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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Prostate_Cancer"
cohort = "GSE235003"
# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE235003"
# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE235003.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE235003.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE235003.csv"
json_path = "./output/preprocess/3/Prostate_Cancer/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
is_gene_available = True # Series contains gene expression data studying OC2's effect
# 2.1 Data Availability
trait_row = None # All samples are prostate cancer cell lines, no control group
age_row = None # Not applicable for cell lines
gender_row = None # Not applicable for cell lines
# 2.2 Data Type Conversion Functions
def convert_trait(x):
return 1 if 'Prostate cancer' in x else 0
def convert_age(x):
return None # Not used
def convert_gender(x):
return None # Not used
# 3. Save 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 # trait_row is None
)
# 4. Skip clinical feature extraction 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)
# The gene identifiers appear to be just numeric indices (4, 5, 6, etc)
# not gene symbols or other interpretable identifiers
# This data will require mapping to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
sample_values = gene_metadata[col].dropna().head().tolist()
print(f"\n{col}:")
print(sample_values)
# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
header = []
for i, line in enumerate(f):
header.append(line.strip())
if i >= 10: # Preview first 10 lines
break
print('\n'.join(header))
# 1. Based on observation:
# - In expression data, identifiers are numeric IDs starting from 4,5,6...
# - In annotation data, 'ID' column contains numeric strings matching these identifiers
# - 'GENE_SYMBOL' column contains human gene symbols we want to map to
# 2. Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
# 3. Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Print shape and preview to verify the mapping
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows after mapping to genes:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2-6. Record that this dataset is not usable due to missing clinical data
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, # Set to True since no clinical data makes it unusable
df=gene_data, # Pass the gene expression data
note="Contains normalized gene expression data but no clinical features for trait analysis."
) |