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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Glucocorticoid_Sensitivity"
cohort = "GSE65645"
# Input paths
in_trait_dir = "../DATA/GEO/Glucocorticoid_Sensitivity"
in_cohort_dir = "../DATA/GEO/Glucocorticoid_Sensitivity/GSE65645"
# Output paths
out_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/GSE65645.csv"
out_gene_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/gene_data/GSE65645.csv"
out_clinical_data_file = "./output/preprocess/3/Glucocorticoid_Sensitivity/clinical_data/GSE65645.csv"
json_path = "./output/preprocess/3/Glucocorticoid_Sensitivity/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Gene Expression Data Availability
# Based on background info, this is an lncRNA microarray study
is_gene_available = True # While it's lncRNA, it's still gene expression data
# 2.1 Data Availability
# From sample characteristics:
# translocation types can be mapped to GC sensitivity based on prior knowledge
trait_row = 1 # translocation row
age_row = None # age not available
gender_row = None # gender not available
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert translocation type to GC sensitivity (binary).
TEL-AML1: GC sensitive (0)
E2A-PBX1: GC sensitive (0)
MLL: GC resistant (1)
"""
if not isinstance(value, str):
return None
value = value.split(': ')[-1].strip()
if value == 'MLL':
return 1
elif value in ['TEL_AML1', 'E2A_PBX1']:
return 0
return None
def convert_age(value):
return None # Not used
def convert_gender(value):
return None # Not used
# 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:
clinical_features = 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 the processed clinical data
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save clinical features
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These IDs appear to be Agilent microarray probe IDs (starting with 'A_19_P')
# and other control probes, not standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
print(f"\n{col}:")
print(values)
# Get probe-to-gene mapping from annotation data
# ID column contains probe IDs matching gene expression data
# GENE_SYMBOL column contains gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
# Apply mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Check dimension of resulting data
print("\nDimensions of gene expression data:")
print(f"Number of genes: {len(gene_data)}")
print(f"Number of samples: {len(gene_data.columns)}")
# Preview first few genes and samples
print("\nPreview of gene expression data:")
preview_dict = preview_df(gene_data)
print(preview_dict)
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# Validate data quality and save cohort info
note = "Gene expression data from glucocorticoid sensitivity study."
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=is_biased,
df=linked_data,
note=note
)
# Save linked data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file)
else:
print(f"Dataset {cohort} did not pass quality validation and will not be saved.") |