  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.
