p1/preprocess/Anorexia_Nervosa/code/GSE60190.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anorexia_Nervosa"
cohort = "GSE60190"

# Input paths
in_trait_dir = "../DATA/GEO/Anorexia_Nervosa"
in_cohort_dir = "../DATA/GEO/Anorexia_Nervosa/GSE60190"

# Output paths
out_data_file = "./output/preprocess/1/Anorexia_Nervosa/GSE60190.csv"
out_gene_data_file = "./output/preprocess/1/Anorexia_Nervosa/gene_data/GSE60190.csv"
out_clinical_data_file = "./output/preprocess/1/Anorexia_Nervosa/clinical_data/GSE60190.csv"
json_path = "./output/preprocess/1/Anorexia_Nervosa/cohort_info.json"

# STEP 1

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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Determine if gene expression data is available
# Based on the background info (Illumina HumanHT-12 v3 microarray measurements),
# we conclude that gene expression data is available.
is_gene_available = True

# 2. Identify rows and define conversion functions for trait, age, and gender.

# After examining the sample characteristics dictionary, we select:
# - trait information in row 3 ("dx: ED", "dx: OCD", "dx: Control", etc.)
#   We'll map "dx: ED" -> 1 (our trait of interest, albeit grouped as ED)
#   and everything else -> 0.
trait_row = 3

# - age information in row 5 (e.g., "age: 50.421917")
age_row = 5

# - gender information in row 7 (e.g., "Sex: F" or "Sex: M")
gender_row = 7

def convert_trait(x: str) -> Optional[int]:
    parts = x.split(":", 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    # Convert "ED" to 1, others (including OCD, Control, etc.) to 0
    return 1 if val == "ED" else 0

def convert_age(x: str) -> Optional[float]:
    parts = x.split(":", 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(x: str) -> Optional[int]:
    parts = x.split(":", 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    # Map "F" -> 0, "M" -> 1
    if val == "F":
        return 0
    elif val == "M":
        return 1
    return None

# 2.1 Check if trait data is available
# We consider trait data available if trait_row is not None
is_trait_available = (trait_row is not None)

# 3. Perform initial filtering and save metadata
# (is_final=False for initial filtering)
is_usable = 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. If trait_row is not None, extract clinical features, preview, and save
if trait_row is not None:
    clinical_data_selected = 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 the selected clinical data
    preview = preview_df(clinical_data_selected)
    print("Preview of selected clinical features:", preview)

    # Save the clinical features to CSV
    clinical_data_selected.to_csv(out_clinical_data_file)
# 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])
print("These are Illumina probe identifiers (e.g., ILMN_xxxx), which are not human gene symbols and thus require mapping.")
print("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))
# STEP6 - Gene Identifier Mapping

# 1. Identify the columns for probe IDs and gene symbols in the gene_annotation DataFrame.
#    From observing the data preview, "ID" holds Illumina probe identifiers matching our gene_data index,
#    and "Symbol" holds the gene symbol information.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

# 2. Apply this mapping to the probe-level expression data to get gene-level expression data.
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Now, 'gene_data' contains gene expression values indexed by gene symbols.
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# Make sure we have the clinical data in scope.
# In previous steps, we stored it as 'clinical_data_selected', so define 'selected_clinical' for consistency here.
selected_clinical = clinical_data_selected

# 2. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)

# 3. Handle missing values, removing or imputing as instructed
linked_data = handle_missing_values(linked_data, trait)

# 4. Determine whether the trait (and potentially other features) is severely biased.
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Conduct final quality validation and save 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,  # We do have a trait column
    is_biased=trait_biased,
    df=linked_data,
    note="Cohort data successfully processed with trait-based analysis."
)

# 6. If the dataset is usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file, index=True)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("The dataset is not usable for trait-based association. Skipping final output.")