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. 2017 Aug 1;33(15):2314-2321.
doi: 10.1093/bioinformatics/btx194.

SCODE: An Efficient Regulatory Network Inference Algorithm From Single-Cell RNA-Seq During Differentiation

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Free PMC article

SCODE: An Efficient Regulatory Network Inference Algorithm From Single-Cell RNA-Seq During Differentiation

Hirotaka Matsumoto et al. Bioinformatics. .
Free PMC article

Abstract

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.

Figures

Fig. 1
Fig. 1
Abstract illustration of SCODE. (a) Sample Z(e) from the ODE of z (b) Estimate W based on linear regression. (c) Optimize B iteratively. (d) Infer A from optimized W and B. (e) The expression dynamics can be reconstructed from the optimized ODE of x
Fig. 2
Fig. 2
The first, second and third quantiles of the RSS values of test data (a) and the correlations among optimized A of the top 50 replicates (b) for each D (D = 2, 4, 6 and 8) for each dataset
Fig. 3
Fig. 3
The first, second and third quantiles of the correlation coefficients between genuine A and inferred A for each D
Fig. 4
Fig. 4
PCA of scRNA-Seq data for each dataset. Each circle represents a cell, and its color represents experimental time (from light gray to black). The reconstructed expression dynamics are projected onto PCA space and are represented by colored lines (green, yellow, orange and red correspond to D =2, 4, 6 and 8, respectively)
Fig. 5
Fig. 5
Observed expression of four TFs and reconstructed dynamics for each D (green, yellow, orange and red correspond to D =2, 4, 6 and 8, respectively). The x-axis represents pseudo-time and y-axis represents log(TPM + 1)
Fig. 6
Fig. 6
(a) Bar graph of positive and negative edges of each TF in decreasing order. For visibility, only the top 60 TFs are shown (see Supplementary text for plot of all TFs). (b) Bar graph of the top 10 TFs

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