ScInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data.

ABSTRACT:

Background

The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train.

Results

To address the above issues, we propose scInterpreter, a deep learning-based interpretable model. scInterpreter substantially outperforms other state-of-the-art (SOTA) models in multiple benchmark datasets. In addition, scInterpreter is extensible and can integrate and annotate atlas scRNA-seq data. We evaluated the robustness of scInterpreter in a variety of situations. Through comparison experiments, we found that with a knowledge prior, the training process can be significantly accelerated. Finally, we conducte

SUBMITTER: Guo ZH 

PROVIDER: S-EPMC10724984 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature