scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation.
scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation.
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DOI：
10.3390/genes13020371
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发表时间：
2022-02-18
期刊：
影响因子：
3.5
通讯作者：
Cai JJ
中科院分区：
文献类型：
--
作者：
Xu Q;Li G;Osorio D;Zhong Y;Yang Y;Lin YT;Zhang X;Cai JJ
轨迹推断（TI）或伪时间分析极大地扩展了单细胞RNA-seq数据的分析框架，使有助于细胞分化的调控基因和参与各种动态细胞过程的基因得以识别。然而，大多数TI分析程序单独处理单个基因，而忽略了基因之间的调节关系。整合来自不同伪时间点基因调控网络（grn）的信息可能会导致更可解释的TI结果。为此，我们引入了scintime——一个将推断轨迹与单细胞grn （scgrn）耦合的无监督机器学习框架，以识别主调控基因。我们通过分析多个scRNA-seq数据集验证了我们方法的性能。在每种情况下，sciintime预测的顶级基因都支持其与相应信号通路的功能相关性，这与现有的功能研究结果一致。总体结果表明，scInTime是利用伪时间序列scgrn的强大工具，允许对TI结果进行清晰的解释，以获得更重要的生物学见解。
Trajectory inference (TI) or pseudotime analysis has dramatically extended the analytical framework of single-cell RNA-seq data, allowing regulatory genes contributing to cell differentiation and those involved in various dynamic cellular processes to be identified. However, most TI analysis procedures deal with individual genes independently while overlooking the regulatory relations between genes. Integrating information from gene regulatory networks (GRNs) at different pseudotime points may lead to more interpretable TI results. To this end, we introduce scInTime—an unsupervised machine learning framework coupling inferred trajectory with single-cell GRNs (scGRNs) to identify master regulatory genes. We validated the performance of our method by analyzing multiple scRNA-seq data sets. In each of the cases, top-ranking genes predicted by scInTime supported their functional relevance with corresponding signaling pathways, in line with the results of available functional studies. Overall results demonstrated that scInTime is a powerful tool to exploit pseudotime-series scGRNs, allowing for a clear interpretation of TI results toward more significant biological insights.
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