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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Stomach_Cancer"
cohort = "GSE172197"
# Input paths
in_trait_dir = "../DATA/GEO/Stomach_Cancer"
in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE172197"
# Output paths
out_data_file = "./output/preprocess/3/Stomach_Cancer/GSE172197.csv"
out_gene_data_file = "./output/preprocess/3/Stomach_Cancer/gene_data/GSE172197.csv"
out_clinical_data_file = "./output/preprocess/3/Stomach_Cancer/clinical_data/GSE172197.csv"
json_path = "./output/preprocess/3/Stomach_Cancer/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, this dataset contains mRNA expression profiles, so it's suitable
is_gene_available = True
# 2.1 Data Availability
# From the sample characteristics, we don't have trait (cancer/normal), age or gender data
# All samples are cancer cell lines, so trait data has only one value (constant)
trait_row = None
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
# Not used since trait_row is None
return None
def convert_age(value):
# Not used since age_row is None
return None
def convert_gender(value):
# Not used since gender_row is None
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. Clinical Feature Extraction
# Skip since trait_row is None, indicating no clinical data available
# 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])
# Looking at the identifiers, these are Affymetrix probe IDs (e.g. '1007_s_at', '1053_at')
# and need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# Get gene mapping dataframe from gene annotation data
# The column 'ID' stores probe IDs which match the gene expression data indices
# The column 'Gene Symbol' stores human gene symbols
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
# Convert probe-level expression data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview result
print("First few rows and columns of mapped gene expression data:")
print(gene_data.iloc[:5, :5])
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)
# 2-6. Skip clinical data linking and further processing since we determined there is no usable clinical data
# Create a minimal DataFrame with one column to satisfy the validation function requirements
df = pd.DataFrame(index=gene_data.columns, columns=['dummy'])
# We consider this dataset biased since it only contains cancer cell lines
note = "Dataset contains only cancer cell lines. While gene expression data was successfully processed, no trait comparison is possible due to lack of normal samples."
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,
df=df,
note=note
) |