SCODE: an efficient regulatory network inference algorithm 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/SCODE.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Bartlett, Thomas E.; Kosmidis, Ioannis; Silva, Ricardo: Two-way sparsity for time-varying networks with applications in genomics (2021)
- Wang, Y. X. Rachel; Li, Lexin; Li, Jingyi Jessica; Huang, Haiyan: Network modeling in biology: statistical methods for gene and brain networks (2021)
- Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)