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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Metabolic_Rate"
cohort = "GSE41168"
# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE41168"
# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE41168.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE41168.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE41168.csv"
json_path = "./output/preprocess/3/Metabolic_Rate/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
is_gene_available = True # The background indicates this is a gene expression study involving muscle and adipose tissue
# 2.1 Data Availability
trait_row = None # Metabolic rate data is described in background but not given in characteristics
age_row = None # Age is not available in characteristics
gender_row = 3 # Gender information is in row 3
# 2.2 Data Type Conversion Functions
def convert_trait(x):
return None # Not used since trait data not available
def convert_age(x):
return None # Not used since age data not available
def convert_gender(x):
if not isinstance(x, str):
return None
x = x.lower().split(': ')[-1].strip()
if 'female' in x:
return 0
elif 'male' in x:
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. Clinical Feature Extraction
# Skip since trait_row is None
# 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])
# These are probe IDs from Affymetrix arrays (_at suffix is typical for Affy probes)
# They need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Convert probe-level measurements to gene expression data using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_data)
print("Gene data shape:", gene_data.shape)
print("\nPreview of gene data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)
# Create a simple dataframe just for validation since no trait data available
df = pd.DataFrame({'no_trait': [0]})
# Since clinical data was not available (trait_row was None), mark dataset as unusable
note = "Contains gene expression data but no metabolic rate measurements"
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, # Set to True since dataset lacks trait data
df=df,
note=note
)
# No linked data saved since trait data was unavailable |