output/preprocess/Cystic_Fibrosis/code/GSE60690.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE60690"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE60690"

# Output paths
out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE60690.csv"
out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE60690.csv"
out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE60690.csv"
json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
# Determine data availability based on provided background and sample characteristics
is_gene_available = True  # Global gene expression in RNA from LCLs (not miRNA/methylation)

# Use a relevant phenotype available in this cohort as the trait: consortium lung phenotype (continuous)
trait_row = 1
age_row = 2  # 'age of enrollment'
gender_row = 0  # 'Sex'

def _after_colon(x):
    if x is None:
        return None
    s = str(x)
    if ":" in s:
        return s.split(":", 1)[1].strip()
    return s.strip()

def convert_trait(x):
    # Convert "consortium lung phenotype: <value>" to float
    v = _after_colon(x)
    if v is None or v == "" or v.lower() in {"na", "n/a", "unknown", "unk"}:
        return None
    try:
        return float(v)
    except Exception:
        import re
        m = re.search(r"[-+]?\d*\.?\d+", v)
        if m:
            try:
                return float(m.group(0))
            except Exception:
                return None
        return None

def convert_age(x):
    v = _after_colon(x)
    if v is None or v == "" or v.lower() in {"na", "n/a", "unknown", "unk"}:
        return None
    try:
        return float(v)
    except Exception:
        # Handle possible units or text; extract leading numeric token
        import re
        m = re.search(r"[-+]?\d*\.?\d+", v)
        if m:
            try:
                return float(m.group(0))
            except Exception:
                return None
        return None

def convert_gender(x):
    v = _after_colon(x)
    if v is None or v == "":
        return None
    vlow = v.lower()
    if vlow in {"female", "f", "0"}:
        return 0
    if vlow in {"male", "m", "1"}:
        return 1
    if vlow in {"na", "n/a", "unknown", "unk"}:
        return None
    return None

# Initial filtering metadata save
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
)

# Clinical feature extraction since trait is available
import os

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)

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
print("requires_gene_mapping = True")

# 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
# 1-2. Decide mapping columns and build mapping dataframe
# Probe IDs match the 'ID' column; gene symbols can be parsed from 'gene_assignment'.
probe_col = 'ID'
gene_symbol_col = 'gene_assignment'
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_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)

# Optionally save the processed gene expression data
import os
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# Step 7: Data Normalization and Linking
import os

# 1. Normalize gene symbols 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 clinical and genetic data (fix variable name)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

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

# 4. Assess bias and drop biased demographics
is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = ("INFO: Trait is 'consortium lung phenotype' (continuous). Age is 'age of enrollment'; "
        "Gender from 'Sex'. Platform required probe->gene mapping; gene symbols normalized by NCBI synonyms.")
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=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    unbiased_linked_data.to_csv(out_data_file)