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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Sarcoma"
cohort = "GSE197147"
# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE197147"
# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE197147.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE197147.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE197147.csv"
json_path = "./output/preprocess/3/Sarcoma/cohort_info.json"
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene Expression Data Availability
# Based on background info mentioning "Gene expression profiling was performed",
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Extract from row 0, where histotype indicates tumor type
trait_row = 0
# Age and gender not available in sample characteristics
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert histotype value to binary indicating if it's Sarcoma (RMS)"""
# Extract value after colon and strip whitespace
if ':' in value:
value = value.split(':')[1].strip()
# RMS (Rhabdomyosarcoma) is a type of sarcoma
return 1 if value == 'RMS' else 0
convert_age = None
convert_gender = None
# 3. Save Metadata
# trait_row is not None, so trait data is available
validate_and_save_cohort_info(
is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=True
)
# 4. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
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 and save clinical data
print("Clinical data preview:")
print(preview_df(clinical_df))
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# The identifiers appear to be from a microarray platform (TC* format)
# and not standard human gene symbols, so they need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Preview the annotation data structure and check usability
print("Column names:")
print(gene_annotation.columns)
print("\nFirst few rows preview:")
print(preview_df(gene_annotation))
# The annotation data lacks a clear mapping between probe IDs and gene symbols
# SPOT_ID.1 contains gene info but in a complex format with multiple transcript records
# This makes reliable gene symbol mapping impossible
print("\nWarning: Gene annotation structure not suitable for probe-to-gene symbol mapping")
gene_annotation = None
# Since we can't properly annotate genes for human analysis,
# update metadata to indicate gene data is not available
is_gene_available = False
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
) |