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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE273225"
# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE273225"
# Output paths
out_data_file = "./output/preprocess/1/Cardiovascular_Disease/GSE273225.csv"
out_gene_data_file = "./output/preprocess/1/Cardiovascular_Disease/gene_data/GSE273225.csv"
out_clinical_data_file = "./output/preprocess/1/Cardiovascular_Disease/clinical_data/GSE273225.csv"
json_path = "./output/preprocess/1/Cardiovascular_Disease/cohort_info.json"
# STEP1
from tools.preprocess import *
# 1. Attempt to identify the paths to the SOFT file and the matrix file
try:
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
except AssertionError:
print("[WARNING] Could not find the expected '.soft' or '.matrix' files in the directory.")
soft_file, matrix_file = None, None
if soft_file is None or matrix_file is None:
print("[ERROR] Required GEO files are missing. Please check file names in the cohort directory.")
else:
# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file,
background_prefixes,
clinical_prefixes)
# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)
# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
is_gene_available = True # The dataset uses nCounter digital gene expression (not purely miRNA or methylation).
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# From inspecting the sample characteristics, there's no indication of "Cardiovascular_Disease" or similar trait info.
# Hence, trait is not available in this dataset:
trait_row = None
# The 'donor age (y)' entries are at row 3, and they have multiple unique values:
age_row = 3
# The 'donor sex' entries at row 4 have both "male" and "female" values, so gender is available:
gender_row = 4
# 2.2 Data Type Conversion
def convert_trait(x: str):
"""
No trait data is available, so always return None.
"""
return None
def convert_age(x: str):
"""
Convert 'donor age (y): 63' to a continuous numeric value.
Parse out the number after the colon, if available.
"""
try:
# split by ':' and take the last part
val_str = x.split(':')[-1].strip()
return float(val_str)
except:
return None
def convert_gender(x: str):
"""
Convert 'donor sex: male' -> 1, 'donor sex: female' -> 0.
Return None if parsing fails.
"""
try:
val_str = x.split(':')[-1].strip().lower()
if val_str == 'female':
return 0
elif val_str == 'male':
return 1
else:
return None
except:
return None
# 3. Save Metadata with initial filtering
is_trait_available = (trait_row is not None)
is_final = False # initial filtering
validate_and_save_cohort_info(
is_final=is_final,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available
)
# 4. Clinical Feature Extraction
# Since trait_row is None, we skip this step (no clinical data extraction).
# STEP3
# Attempt to read gene expression data; if the library function yields an empty DataFrame,
# try re-reading without ignoring lines that start with '!' (because sometimes GEO data may
# place actual expression rows under lines that begin with '!').
gene_data = get_genetic_data(matrix_file)
if gene_data.empty:
print("[WARNING] The gene_data is empty. Attempting alternative loading without treating '!' as comments.")
import gzip
# Locate the marker line first
skip_rows = 0
with gzip.open(matrix_file, 'rt') as file:
for i, line in enumerate(file):
if "!series_matrix_table_begin" in line:
skip_rows = i + 1
break
# Read the data again, this time not treating '!' as comment
gene_data = pd.read_csv(
matrix_file,
compression="gzip",
skiprows=skip_rows,
delimiter="\t",
on_bad_lines="skip"
)
gene_data = gene_data.rename(columns={"ID_REF": "ID"}).astype({"ID": "str"})
gene_data.set_index("ID", inplace=True)
# Print the first 20 row IDs to confirm data structure
print(gene_data.index[:20])
# Based on the given identifiers (e.g., ABCB1, ABCF1, ABL1), these appear to be standard human gene symbols.
# Therefore, no mapping to gene symbols is required.
requires_gene_mapping = False
import os
import pandas as pd
# STEP7: Data Normalization and Linking
# 1) Normalize the gene symbols in the previously obtained gene_data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
# 2) Load clinical data only if it exists and is non-empty
if os.path.exists(out_clinical_data_file) and os.path.getsize(out_clinical_data_file) > 0:
# Read the file
clinical_temp = pd.read_csv(out_clinical_data_file)
# Adjust row index to label the trait, age, and gender properly
if clinical_temp.shape[0] == 3:
clinical_temp.index = [trait, "Age", "Gender"]
elif clinical_temp.shape[0] == 2:
clinical_temp.index = [trait, "Gender"]
elif clinical_temp.shape[0] == 1:
clinical_temp.index = [trait]
# 2) Link the clinical and normalized genetic data
linked_data = geo_link_clinical_genetic_data(clinical_temp, normalized_gene_data)
# 3) Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4) Check for severe bias in the trait; remove biased demographic features if present
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5) Final quality validation and save metadata
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=f"Final check on {cohort} with {trait}."
)
# 6) If the linked data is usable, save it
if is_usable:
linked_data.to_csv(out_data_file)
else:
# If no valid clinical data file is found, finalize metadata indicating trait unavailability
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=True, # Force a fallback so that it's flagged as unusable
df=pd.DataFrame(),
note=f"No trait data found for {cohort}, final metadata recorded."
)
# Per instructions, do not save a final linked data file when trait data is absent. |