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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "COVID-19"
cohort = "GSE211378"
# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE211378"
# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE211378.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE211378.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE211378.csv"
json_path = "./output/preprocess/3/COVID-19/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 from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on series summary mentioning "Whole Blood profiling", gene expression data should be available
is_gene_available = True
# 2.1 Data Availability
# Based on series design describing COVID convalescent vs Healthy donors
trait_row = 12 # nanostring_id contains trait info
age_row = None # No age data available
gender_row = None # No gender data available
# 2.2 Data Type Conversion Functions
def convert_trait(value):
"""Convert COVID-19 status to binary (0: healthy, 1: COVID convalescent)"""
if not value or ':' not in value:
return None
id_str = value.split(':')[1].strip()
# From series design, ID format suggests trait info
if '_' in id_str:
return 1 # COVID convalescent
else:
return 0 # Healthy
def convert_age(value):
"""Not needed as age data is not available"""
return None
def convert_gender(value):
"""Not needed as gender data is not available"""
return None
# 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. Clinical Feature Extraction
if trait_row is not None:
clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait)
print("Preview of extracted clinical features:")
print(preview_df(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 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index)[:20])
# These look like official human gene symbols (HGNC approved symbols)
# Examples:
# ACE - Angiotensin Converting Enzyme
# ACKR2/3/4 - Atypical Chemokine Receptors
# ACSL1/3/4 - Acyl-CoA Synthetase Long Chain Family Members
# AKT1/2/3 - AKT Serine/Threonine Kinases
requires_gene_mapping = False
# 1. Normalize gene symbols and save
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
normalized_gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
clinical_data_loaded = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data_loaded, normalized_gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in features
trait_biased, filtered_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save cohort info
note = "This dataset contains COVID-19 binary trait data (convalescent vs healthy) and gene expression data from whole blood samples. Age and gender data are not available."
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=filtered_data,
note=note
)
# 6. Save linked data if usable
if is_usable:
filtered_data.to_csv(out_data_file) |