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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE52937"
# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE52937"
# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE52937.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE52937.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE52937.csv"
json_path = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/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
# This is likely a gene expression dataset based on series title and design
# investigating genetic factors in ALS, not miRNA or methylation
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# No trait data comparing ALS vs controls - just cell lines with SETX knockdown
trait_row = None # No ALS trait data
age_row = None # Age not available
gender_row = None # Gender not available
def convert_trait(x):
return None
def convert_age(x):
return None
def convert_gender(x):
return None
# 3. Save Metadata
# Initial filtering - trait_row is None so trait data not available
is_trait_available = trait_row is not None
validate_and_save_cohort_info(False, cohort, json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available)
# 4. Clinical Feature Extraction
# Skip since trait_row is None and clinical data not available
# 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)
# Observe gene identifiers starting with 'ILMN_', which indicates Illumina probe IDs
# These are not human gene symbols and need to be mapped
requires_gene_mapping = True
# Extract gene annotation from SOFT file and get meaningful data
gene_annotation = get_gene_annotation(soft_file)
# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))
print("\nNumber of non-null values in each column:")
print(gene_annotation.count())
print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers")
print("'Symbol' column: Gene name mapping")
# 1. Extract mapping columns from gene annotation data
prob_col = 'ID' # Column with Illumina probe IDs (ILMN_*)
gene_col = 'Symbol' # Column with gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
# 2. Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 3. Normalize gene symbols to ensure consistency
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save gene data
gene_data.to_csv(out_gene_data_file)
# Since trait data is not available (trait_row=None from Step 2), skip data linking
# Only need to save normalized gene data and validate cohort info
# Save normalized gene data
gene_data.to_csv(out_gene_data_file)
# Validate and save cohort info, indicating trait data not available
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False, # No trait data available
is_biased=True, # Set to True since dataset lacks trait data
df=gene_data, # Pass gene expression data
note="Dataset contains gene expression data but lacks trait information"
) |