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from tools.preprocess import *
# Processing context
trait = "Depression"
cohort = "GSE273630"
# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE273630"
# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE273630.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE273630.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE273630.csv"
json_path = "./output/z2/preprocess/Depression/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
# 1. Gene expression data availability
# Based on Nanostring digital transcript panel for inflammatory genes -> gene expression available
is_gene_available = True
# 2. Variable availability and data type conversion
# No usable clinical keys for trait/age/gender found in the sample characteristics.
# Background indicates all participants are male (constant -> not usable). Age not present as a field.
trait_row = None
age_row = None
gender_row = None
def _extract_value(x):
if x is None:
return None
if isinstance(x, (int, float)):
return x
s = str(x)
# take substring after the last colon if present
parts = s.split(":")
val = parts[-1].strip() if len(parts) > 1 else s.strip()
return val if val != "" else None
# Depression (trait): choose binary mapping if present
def convert_trait(x):
val = _extract_value(x)
if val is None:
return None
v = str(val).strip().lower()
# common positive indicators
pos = {"depression", "depressed", "mdd", "major depressive disorder", "case", "patient", "yes", "mds"}
neg = {"control", "healthy", "non-depressed", "no depression", "no", "hc"}
if v in pos:
return 1
if v in neg:
return 0
# heuristic patterns
if "depress" in v or "mdd" in v:
return 1
if "control" in v or "healthy" in v:
return 0
return None # unknown or non-depression-related field
# Age: continuous
def convert_age(x):
val = _extract_value(x)
if val is None:
return None
v = str(val).lower()
nums = re.findall(r"\d+\.?\d*", v)
if not nums:
return None
try:
age_val = float(nums[0])
if 0 < age_val < 120:
return age_val
except Exception:
return None
return None
# Gender: binary female->0, male->1
def convert_gender(x):
val = _extract_value(x)
if val is None:
return None
v = str(val).strip().lower()
if v in {"male", "m", "man", "boy"}:
return 1
if v in {"female", "f", "woman", "girl"}:
return 0
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 (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)
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file) |