N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification

TL;DR

N-ACT is an interpretable deep learning model that automatically identifies cell types and salient genes from scRNAseq data, offering high accuracy and interpretability without manual annotation.

Contribution

It introduces the first interpretable neural network for ACTI using neural-attention to detect salient genes, improving transparency and performance.

Findings

Accurately identifies marker genes and cell types

Performs comparably to state-of-the-art models

Operates in an unsupervised manner

Abstract

Single-cell RNA sequencing (scRNAseq) is rapidly advancing our understanding of cellular composition within complex tissues and organisms. A major limitation in most scRNAseq analysis pipelines is the reliance on manual annotations to determine cell identities, which are time consuming, subjective, and require expertise. Given the surge in cell sequencing, supervised methods-especially deep learning models-have been developed for automatic cell type identification (ACTI), which achieve high accuracy and scalability. However, all existing deep learning frameworks for ACTI lack interpretability and are used as "black-box" models. We present N-ACT (Neural-Attention for Cell Type identification): the first-of-its-kind interpretable deep neural network for ACTI utilizing neural-attention to detect salient genes for use in cell-type identification. We compare N-ACT to conventional annotation methods on two previously manually annotated data sets, demonstrating that N-ACT accurately identifies marker genes and cell types in an unsupervised manner, while performing comparably on multiple data sets to current state-of-the-art model in traditional supervised ACTI.