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

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