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from tools.preprocess import *
# Processing context
trait = "Depression"
cohort = "GSE208668"
# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE208668"
# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE208668.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE208668.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE208668.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
# Step 1: Determine gene expression data availability based on background info
# Background explicitly states raw data was lost and not included -> no usable gene expression data.
is_gene_available = False
# Step 2: Identify rows for trait, age, and gender from the Sample Characteristics Dictionary
trait_row = 9 # 'history of depression: yes/no' -> aligns with trait "Depression"
age_row = 1 # 'age: <number>'
gender_row = 2 # 'gender: female/male'
# Data availability flags
is_trait_available = trait_row is not None
# Step 2.2: Define conversion functions
def _after_colon(x):
if x is None:
return None
s = str(x)
if ':' in s:
s = s.split(':', 1)[1]
return s.strip().strip('"').strip("'")
def convert_trait(x):
"""
Map history of depression to binary: no->0, yes->1
"""
v = _after_colon(x)
if v is None or v == '':
return None
v_lower = v.strip().lower()
mapping_yes = {'yes', 'y', '1', 'true', 'present', 'positive', 'pos'}
mapping_no = {'no', 'n', '0', 'false', 'absent', 'negative', 'neg'}
if v_lower in mapping_yes:
return 1
if v_lower in mapping_no:
return 0
# Heuristic: if contains 'yes' or 'no' substrings
if 'yes' in v_lower:
return 1
if 'no' in v_lower:
return 0
return None
def convert_age(x):
"""
Convert age to continuous (float).
"""
v = _after_colon(x)
if v is None or v == '':
return None
try:
return float(str(v).strip())
except Exception:
return None
def convert_gender(x):
"""
Map gender to binary: female->0, male->1
"""
v = _after_colon(x)
if v is None or v == '':
return None
v_lower = v.strip().lower()
if v_lower in {'female', 'f', 'woman', 'women', 'girl'}:
return 0
if v_lower in {'male', 'm', 'man', 'men', 'boy'}:
return 1
# Sometimes encoded as 0/1 or F/M
if v_lower in {'0'}:
return 0
if v_lower in {'1'}:
return 1
return None
# Step 3: Initial filtering and save metadata
_ = 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 (only if trait_row is available)
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 and save
preview = preview_df(selected_clinical_df)
print(preview)
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file) |