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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Arrhythmia"
cohort = "GSE41177"
# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE41177"
# Output paths
out_data_file = "./output/preprocess/3/Arrhythmia/GSE41177.csv"
out_gene_data_file = "./output/preprocess/3/Arrhythmia/gene_data/GSE41177.csv"
out_clinical_data_file = "./output/preprocess/3/Arrhythmia/clinical_data/GSE41177.csv"
json_path = "./output/preprocess/3/Arrhythmia/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)
# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")
# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
print(f"Feature: {feature}")
print(f"Values: {values}\n")
# 1. Gene Expression Data
is_gene_available = True # Dataset contains microarray gene expression data
# 2. Variable Availability and Conversion Functions
def convert_trait(value):
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 'left atrial appendage' in value:
return 0 # LAA
elif 'left atrial junction' in value:
return 1 # LA-PV junction
return None
def convert_age(value):
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 'y' in value:
try:
return float(value.replace('y',''))
except:
return None
return None
def convert_gender(value):
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 'female' in value:
return 0
elif 'male' in value:
return 1
return None
# Row identifiers for each variable
trait_row = 0 # organ location
age_row = 2 # age in years
gender_row = 1 # gender
# 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. Extract clinical features
if trait_row is not None:
selected_clinical = 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 processed clinical data:")
print(preview_df(selected_clinical))
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])
# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
lines = []
for i, line in enumerate(f):
if "!series_matrix_table_begin" in line:
# Get the next 5 lines after the marker
for _ in range(5):
lines.append(next(f).strip())
break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
print(line)
# Based on identifiers like "1007_s_at", "1053_at", etc., these appear to be Affymetrix probe IDs
# rather than human gene symbols and will need to be mapped
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)
# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Preview the processed gene data
print("Gene Expression Data Shape:", gene_data.shape)
print("\nPreview of gene expression data:")
print(preview_df(gene_data))
# 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
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save cohort info
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
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |