TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data.
TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data.
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DOI：
10.1093/nar/gkaa1014
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发表时间：
2021-01-11
影响因子：
14.9
通讯作者：
Won KJ
中科院分区：
文献类型：
--
作者：
Kim J;T Jakobsen S;Natarajan KN;Won KJ
准确预测基因调控规则对理解细胞过程非常重要。为大量转录组学设计的现有计算算法通常需要大量的时间点来推断基因调控网络（grn），适用于少数基因，并且无法有效地检测潜在的因果关系。在这里，我们提出了一种新的方法“TENET”，从单细胞RNA测序（scRNAseq）数据集重建grn。TENET利用传递熵（TE）来衡量基因间因果关系的数量，从scRNAseq数据中预测大规模的基因调控级联/关系。在从公共数据集中识别关键调控因子方面，TENET表现出比其他GRN重构器更好的性能。特别是从scRNAseq中，TENET确定了胚胎干细胞（ESCs）和直接心肌细胞重编程过程中的关键转录因子，而其他预测因子都失败了。我们进一步证明，已知靶基因的TE值显著较高，TENET预测TE值较高的基因更容易受到其调控因子的扰动。使用TENET，我们鉴定并验证了Nme2是培养条件特异性干细胞因子。这些结果表明，TENET能够从scRNAseq数据中识别关键调控因子。
Accurate prediction of gene regulatory rules is important towards understanding of cellular processes. Existing computational algorithms devised for bulk transcriptomics typically require a large number of time points to infer gene regulatory networks (GRNs), are applicable for a small number of genes and fail to detect potential causal relationships effectively. Here, we propose a novel approach ‘TENET’ to reconstruct GRNs from single cell RNA sequencing (scRNAseq) datasets. Employing transfer entropy (TE) to measure the amount of causal relationships between genes, TENET predicts large-scale gene regulatory cascades/relationships from scRNAseq data. TENET showed better performance than other GRN reconstructors, in identifying key regulators from public datasets. Specifically from scRNAseq, TENET identified key transcriptional factors in embryonic stem cells (ESCs) and during direct cardiomyocytes reprogramming, where other predictors failed. We further demonstrate that known target genes have significantly higher TE values, and TENET predicted higher TE genes were more influenced by the perturbation of their regulator. Using TENET, we identified and validated that Nme2 is a culture condition specific stem cell factor. These results indicate that TENET is uniquely capable of identifying key regulators from scRNAseq data.
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影响因子：
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