scCapsNet-mask: an automatic version of scCapsNet

Summary Recently we developed scCapsNet, an interpretable deep learning cell type classifier for single cell RNA sequencing data, based on capsule network. However, the running process of scCapsNet is not fully automatic, in which a manual intervention is required for getting the final results. Here we present scCapsNet-mask, an updated version of scCapsNet that utilizes a mask to fully automate the running process of scCapsNet. scCapsNet-mask could constrain the internal parameter coupling coefficients and result in a one to one correspondence between the primary capsule and type capsule. Based on those bijective mapping between primary capsule and type capsule, the model could automatically extract the cell type related genes according to weight matrix connecting input and primary capsule, without a need for manual inspection of the relationship between primary capsules and type capsules. The scCapsNet-mask is evaluated on two single cell RNA sequence datasets. The results show that scCapsNet-mask not only retains the merits of the original scCapsNet with high classification accuracy and high interpretability, but also has the virtue of automatic processing.