Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data
Journal:
arXiv
Published Date:
Jan 7, 2025
Abstract
Neurodegenerative diseases (NDDs) are complex and lack effective treatment
due to their poorly understood mechanism. The increasingly used data analysis
from Single nucleus RNA Sequencing (snRNA-seq) allows to explore transcriptomic
events at a single cell level, yet face challenges in interpreting the
mechanisms underlying a disease. On the other hand, Neural Network (NN) models
can handle complex data to offer insights but can be seen as black boxes with
poor interpretability. In this context, explainable AI (XAI) emerges as a
solution that could help to understand disease-associated mechanisms when
combined with efficient NN models. However, limited research explores XAI in
single-cell data. In this work, we implement a method for identifying
disease-related genes and the mechanistic explanation of disease progression
based on NN model combined with SHAP. We analyze available Huntington's disease
(HD) data to identify both HD-altered genes and mechanisms by adding Gene Set
Enrichment Analysis (GSEA) comparing two methods, differential gene expression
analysis (DGE) and NN combined with SHAP approach. Our results show that DGE
and SHAP approaches offer both common and differential sets of altered genes
and pathways, reinforcing the usefulness of XAI methods for a broader
perspective of disease.
