p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE21359.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
cohort = "GSE21359"

# Input paths
in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359"

# Output paths
out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.csv"
out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv"
out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.csv"
json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/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)
# Step 1: Determine if the dataset likely contains gene expression data
is_gene_available = True  # Based on the background info ("Affymetrix arrays ..."), we consider this as gene expression data

# Step 2.1: Determine data availability
# Observing the sample characteristics dictionary, we see:
#   - key=0 appears to contain various ages.
#   - key=1 appears to contain M and F genders.
#   - key=3 contains smoking status with COPD or non-COPD conditions.
# We will use these keys for age, gender, and COPD trait, respectively.
trait_row = 3
age_row = 0
gender_row = 1

# Step 2.2: Data type conversions

def convert_trait(value: str) -> int:
    """
    Convert the raw smoking status string to a binary trait:
    1 = COPD
    0 = non-COPD
    Unknown/invalid -> None
    """
    # Typically the string has a pattern like "smoking status: COPD, GOLD-II, 60 pack-years"
    # or "smoking status: non-smoker".
    # Extract the portion after the first colon, then check if it contains "COPD".
    parts = value.split(":", 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if "copd" in val:
        return 1
    elif "smoker" in val or "non-smoker" in val:
        return 0
    return None

def convert_age(value: str) -> float:
    """
    Convert the raw age string to a numeric (continuous) age.
    Unknown/invalid -> None
    """
    parts = value.split(":", 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(value: str) -> int:
    """
    Convert the raw gender string to a binary indicator:
    0 = Female
    1 = Male
    Unknown/invalid -> None
    """
    parts = value.split(":", 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'm':
        return 1
    elif val == 'f':
        return 0
    return None

# Step 3: Conduct initial filtering and save metadata
is_trait_available = (trait_row is not None)
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
)

# Step 4: If trait data is available, extract clinical features
if trait_row is not None:
    selected_clinical = 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
    )
    # Observe the extracted clinical features
    preview_output = preview_df(selected_clinical)
    print("Preview of selected clinical features:", preview_output)

    # Save the clinical data
    selected_clinical.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 identifiers (e.g., "1007_s_at", "1053_at", etc.), these are Affymetrix probe set IDs, not standard human gene symbols.
# Therefore, they require mapping to gene symbols.
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. In the gene annotation DataFrame, "ID" matches the probe IDs in gene_data.index,
#    and "Gene Symbol" is the column containing human gene symbols.
# 2. Extract these columns to produce a mapping dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

# 3. Convert probe-level measurements into gene-level expression data.
gene_data = apply_gene_mapping(gene_data, mapping_df)
# 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)

# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
#    Use 'selected_clinical' variable from the previous step instead of 'selected_clinical_df'.
linked_data = geo_link_clinical_genetic_data(selected_clinical, 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.
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=linked_data
)

# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
if is_usable:
    unbiased_linked_data.to_csv(out_data_file)