p1/preprocess/Cystic_Fibrosis/code/GSE107846.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE107846"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE107846"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE107846.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE107846.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE107846.csv"
json_path = "./output/preprocess/1/Cystic_Fibrosis/cohort_info.json"

# STEP1
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)
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on the GEO entry, we assume it's a gene expression dataset.

# 2. Variable Availability
trait_row = 5   # "state: CF" or "state: Healthy"
age_row = 1     # "age: ..."
gender_row = 2  # "Sex: F" / "Sex: M"

# 2.2 Data Type Conversions
def convert_trait(value: str):
    # Extract the text after the first colon
    val = value.split(":", 1)[1].strip() if ":" in value else value.strip()
    # Convert to binary: CF -> 1, Healthy -> 0
    if val.upper() == "CF":
        return 1
    elif val.upper() == "HEALTHY":
        return 0
    return None

def convert_age(value: str):
    # Extract the text after the first colon
    val = value.split(":", 1)[1].strip() if ":" in value else value.strip()
    # Convert to float if possible
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str):
    # Extract the text after the first colon
    val = value.split(":", 1)[1].strip() if ":" in value else value.strip()
    # Convert to binary: F -> 0, M -> 1
    if val.upper() == "F":
        return 0
    elif val.upper() == "M":
        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 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)
    print("Preview of selected clinical features:", preview)
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 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])
# Based on the gene identifiers (ILMN_XXXXXX), these appear to be Illumina probe IDs.
# Therefore, they require mapping to standard gene symbols.

requires_gene_mapping = True
# STEP5
# 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: Gene Identifier Mapping

# 1. Decide which key in the gene annotation corresponds to the same identifier type as in the gene expression data
#    and which key corresponds to the gene symbols.
#    From observation, 'ID' matches ILMN probe identifiers (e.g., "ILMN_1245321") and 'SYMBOL' stores gene symbols.

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

# 3. Convert probe-level measurements to gene expression data by applying the gene mapping.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# (gene_data now contains gene expression values indexed by gene symbols)
print("Mapped gene_data shape:", gene_data.shape)
print("First few rows of mapped gene expression data:\n", gene_data.head())
import pandas as pd

# STEP7

# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Based on Step 2, we concluded trait_row=5 (thus trait data is available).
is_trait_available = True

if not is_trait_available:
    # 2-4: Skip linking, missing value handling, and bias checks because trait is unavailable.
    empty_df = pd.DataFrame()
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False,
        is_biased=True,
        df=empty_df,
        note="Trait data not available; skipping further steps."
    )
else:
    # 2. Load the clinical data. Since the CSV was saved with index=False, we first read the file,
    # then manually set the row index to ["Cystic_Fibrosis","Age","Gender"].
    selected_clinical_data = pd.read_csv(out_clinical_data_file, header=0)
    selected_clinical_data.index = [trait, "Age", "Gender"]

    # 3. Link the clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)

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

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

    # 6. Conduct final quality validation and save the cohort information
    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="Final check after linking and missing-value handling."
    )

    # 7. If the dataset is usable, save it as CSV
    if is_usable:
        unbiased_linked_data.to_csv(out_data_file)