Singlecell RNAseq Imputation Based on Matrix Completion with Side Information Dropout events in singlecell RNA sequencing cause large numbers of zero values in gene expression matrices. Zero values hinder accurate downstream analysis of singlecell RNAseq (scRNAseq) data. In this study, to estimate the zero values in scRNAseq data, we proposed a novel method based on the low rank matrix completion approach. The novelty of the proposed method is due to using the gene association information as side information. We observed that incorporating the gene association information can facilitate more accurate recovery of gene expression matrices. To further improve the accuracy of zero imputation, we additionally employed a statistical model to estimate the dropout probability of each gene to adjust imputed gene expression matrices. We conducted extensive experiments to evaluate the performance of the proposed approach using several datasets. We compared the performance of the proposed method with that of three commonly used zero imputation methods in terms of accuracy in downstream clustering analysis. Results show that the proposed method has higher or comparable power for accurate zero imputation while has shorter run time compared to the commonly used zero imputation methods.

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.