Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data

TL;DR

This paper introduces an explainable AI approach using neural networks and SHAP to identify disease-related genes and mechanisms in single-cell RNA sequencing data, specifically applied to Huntington's disease.

Contribution

It develops a novel method combining neural networks with SHAP for mechanistic insights in single-cell data, addressing interpretability challenges in disease analysis.

Findings

SHAP-based analysis identifies both common and unique disease-related genes.

The method provides mechanistic explanations complementing traditional differential expression analysis.

Results demonstrate the potential of XAI for understanding complex neurodegenerative diseases.

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.