p1/preprocess/Cystic_Fibrosis/code/GSE71799.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE71799"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE71799"

# Output paths
out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE71799.csv"
out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE71799.csv"
out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE71799.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
# According to the background summary, gene expression analysis was performed, so:
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# Based on the sample characteristics dictionary:
# {0: ['responder cells: UPN727 cells']}
# There's a single key (0) with the same value for all samples, which does not provide trait,
# age, or gender variability. Therefore, all three are considered not available.

trait_row = None
age_row = None
gender_row = None

# Define conversion functions (though they won't be used due to None rows).
def convert_trait(value: str):
    # No actual data keys to parse, return None
    return None

def convert_age(value: str):
    # No actual data keys to parse, return None
    return None

def convert_gender(value: str):
    # No actual data keys to parse, return None
    return None

# 3. Save metadata with initial filtering
# Trait availability depends on whether trait_row is None.
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
)

# 4. Clinical Feature Extraction
# Since trait_row is None, we skip this step.
# 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("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. Identify columns in the annotation dataframe: 'ID' for probe identifiers and 'Gene Symbol' for gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

# 2. Convert probe-level expression data to gene-level expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)
import os
import pandas as pd

# STEP 7 (Revised with dummy DataFrame for final validation)

# 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)

# Because trait_row is None, there's no clinical data to link, so we skip trait-related steps.

# 2) Provide a dummy DataFrame and is_biased flag for final validation
dummy_df = pd.DataFrame()
dummy_is_biased = False

# 3) Final validation
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=dummy_is_biased,
    df=dummy_df,
    note="No trait data in GSE71799, only gene expression data."
)

# 4) Since the dataset is not usable due to no trait data, do not save any linked data.