An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data

Single-cell RNA sequencing (scRNA-seq) technologies are used to characterize the heterogeneity of cells in cell types, developmental stages and spatial positions. The rapid accumulation of scRNA-seq data has enabled single-cell-type labelling to transform single-cell transcriptome analysis. Here we propose an interpretable deep-learning architecture using capsule networks (called scCapsNet). A capsule structure (a neuron vector representing a set of properties of a specific object) captures hierarchical relations. By utilizing competitive single-cell-type recognition, the scCapsNet model is able to perform feature selection to identify groups of genes encoding different subcellular types. The RNA expression signatures, which enable subcellular-type recognition, are effectively integrated into the parameter matrices of scCapsNet. This characteristic enables the discovery of gene regulatory modules in which genes interact with each other and are closely related in function, but present distinct expression patterns. The wealth of data gathered from single-cell RNA sequencing can be processed with deep learning techniques, but often those methods are too opaque to reveal why a single cell is labelled to be a certain cell type. Lifei Wang and colleagues present an RNA-sequencing analysis method that uses capsule networks and is interpretable enough to allow for identification of cell-type-specific genes.