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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Metabolic_Rate"
cohort = "GSE106800"
# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE106800"
# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE106800.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE106800.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE106800.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
# Background info mentions microarray analysis on muscle biopsies, so gene data likely exists
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# Metabolic rate (trait) can be inferred from fasting glucose, insulin & FFA measurements in rows 8,9,10
# Age data is in row 2, Gender in row 0
trait_row = 9 # Using insulin level as proxy for metabolic rate since title mentions insulin resistance
age_row = 2
gender_row = 0
def convert_trait(value):
# Extract numeric insulin value after colon as continuous trait
try:
return float(value.split(': ')[1])
except:
return None
def convert_age(value):
# Extract numeric age value after colon
try:
return float(value.split(': ')[1])
except:
return None
def convert_gender(value):
# Convert gender to binary (female=0, male=1)
try:
gender = value.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. Clinical Feature Extraction
if trait_row is not None:
selected_clinical = geo_select_clinical_features(
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
)
print("Preview of selected clinical features:")
print(preview_df(selected_clinical))
# Save clinical data
selected_clinical.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])
# Based on the gene identifiers shown (e.g., '16650001', '16650003', etc.),
# these appear to be probe IDs from a microarray platform rather than human gene symbols.
# They need to be mapped to standard gene symbols for analysis.
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))
# From inspection, 'ID' stores gene identifiers matching those in expression data,
# and 'gene_assignment' contains gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# Apply mapping to convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview results
print("Gene expression data shape:", gene_data.shape)
print("\nFirst 5 gene symbols:", gene_data.index[:5])
print("\nPreview of expression values:")
print(gene_data.head())
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and information saving
note = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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=trait_biased,
df=linked_data,
note=note
)
# 6. Save linked data only if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |