Deep generative autoencoder for lowdimensional embeding extraction from singlecell RNAseq data Singlecell RNA sequencing (scRNAseq) can reveal biological diversity at the cellular level that are unexplored by bulk RNA sequencing (RNAseq), but they suffer from the excessive zero expression counts and the limitation of the scalability in practice. Here, we propose a nonlinear generative autoencoder based method, scSVA, relying on an integration of variational autoencoder and dropout imputations. Specifically, scSVA automatically identifies the dropouts and recovery these values only to avoid introducing new biases. Then, scSVA utilizes stochastic optimization and deep neural network to extract the lowdimensional embedding from gene expression levels. We illustrate the benefits of scSVA through indepth real analyses of six published scRNAseq data sets. scSVA is up to 1.3 times more powerful cell clustering accuracy than existing approaches. The high power of scSVA allows us to identify new cell types that reveal new biology from scRNAseq data that otherwise cannot be revealed by existing approaches.

Detection of Cell Types from Singlecell RNAseq Data using Similarity via Kernel Preserving Learning Embedding The recent advances in singlecell sequencing techniques allow us to study biological issues on cell levels. Detecting cell types from scRNAseq data analysis is important and meaningful. However, highlevel noise and the nonlinearity and sparsity of scRNAseq data are great challenges. In this paper, we propose a celltype detection algorithm preserving the overall cell relations named POCR to analyze scRNAseq data. POCR utilizes a kernel embedding similarity measure to calculate celltocell similarity, by minimizing the reconstruction error of a kernel matrix, rather than the reconstruction error of the original data adopted by other similarity metrics. According to the scale of scRNAseq datasets, we select Gaussian kernel or linear kernel to calculate the embedding. We then adopt spectral clustering to detect the cell types based on the learned celltocell similarity. The results are further visualized to demonstrate the effectiveness of the celltype detection algorithm POCR. Further analysis shows that the learned similarity could improve the clustering and visualization of cell types in scRNAseq data. Our proposed algorithm is compared with five other stateoftheart cell subtype detection methods. The effectiveness of the algorithms is evaluated by two criteria: ARI and NMI. The experiments show that POCR achieves accurate and robust performance across different scRNAseq data. Our python implementation of POCR is available at https:github.comZeMingLiuPOCR.