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from tools.preprocess import *
# Processing context
trait = "Allergies"
cohort = "GSE169149"
# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE169149"
# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE169149.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE169149.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE169149.csv"
json_path = "./output/z1/preprocess/Allergies/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) Determine gene expression availability
is_gene_available = True # Blood tissue and treatment context imply gene expression profiling (not miRNA/methylation only)
# 2) Identify availability rows in the sample characteristics
# Sample Characteristics Dictionary given:
# 0: ['subject status: Sarcoidosis patient', 'subject status: healthy control']
# 1: ['treatment: none', 'treatment: tofacitinib']
# 2: ['tissue: Blood']
trait_row = None # No Allergies-related info present
age_row = None # No age info present
gender_row = None # No gender info present
# 2.2) Conversion functions
def _after_colon(x):
if x is None:
return None
if not isinstance(x, str):
x = str(x)
parts = x.split(":", 1)
v = parts[1] if len(parts) > 1 else parts[0]
return v.strip()
def convert_trait(x):
# Map allergy-related status to binary: 1 = has allergies/atopy; 0 = no allergies/controls; unknown -> None
v = _after_colon(x)
if v is None or v == "":
return None
s = v.lower()
# Strong positive indicators
pos_terms = [
"allergy", "allergies", "allergic", "atopy", "atopic", "asthma",
"hay fever", "rhinitis", "eczema", "urticaria"
]
if any(term in s for term in pos_terms):
return 1
# Strong negative indicators
neg_terms = [
"non-atopic", "healthy control", "control", "no allergy", "without allergies",
"none", "negative", "neg", "absent"
]
if any(term in s for term in neg_terms):
return 0
# Generic yes/no
if s in {"yes", "y", "true", "1", "positive", "pos", "present"}:
return 1
if s in {"no", "n", "false", "0"}:
return 0
return None
def convert_age(x):
v = _after_colon(x)
if v is None or v == "":
return None
s = v.lower().replace("years", "").replace("year", "").replace("yrs", "").replace("yr", "").strip()
# Remove common non-numeric placeholders
if s in {"na", "n/a", "unknown", "none"}:
return None
# Extract leading numeric if present
try:
return float(s.split()[0].replace(",", ""))
except Exception:
# Try to find a number within the string
import re
m = re.search(r"[-+]?\d*\.?\d+", s)
if m:
try:
return float(m.group(0))
except Exception:
return None
return None
def convert_gender(x):
# 0 = female, 1 = male
v = _after_colon(x)
if v is None or v == "":
return None
s = v.strip().lower()
# Standard labels
if s in {"female", "f", "woman", "women"}:
return 0
if s in {"male", "m", "man", "men"}:
return 1
# Handle common encodings
if s in {"0", "1"}:
return 1 if s == "1" else 0
if s in {"na", "n/a", "unknown", "none"}:
return None
return None
# 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
)
# 4) Clinical feature extraction (skip because trait_row is None)
if trait_row is not None:
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_df(selected_clinical_df)
# Save selected clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file) |