SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

Abstract

MotivationThe 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.ResultsIn 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 ImplementationThe R source code of SCODE is available at https://github.com/hmatsu1226/SCODESupplementary information Supplementary data are available at Bioinformatics online.

Current best practices in single‐cell RNA‐seq analysis: a tutorial

“…Inferring gene regulatory relationships is related to the detection of trajectory-associated regulatory genes. Indeed, several single-cell GRN inference methods use trajectories with mechanistic differential equation models (Ocone et al, 2015;Matsumoto et al, 2017).…”

“…While there exist GRN inference methods that were specifically developed for scRNA-seq data (SCONE: Matsumoto et al, 2017;PIDC: Chan et al, 2017;SCENIC: Aibar et al, 2017), a recent comparison has shown both bulk and single-cell methods to perform poorly on these data (Chen & Mar, 2018). GRN inference methods may still offer valuable insights to identify causal regulators of biological processes, yet we recommend that these methods be used with care.…”

Learning Pathway Dynamics from Single‐Cell Proteomic Data: A Comparative Study

“…For this reason, most algorithms require the user to provide a starting position, for example, if the dynamic process involves differentiation, a cell at a pluripotent stage would be chosen as a starting point. Other approaches used for finding such curve are Gaussian Processes and Differential Equations (21)(22)(23).…”

Computational approaches for high‐throughput single‐cell data analysis

“…As was discussed in the section 'Approaches modelling gradual transitions', cells can be ordered along developmental trajectories. Some network inference methods can include the information from these inferred trajectories to reconstruct dynamic regulatory networks (AR1MA1 [117], SCODE [118]). Another source of external information could come from perturbational studies, in which genes are knocked out and the consequences on the transcriptome can be observed [21].…”