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GenoTEX / output /preprocess /Depression /code /GSE273630.py

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# Path Configuration
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)

	# Path Configuration
	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)