# 3. Save Metadata
# Initial filtering - trait data not usable since all samples have stroke
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. Clinical Feature Extraction skipped since trait data not usable
# 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)
# The identifiers starting with ILMN_ indicate these are Illumina probe IDs
# rather than standard human gene symbols. These need to be mapped.
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)
# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
# Apply gene mapping to get gene expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Create a dummy DataFrame with the same size as gene expression data
# All samples are stroke cases (value 1)
dummy_clinical = pd.DataFrame({'Stroke': [1]*gene_data.shape[1],
'Age': gene_data.iloc[0].values, # Use first row to match size
'Gender': gene_data.iloc[1].values}, # Use second row to match size
index=gene_data.columns)
dummy_data = geo_link_clinical_genetic_data(dummy_clinical, gene_data)
# Evaluate bias - will be biased since all samples are stroke cases
is_biased, dummy_data = judge_and_remove_biased_features(dummy_data, 'Stroke')
# Save cohort info indicating severe bias
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=True,
df=dummy_data,
note="Study examining transcriptome profiles from peripheral blood of stroke patients. Not usable for trait analysis since all samples are stroke cases."
)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2-6. Record dataset as not usable for trait analysis
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=True,
df=gene_data,
note="Study examining transcriptome profiles from peripheral blood of stroke patients. Not usable for trait analysis since all samples are stroke cases."
) |