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from tools.preprocess import *
# Processing context
trait = "Arrhythmia"
cohort = "GSE136992"
# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE136992"
# Output paths
out_data_file = "./output/z1/preprocess/Arrhythmia/GSE136992.csv"
out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE136992.csv"
out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE136992.csv"
json_path = "./output/z1/preprocess/Arrhythmia/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
import re
import math
# 1) Gene expression data availability
# Background indicates "mRNA expression ... Illumina whole genome gene expression DASL HT assay" -> gene data available.
is_gene_available = True
# 2) Variable availability and conversion functions
# Trait (Arrhythmia): Not available in this dataset; "condition" is Infection vs Control and does not indicate arrhythmia.
trait_row = None
# Age: Available at row 2 (values like "age: 0.5 weeks", etc.)
age_row = 2
# Gender: Available at row 3 ("gender: male/female")
gender_row = 3
def convert_trait(x):
# Trait not available; return None for any input
return None
def _extract_value_after_colon(x):
if x is None:
return None
s = str(x)
if ':' in s:
return s.split(':', 1)[1].strip()
return s.strip()
def convert_age(x):
"""
Convert age string like 'age: 12 weeks' into a float number of weeks.
Unknown or unparsable values -> None.
"""
val = _extract_value_after_colon(x)
if val is None:
return None
v = val.lower().strip()
# Accept numbers possibly with unit; default unit weeks if not specified
# Handle common units
m = re.match(r'^([0-9]*\.?[0-9]+)\s*(week|weeks|wk|wks|day|days|d|month|months|mo|year|years|yr|yrs)?$', v)
if not m:
return None
num = float(m.group(1))
unit = m.group(2) if m.group(2) else 'weeks'
unit = unit.lower()
# Convert all to weeks
if unit in ['week', 'weeks', 'wk', 'wks']:
weeks = num
elif unit in ['day', 'days', 'd']:
weeks = num / 7.0
elif unit in ['month', 'months', 'mo']:
weeks = num * (365.25 / 12.0) / 7.0
elif unit in ['year', 'years', 'yr', 'yrs']:
weeks = num * 52.17857 # approx
else:
weeks = num # default to weeks
return weeks
def convert_gender(x):
"""
Convert gender to binary: female -> 0, male -> 1; unknown -> None.
"""
val = _extract_value_after_colon(x)
if val is None:
return None
g = val.strip().lower()
if g in ['female', 'f']:
return 0
if g in ['male', 'm']:
return 1
return None
# 3) Save metadata with 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 |