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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Amyotrophic_Lateral_Sclerosis"
cohort = "GSE212134"
# Input paths
in_trait_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis"
in_cohort_dir = "../DATA/GEO/Amyotrophic_Lateral_Sclerosis/GSE212134"
# Output paths
out_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/GSE212134.csv"
out_gene_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/gene_data/GSE212134.csv"
out_clinical_data_file = "./output/preprocess/3/Amyotrophic_Lateral_Sclerosis/clinical_data/GSE212134.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
is_gene_available = True # Dataset mentions mRNA, indicating gene expression data
# 2.1 Variable Keys
trait_row = None # Cannot find any disease status indication in sample characteristics
age_row = None # Age data not available in sample characteristics
gender_row = 0 # Gender data is in first row (index 0)
# 2.2 Data Type Conversion Functions
def convert_trait(value):
return None # No trait data available
def convert_age(value):
return None # No age data available
def convert_gender(value):
if not isinstance(value, str):
return None
value = value.lower().split(': ')[-1]
if 'female' in value:
return 0
elif 'male' in value:
return 1
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. Skip clinical feature extraction since trait_row is None
# 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)
# These IDs are numeric probe IDs from a microarray platform, not gene symbols
# They need to be mapped to human gene symbols for meaningful analysis
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")
# Step 1: 'ID' column in annotation matches numeric probe IDs in expression data
# For gene symbols, need to extract from 'gene_assignment' which has format:
# "NR_024005 // DDX11L2 // description // location // ID"
# Extract gene symbols from gene_assignment column
def extract_gene_symbol(assignment):
if pd.isna(assignment) or assignment == '---':
return None
# Split by // and take the second item which is the gene symbol
parts = assignment.split('//')
if len(parts) >= 2:
return parts[1].strip()
return None
# Add Symbol column with extracted gene symbols
gene_annotation['Symbol'] = gene_annotation['gene_assignment'].apply(extract_gene_symbol)
# Step 2: Create mapping dataframe with probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')
# Step 3: Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Preview the mapped gene data
print("Gene expression data after mapping:")
print("Shape:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save normalized gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 2. Since no trait data is available (trait_row was None), validate and mark dataset as unusable
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, # Dataset without trait data is biased/unusable
df=gene_data,
note="Gene expression data successfully processed but no trait information available for analysis"
) |