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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Arrhythmia"
cohort = "GSE143924"
# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE143924"
# Output paths
out_data_file = "./output/preprocess/1/Arrhythmia/GSE143924.csv"
out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE143924.csv"
out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE143924.csv"
json_path = "./output/preprocess/1/Arrhythmia/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) Assess Gene Expression Data Availability
is_gene_available = True # "Transcriptome analysis" indicates gene expression data is available.
# 2) Identify Variable Availability (trait, age, gender) and Define Converters
trait_row = 1
age_row = None
gender_row = None
def convert_trait(value: str):
parts = value.split(':')
val = parts[1].strip() if len(parts) > 1 else parts[0].strip()
# Map sinus rhythm => 0, atrial fibrillation => 1, otherwise None
if 'sinus rhythm' in val.lower():
return 0
elif 'atrial fibrillation' in val.lower():
return 1
return None
def convert_age(value: str):
return None # No age data available
def convert_gender(value: str):
return None # No gender data available
# 3) 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
)
# 4) If trait data is available, extract clinical features and save
if trait_row is not None:
selected_clinical_data = 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
)
print("Preview of Selected Clinical Features:")
print(preview_df(selected_clinical_data, n=5, max_items=200))
selected_clinical_data.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, they appear to be recognized human gene symbols or their aliases.
requires_gene_mapping = False
# STEP 5: 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_data, 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.") |