File size: 3,929 Bytes
56598a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # Path Configuration
from tools.preprocess import *
# Processing context
trait = "Bladder_Cancer"
cohort = "GSE244266"
# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE244266"
# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE244266.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE244266.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE244266.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
# 1. Gene expression data availability
is_gene_available = True # RNA-based molecular subtype analysis implies gene expression data is available.
# 2. Variable availability
# From the sample characteristics dictionary:
# 0: treatment arm
# 1: disease (muscle-invasive bladder cancer) -> constant, not useful
# 2: clinical stage stratification
trait_row = None # Everyone has muscle-invasive bladder cancer -> constant feature
age_row = None # No age field present
gender_row = None # No gender field present
# 2.2 Conversion functions
def _extract_value(cell):
if cell is None:
return None
s = str(cell)
if ':' in s:
return s.split(':', 1)[1].strip()
return s.strip()
def convert_trait(cell):
v = _extract_value(cell)
if v is None or v == '':
return None
vl = v.lower()
# Map common labels to binary case/control for bladder cancer
if any(k in vl for k in ['normal', 'adjacent normal', 'healthy', 'control', 'benign', 'non-cancer']):
return 0
if any(k in vl for k in ['bladder cancer', 'urothelial', 'carcinoma', 'tumor', 'tumour', 'case', 'muscle-invasive']):
return 1
return None
def convert_age(cell):
v = _extract_value(cell)
if v is None or v == '':
return None
# extract first numeric token as age in years
import re
m = re.search(r'[-+]?\d*\.?\d+', v)
if not m:
return None
try:
age = float(m.group())
if age < 0 or age > 120:
return None
return age
except:
return None
def convert_gender(cell):
v = _extract_value(cell)
if v is None or v == '':
return None
vl = v.lower()
if vl in ['female', 'f', 'woman', 'women']:
return 0
if vl in ['male', 'm', 'man', 'men']:
return 1
# handle prefixes like 'gender: Male', 'sex: female'
if 'female' in vl:
return 0
if 'male' in vl:
return 1
return None
# 3. Save metadata (initial filtering)
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
# Skipped because trait_row is None (no usable trait variability in this dataset). |