scCapsNet-mask: an updated version of scCapsNet with extended applicability in functional analysis related to scRNA-seq data

Abstract

Background With the rapid accumulation of scRNA-seq data, more and more automatic cell type identification methods have been developed, especially those based on deep learning. Although these methods have reached relatively high prediction accuracy, many issues still exist. One is the interpretability. The second is how to deal with the non-standard test samples that are not encountered in the training process. Results Here we introduce scCapsNet-mask, an updated version of scCapsNet. The scCapsNet-mask provides a reasonable solution to the issues of interpretability and non-standard test samples. Firstly, the scCapsNet-mask utilizes a mask to ease the task of model interpretation in the original scCapsNet. The results show that scCapsNet-mask could constrain the coupling coefficients, and make a one-to-one correspondence between the primary capsules and type capsules. Secondly, the scCapsNet-mask can process non-standard samples more reasonably. In one example, the scCapsNet-mask was trained on the committed cells, and then tested on less differentiated cells as the non-standard samples. It could not only estimate the lineage bias of less differentiated cells, but also distinguish the development stages more accurately than traditional machine learning models. Therefore, the pseudo-temporal order of cells for each lineage could be established. Following these pseudo-temporal order, lineage specific genes exhibit a gradual increase expression pattern and stem cell associated genes exhibit a gradual decrease expression pattern. In another example, the scCapsNet-mask was trained on scRNA-seq data, and then used to assign cell type in spatial transcriptomics that may contain non-standard sample of doublets. The results show that the scCapsNet-mask not only restored the spatial map but also identified several non-standard samples of doublet. Conclusions The scCapsNet-mask offers a suitable solution to the challenge of interpretability and non-standard test samples. By adding a mask, it has the advantages of automatic processing and easy interpretation compared with the original scCapsNet. In addition, the scCapsNet-mask could more accurately reflect the composition of non-standard test samples than traditional machine learning methods. Therefore, it can extend its applicability in functional analysis, such as fate bias prediction in less differentiated cells and cell type assignment in spatial transcriptomics.

InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation

“…Adding masks is a common way to enhance models in deep learning [ 28 ]. In inClust+, we augment our model through a pair of mask modules (the input-mask module and the output-mask module).…”

“…In short, our model can not only integrate data, but also complete other downstream tasks on the basis of data integration (e. g. Out-of-distribution generation, label transfer and new type identification, spatial domain segmentation, cross modal imputation and generation). Adding masks is a common way to enhance models in deep learning [28]. In inClust+, we augment our model through a pair of mask modules (the input-mask module and the output-mask module).…”

InClust+: the multimodal version of inClust for multimodal data integration, imputation, and cross modal generation

“…Adding masks is a common way to enhance models in deep learning [27]. In inClust+, we augment our model through a pair of masks module (the input-mask module and the output-mask module).…”