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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Osteoporosis"
cohort = "GSE80614"
# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE80614"
# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE80614.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE80614.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE80614.csv"
json_path = "./output/preprocess/3/Osteoporosis/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
# The background info mentions "microarray expression profiling" indicating gene expression data
is_gene_available = True
# 2.1 Data Availability
# Trait data not directly available since this is a control/case study comparing
# osteogenic vs adipogenic differentiation, not diseased vs healthy samples
trait_row = None
# Age data available in key 1
age_row = 1
# Gender data available in key 0
gender_row = 0
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# Not needed since trait data not available
return None
def convert_age(x):
# Extract number from string like "age: 19 years" or "age: 19"
try:
return float(x.split(': ')[1].split(' ')[0])
except:
return None
def convert_gender(x):
# Convert gender to binary (female=0, male=1)
try:
gender = x.split(': ')[1].lower()
if gender == 'male':
return 1
elif gender == 'female':
return 0
return None
except:
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. Skip clinical feature extraction 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])
# The gene identifiers start with "ILMN_" indicating these are Illumina probe IDs, not standard 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))
# 1. Identify the relevant columns for mapping
# 'ID' in gene annotation matches the probe IDs (ILMN_*) from gene expression data
# 'Symbol' contains the corresponding gene symbols
prob_col = 'ID'
gene_col = 'Symbol'
# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview the results
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)
note = "Gene expression data available but no clinical variables for association studies"
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, # Set to True since no clinical data makes it unusable
df=genetic_data, # Pass the gene expression data
note=note
) |