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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Glioblastoma"
cohort = "GSE39144"
# Input paths
in_trait_dir = "../DATA/GEO/Glioblastoma"
in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE39144"
# Output paths
out_data_file = "./output/preprocess/3/Glioblastoma/GSE39144.csv"
out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE39144.csv"
out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE39144.csv"
json_path = "./output/preprocess/3/Glioblastoma/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
# GeneChip Human Genome U133 Plus 2.0 Array was used, indicating gene expression data
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# For trait: Feature 0 contains "cell type: glioma-initiating cells" which indicates glioblastoma samples
trait_row = 0
# Age: No age information available in sample characteristics
age_row = None
# Gender: Feature 2 contains gender information
gender_row = 2
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
# Convert glioma vs non-glioma
if "glioma-initiating cells" in value.lower():
return 1
return 0
def convert_age(value: str) -> float:
# Not used but defined for completeness
return None
def convert_gender(value: str) -> int:
# Extract value after colon and convert to binary
if ":" in value:
gender = value.split(":")[1].strip().lower()
if gender == "female":
return 0
elif gender == "male":
return 1
elif "pooled female" in gender:
return 0
elif "pooled male" in gender:
return 1
return None
# 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=(trait_row is not None)
)
# 4. Clinical Feature Extraction
# Extract clinical features since trait_row is not None
clinical_df = geo_select_clinical_features(
clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
age_row=age_row,
convert_age=convert_age,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the extracted features
preview_result = preview_df(clinical_df)
print("Preview of clinical features:")
print(preview_result)
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# 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)
# Examine the gene identifiers - these are Affymetrix probe IDs that require mapping to gene symbols
# Format: XXXXX_at, XXXXX_s_at, XXXXX_x_at etc.
# These are probe set IDs from Affymetrix arrays, not gene symbols
requires_gene_mapping = True
# Get file paths using library function
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 gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 1. Identify relevant columns from gene annotation
# 'ID' column matches probe IDs in gene expression data
# 'Gene Symbol' column contains the gene symbols
prob_col = 'ID'
gene_col = 'Gene Symbol'
# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
# 3. Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)
# Preview result
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save cohort 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=is_biased,
df=linked_data,
note="Gene expression data from hiPSCs, hESCs and differentiated cells, including glioblastoma cells"
)
# 6. Save linked data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |