output/preprocess/Bone_Density/code/GSE198934.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bone_Density"
cohort = "GSE198934"

# Input paths
in_trait_dir = "../DATA/GEO/Bone_Density"
in_cohort_dir = "../DATA/GEO/Bone_Density/GSE198934"

# Output paths
out_data_file = "./output/z2/preprocess/Bone_Density/GSE198934.csv"
out_gene_data_file = "./output/z2/preprocess/Bone_Density/gene_data/GSE198934.csv"
out_clinical_data_file = "./output/z2/preprocess/Bone_Density/clinical_data/GSE198934.csv"
json_path = "./output/z2/preprocess/Bone_Density/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
is_gene_available = True  # Affymetrix transcriptional profiling indicates gene expression data.

# 2. Variable Availability
trait_row = None           # No sample-level BMD values found; background only states varying BMD.
age_row = 0                # 'age (years):' present at key 0.
gender_row = None          # Cohort consists of women only (constant feature -> not useful).

# 2.2 Data Type Conversion Functions
def _extract_after_colon(x):
    if x is None:
        return None
    s = str(x)
    parts = s.split(':', 1)
    return parts[1].strip() if len(parts) == 2 else s.strip()

def _to_float_from_text(x):
    if x is None:
        return None
    m = re.search(r'[-+]?\d*\.?\d+', x)
    return float(m.group()) if m else None

def convert_trait(x):
    # Treat BMD as continuous when available; here likely absent.
    val = _extract_after_colon(x)
    return _to_float_from_text(val)

def convert_age(x):
    val = _extract_after_colon(x)
    return _to_float_from_text(val)

def convert_gender(x):
    val = _extract_after_colon(x)
    if val is None:
        return None
    v = val.strip().lower()
    if v in {'female', 'f', 'woman', 'women'}:
        return 0
    if v in {'male', 'm', 'man', 'men'}:
        return 1
    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: skipped because trait_row is None
# (No action required)

# Step 3: Gene Data Extraction
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])

# Step 4: Gene Identifier Review
# The observed identifiers are numeric probe IDs, not human gene symbols.
requires_gene_mapping = True
print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Gene Annotation
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))

# Step 6: Gene Identifier Mapping
# Determine appropriate columns for mapping
id_col = 'ID'
candidate_gene_cols = ['gene_assignment', 'mrna_assignment', 'GB_LIST']

# Select the first available gene annotation column with non-null values
gene_col = None
for col in candidate_gene_cols:
    if col in gene_annotation.columns and gene_annotation[col].notna().any():
        gene_col = col
        break

if gene_col is None:
    raise ValueError("No suitable gene annotation column found for mapping.")

# 2. Get a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_col)

# 3. Apply mapping to convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

# Step 7: Data Normalization and Linking
# 1. Normalize the obtained gene data and save gene-level data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Determine trait availability from previous steps
try:
    is_trait_available = (trait_row is not None)
except NameError:
    is_trait_available = False

# 2-6. Proceed only if trait data is available and clinical data was extracted; otherwise record unusable due to missing trait
if is_trait_available and 'selected_clinical_data' in globals():
    # 2. Link the clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

    # 3. Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

    # 4. Bias assessment and demographic feature pruning
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final validation and save cohort metadata
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=True,
        is_biased=is_trait_biased,
        df=unbiased_linked_data,
        note="INFO: Clinical and genetic data linked; standard preprocessing applied."
    )

    # 6. Save linked data if usable
    if is_usable:
        unbiased_linked_data.to_csv(out_data_file)
else:
    # Trait unavailable: record initial filtering only, no linking or downstream steps
    linked_data = None
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )