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

Hirotaka Matsumoto, Hisanori Kiryu, Chikara Furusawa, Minoru Ko, Shigeru Ko, Norio Gouda, Tetsutaro Hayashi, Itoshi Nikaido

研究成果: Article

28 引用 (Scopus)

抄録

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 RNASeq 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 RNASeq 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 singlecell differentiation analyses.

元の言語English
ページ(範囲)2314-2321
ページ数8
ジャーナルBioinformatics
33
発行部数15
DOI
出版物ステータスPublished - 2017 8 1

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Regulatory Networks
RNA
Cell
Genes
Ordinary differential equations
Functional Relationship
Gene Regulatory Network
Gene Regulatory Networks
Time Complexity
Ordinary differential equation
Efficient Algorithms
Gene
Necessary
Alternatives
Datasets

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

これを引用

SCODE : An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. / Matsumoto, Hirotaka; Kiryu, Hisanori; Furusawa, Chikara; Ko, Minoru; Ko, Shigeru; Gouda, Norio; Hayashi, Tetsutaro; Nikaido, Itoshi.

:: Bioinformatics, 巻 33, 番号 15, 01.08.2017, p. 2314-2321.

研究成果: Article

Matsumoto, Hirotaka ; Kiryu, Hisanori ; Furusawa, Chikara ; Ko, Minoru ; Ko, Shigeru ; Gouda, Norio ; Hayashi, Tetsutaro ; Nikaido, Itoshi. / SCODE : An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. :: Bioinformatics. 2017 ; 巻 33, 番号 15. pp. 2314-2321.
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