DeepCI: a deep learning based clustering method for single cell RNA-seq data

Single cell RNA sequencing enables researchers to analyze cellular heterogeneity at high resolution. In the cellular heterogeneity analysis, unsupervised clustering has been a common and powerful way to identify cell types. Nevertheless, the high dropout rate and high dimension of scRNA-seq data make it still a challenging task. In this study, we proposed DeepCI, a deep neural network based single cell clustering method, which simultaneously accomplishes low-dimensional representation learning and clustering with implicit imputation of scRNA-seq data. Tested on real datasets, DeepCI obtained overall better clustering and visualization performance than several state-of-the-art approaches.