1Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, Wako, Saitama 351-0198, Japan2Department of Computational Biology and Medical Sciences, Faculty of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan3Quantitative Biology Center (QBiC), RIKEN, Suita, Osaka 565-0874, Japan4Universal Biology Institute, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan5Department of Systems Medicine, Keio University School of Medicine, Tokyo 160-8582, Japan
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Motivation:The analysis of RNA-Seq data from individual differentiating cells enables us to reconstruct the differentiation process and the degree of differentiation (in pseudo-time) of each cell. Such analyses can reveal detailed expression dynamics and functional relationships for differentiation. To further elucidate differentiation processes, more insight into gene regulatory networks is required. The pseudo-time can be regarded as time information and, therefore, single-cell RNA-Seq data are time-course data with high time resolution. Although time-course data are useful for inferring networks, conventional inference algorithms for such data suffer from high time complexity when the number of samples and genes is large. Therefore, a novel algorithm is necessary to infer networks from single-cell RNA-Seq during differentiation.Results:In this study, we developed the novel and efficient algorithm SCODE to infer regulatory networks, based on ordinary differential equations. We applied SCODE to three single-cell RNA-Seq datasets and confirmed that SCODE can reconstruct observed expression dynamics. We evaluated SCODE by comparing its inferred networks with use of a DNaseI-footprint based network. The performance of SCODE was best for two of the datasets and nearly best for the remaining dataset. We also compared the runtimes and showed that the runtimes for SCODE are significantly shorter than for alternatives. Thus, our algorithm provides a promising approach for further single-cell differentiation analyses.Availability and Implementation:The R source code of SCODE is available at https://github.com/hmatsu1226/SCODEContact:email@example.comSupplementary information:Supplementary data are available at Bioinformatics online.