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from tools.preprocess import *
# Processing context
trait = "Bladder_Cancer"
cohort = "GSE201395"
# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE201395"
# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE201395.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE201395.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE201395.csv"
json_path = "./output/z1/preprocess/Bladder_Cancer/cohort_info.json"
# Step 1: Initial Data Loading
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# 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("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# Step 2: Dataset Analysis and Clinical Feature Extraction
# Step 1: Determine gene expression availability based on background info
# Affymetrix HTA 2.0 is a gene expression microarray platform.
is_gene_available = True
# Step 2: Determine availability of trait, age, and gender
# The dataset consists of urothelial carcinoma cell lines only; no human subject-level trait variability.
# Sample characteristics only include cell line names; no age or gender data.
trait_row = None
age_row = None
gender_row = None
# Step 2.2: Define conversion functions
def _after_colon(value):
if value is None:
return None
s = str(value)
return s.split(":", 1)[-1].strip() if ":" in s else s.strip()
def convert_trait(value):
"""
Map to binary bladder cancer status if inferable:
- 1: cancer/urothelial carcinoma/tumor
- 0: normal/control/non-tumor/benign
- None: unknown
"""
v = _after_colon(value)
if not v:
return None
vl = v.lower()
non_tumor_tokens = ["normal", "control", "healthy", "non-tumor", "benign", "adjacent normal", "no cancer"]
tumor_tokens = ["cancer", "carcinoma", "tumor", "malignant", "urothelial", "bladder"]
if any(t in vl for t in non_tumor_tokens):
return 0
if any(t in vl for t in tumor_tokens):
return 1
return None
def convert_age(value):
v = _after_colon(value)
if not v:
return None
vl = v.lower()
if vl in {"na", "n/a", "unknown", "none", "missing"}:
return None
# Extract first number as age
import re
m = re.search(r"(\d+(\.\d+)?)", vl)
if m:
try:
return float(m.group(1))
except Exception:
return None
return None
def convert_gender(value):
v = _after_colon(value)
if not v:
return None
vl = v.lower()
if vl in {"female", "f", "woman", "women"}:
return 0
if vl in {"male", "m", "man", "men"}:
return 1
return None
# Step 3: Initial filtering and 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
)
# Step 4: Clinical feature extraction (skip because trait_row is None)
# If trait_row were available, we would extract and save clinical features like this:
# selected_clinical_df = 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 = preview_df(selected_clinical_df)
# selected_clinical_df.to_csv(out_clinical_data_file, index=True) |