GeniOmics

公开知识文档

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

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 appli

打开平台知识库

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

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

  • 引用：Matsumoto H, Kiryu H, Furusawa C, Ko MSH, Ko SBH, Gouda N, Hayashi T, Nikaido I. (2017). SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics (Oxford, England). PMID 28379368 · PMC5860123 · DOI 10.1093/bioinformatics/btx194
  • 证据类型：METHODS
  • 主题：rna-seq、single-cell
  • 被引次数（采集时）：303
  • 原始记录：[Europe PMC](https://europepmc.org/article/MED/28379368)
  • 来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

    • 作者摘要（按来源许可复用）

    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/SCODE. Contact hirotaka.matsumoto@riken.jp. Supplementary information Supplementary data are available at Bioinformatics online.

    合规说明

    本页保存的是来源文献书目信息及其在 CC-BY 许可下公开的作者摘要。除去除来源 HTML 标签和规范化空白外，摘要未作内容改写。本页不代表 GeniOmics 的医学建议；原文版权、署名和许可仍归原权利人，请通过原始记录核对最新版本、更正或撤稿状态。

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