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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE159472"
# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE159472"
# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE159472.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE159472.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE159472.csv"
json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"
# Get file paths for soft and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each clinical feature row
clinical_features = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Availability
# Based on background info and series title, this is a microarray expression data for DLBCL
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Row Numbers
# Trait (ABC/GCB subtypes) is in row 2
trait_row = 2
# Age and gender not available in characteristics
age_row = None
gender_row = None
# 2.2 Conversion Functions
def convert_trait(x):
"""Convert DLBCL subtype to binary: ABC=1, GCB=0"""
try:
if not isinstance(x, str):
return None
x = x.split(': ')[1].strip()
if 'ABC' in x:
return 1
elif 'GCB' in x:
return 0
return None
except:
return None
def convert_age(x):
return None
def convert_gender(x):
return None
# 3. Save initial 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. Extract clinical features since trait data is available
if trait_row is not None:
clinical_features = 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
)
# Preview the extracted features
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)
# Print DataFrame info and dimensions to verify data structure
print("DataFrame info:")
print(genetic_data.info())
print("\nDataFrame dimensions:", genetic_data.shape)
# Print an excerpt of the data to inspect row/column structure
print("\nFirst few rows and columns of data:")
print(genetic_data.head().iloc[:, :5])
# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Review gene identifiers - these appear to be Affymetrix probe IDs (e.g. "1007_s_at")
# rather than standard human gene symbols, so mapping will be required
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)
# Print information about annotation data
print("Gene Annotation Preview:")
print("\nDataFrame Shape:", gene_annotation.shape)
print("\nColumn Names:")
print(gene_annotation.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_annotation))
# Get mapping between gene IDs and gene symbols from annotation data
# 'ID' column matches probe IDs in expression data, 'Gene Symbol' contains human gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Print info about the resulting gene expression data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few mapped genes and their expression values:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# Debug print to check data before handling missing values
print("\nPreview of linked data before handling missing values:")
print(linked_data.head())
# 3. Handle missing values
linked_data = handle_missing_values(df=linked_data, trait_col=trait)
print("\nPreview of linked data after handling missing values:")
print(linked_data.head())
# 4. Check for biases and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate dataset quality and save metadata
note = ""
if is_biased:
note = "The trait distribution is severely biased."
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
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |