p1/preprocess/Asthma/code/GSE270312.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE270312"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE270312"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE270312.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE270312.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE270312.csv"
json_path = "./output/preprocess/1/Asthma/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)
# Step 1: Gene Expression Data Availability
# Based on the background stating "RNA transcriptome responses" were measured, we consider it gene expression data.
is_gene_available = True

# Step 2: Variable Availability and Conversion

# 2.1 Identify rows for trait, age, and gender
# From the sample characteristics dictionary, 'asthma status' = row 3, 'gender' = row 2. 
# No age information is provided.
trait_row = 3
age_row = None
gender_row = 2

# 2.2 Define data conversion functions
def convert_trait(value: str):
    # Example: "asthma status: Yes"
    # Split by colon, then strip extra spaces
    parts = value.split(":")
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == "yes":
        return 1
    elif val == "no":
        return 0
    return None

def convert_age(value: str):
    # No age data available, so return None
    return None

def convert_gender(value: str):
    # Example: "gender: Female"
    parts = value.split(":")
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == "female":
        return 0
    elif val == "male":
        return 1
    return None

# Step 3: Save Metadata (initial filtering)
# Trait data is considered available if we have a valid row for it
is_trait_available = (trait_row is not None)

filter_pass = 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: Clinical Feature Extraction
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 the selected clinical features
    preview_clinical = preview_df(selected_clinical_df)
    # (You could print the preview or store it if needed; omitted here for brevity.)

    # Save the clinical data
    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 observed gene identifiers such as ABCF1, ACE, ACKR2, etc.,
# these appear to be valid human gene symbols and do not require additional mapping.

print("These genes are human gene symbols.")

# Conclusion
print("\nrequires_gene_mapping = False")
# 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}")

# 2. Link the clinical and genetic data on sample IDs
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_col=trait)

# 4. Determine whether the trait/demographic features are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=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,
    is_biased=trait_biased,
    df=linked_data,
    note="Trait data and gene data successfully linked."
)

# 6. If the dataset is deemed usable, save the final linked data as a CSV file
if is_usable:
    linked_data.to_csv(out_data_file)
    print(f"Saved final linked data to {out_data_file}")
else:
    print("Dataset was not deemed usable; final linked data not saved.")