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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Asthma"
cohort = "GSE205151"
# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE205151"
# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE205151.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE205151.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE205151.csv"
json_path = "./output/preprocess/1/Asthma/cohort_info.json"
# STEP 1
# 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. Gene expression data availability
# Based on the metadata: "mRNA was analyzed using a targeted Nanostring immunology array,"
# indicating this study involves gene expression data.
is_gene_available = True
# 2. Variable Availability and Conversion
# From the sample characteristics, only two keys (0 and 1) are available:
# 0 -> polyic_stimulation, and 1 -> cluster
# There's no mention of 'Asthma' variation, age, or gender.
# So, all samples are asthmatic, which yields no variability in 'trait',
# and age/gender aren't in the dictionary.
trait_row = None # No variation in "Asthma" (everyone is asthmatic)
age_row = None # Not found
gender_row = None # Not found
def convert_trait(value: str) -> int:
"""
Trait data is not available/variable here,
so we won't actually use this function.
"""
return None
def convert_age(value: str) -> float:
"""
Age data not available.
"""
return None
def convert_gender(value: str) -> int:
"""
Gender data not available.
"""
return None
# 3. Save Metadata (initial filtering)
# Trait data is not available because there's no variability.
is_trait_available = False
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 extraction for this dataset.
# 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 inspection, these appear to be standard human gene symbols.
print("requires_gene_mapping = False") |