output/preprocess/Bone_Density/code/GSE56815.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bone_Density"
cohort = "GSE56815"

# Input paths
in_trait_dir = "../DATA/GEO/Bone_Density"
in_cohort_dir = "../DATA/GEO/Bone_Density/GSE56815"

# Output paths
out_data_file = "./output/z2/preprocess/Bone_Density/GSE56815.csv"
out_gene_data_file = "./output/z2/preprocess/Bone_Density/gene_data/GSE56815.csv"
out_clinical_data_file = "./output/z2/preprocess/Bone_Density/clinical_data/GSE56815.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 os
import re
import pandas as pd

# 1. Gene Expression Data Availability
# Background indicates Affymetrix HG-U133A microarray analyses on monocytes => gene expression available.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Data Availability
# From the sample characteristics dictionary:
# 0: ['gender: Female'] -> constant, not useful
# 1: ['bone mineral density: high BMD', 'bone mineral density: low BMD'] -> trait available
# 2: ['state: postmenopausal', 'state: premenopausal'] -> not age
# 3: ['cell type: monocytes'] -> constant, not useful
trait_row = 1
age_row = None
gender_row = None

# 2.2 Data Type Conversion

def _after_colon_lower(x: str) -> str:
    if x is None or (isinstance(x, float) and pd.isna(x)):
        return ""
    s = str(x)
    if ':' in s:
        s = s.split(':', 1)[1]
    return s.strip().lower()

# Trait: binary (low vs high BMD): low -> 0, high -> 1
def convert_trait(x):
    s = _after_colon_lower(x)
    if not s:
        return None
    if 'low' in s:
        return 0
    if 'high' in s:
        return 1
    return None

# Age: not available in this dataset; function provided for completeness
def convert_age(x):
    s = _after_colon_lower(x)
    if not s:
        return None
    m = re.search(r'[-+]?\d*\.?\d+', s)
    if m:
        try:
            return float(m.group())
        except Exception:
            return None
    return None

# Gender: not available (all Female); function provided for completeness
def convert_gender(x):
    s = _after_colon_lower(x)
    if not s:
        return None
    if s in {'f', 'female', 'woman', 'women'}:
        return 0
    if s in {'m', 'male', '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 (only if trait data 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 = preview_df(selected_clinical_df, n=5)
    print("Clinical features preview:", preview)

    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# 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
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
# 1. Decide columns for mapping: probe IDs and gene symbols
probe_col = 'ID'  # Matches probe identifiers in gene expression data (e.g., '1007_s_at')
gene_symbol_col = 'Gene Symbol'  # Contains human gene symbols

# 2. Build mapping dataframe from annotation
mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)

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

# Step 7: Data Normalization and Linking
import os

# 1. Normalize the obtained gene data and save
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct quality check and save the cohort information.
note = ("INFO: All samples are female (Gender constant and removed); Age not available; "
        "Monocyte expression measured on Affymetrix HG-U133A; trait is high vs low hip BMD.")
is_usable = validate_and_save_cohort_info(
    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note
)

# 6. If the linked data is usable, save it
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)