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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE140161"
# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE140161"
# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE140161.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE140161.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE140161.csv"
json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 Availability
# Yes - Series_overall_design indicates Affymetrix chip was used for whole blood transcriptome
is_gene_available = True
# 2.1 Data Availability
# Disease state is constant "Sjögren's syndrome", not usable
trait_row = None
# Gender is available in row 1
gender_row = 1
# Age is not available
age_row = None
# 2.2 Data Type Conversion
def convert_trait(x):
# Not used since trait_row is None
return None
def convert_gender(x):
if not isinstance(x, str):
return None
value = x.split(': ')[1].lower() if ': ' in x else x.lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
def convert_age(x):
# Not used since age_row is None
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 skipped since trait_row is None
# 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)
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 IDs and gene symbols from annotation data
def get_gene_name(text):
"""Extract gene symbol from RefSeq annotation text"""
if not isinstance(text, str):
return None
# Look for gene symbols after RefSeq
match = re.search(r'RefSeq // Homo sapiens .+?\(([A-Z0-9]+)\)', text)
if match:
return match.group(1)
# Also try looking for gene symbols after HGNC Symbol tag
match = re.search(r'\[Source:HGNC Symbol;Acc:HGNC:\d+\] // ([A-Z0-9]+)', text)
if match:
return match.group(1)
return None
# Create mapping dataframe
mapping_data = pd.DataFrame({
'ID': gene_metadata['ID'],
'Gene': gene_metadata['SPOT_ID.1'].apply(get_gene_name)
})
# Map probes to genes and combine expression values
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Preview result
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# Save normalized gene data for future use
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Create minimal clinical features with constant trait
clinical_features = pd.DataFrame({'Sjogrens': 1}, index=gene_data.columns)
# Link data and check bias
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
linked_data = handle_missing_values(linked_data, 'Sjogrens')
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Sjogrens')
# 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=trait_biased,
df=linked_data,
note="Dataset contains gene expression data but all samples are Sjögren's syndrome cases."
)
# Save if usable (won't be in this case due to constant trait)
if is_usable:
linked_data.to_csv(out_data_file) |