scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data.
scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data.
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scMINER：一种基于相互信息的框架，用于从单细胞组学数据中识别隐藏的驱动因素。
DOI：
10.1101/2023.01.26.523391
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Yu,Jiyang
中科院分区：
文献类型：
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作者：
Ding,Liang;Shi,Hao;Qian,Chenxi;Burdyshaw,Chad;Veloso,JoaoPedro;Khatamian,Alireza;Pan,Qingfei;Dhungana,Yogesh;Xie,Zhen;Risch,Isabel;Yang,Xu;Huang,Xin;Yan,Lei;Rusch,Michael;Brewer,Michael;Yan,Koon-Kiu;Chi,Hongbo;Yu,Jiyang
单细胞组学数据的稀疏性使得剖析调节细胞状态的转录和信号驱动器的布线和重新布线具有挑战性。许多驱动因子，称为“隐藏驱动因子”，由于低表达以及由翻译后和其他修饰引起的RNA和蛋白质活性之间的不一致性，难以通过常规表达分析鉴定。为了解决这个问题，我们开发了scMINER，这是一种基于互信息（MI）的计算框架，用于无监督聚类分析和细胞内网络的细胞类型特异性推断，隐藏驱动程序和来自单细胞RNA-seq数据的网络重新布线。我们设计了scMINER来捕获非线性的细胞-细胞和基因-基因关系，并推断驱动程序活动。系统基准测试表明，scMINER优于流行的单细胞聚类算法，特别是在区分相似的细胞类型方面。关于网络推断，scMINER不依赖于可用于有限的一组转录因子的结合基序，因此scMINER可以从scRNA-seq实验中为超过6，000个转录和信号传导驱动因子提供定量活性评估。作为演示，我们使用scMINER来揭示隐藏的转录和信号驱动程序，并剖析它们在免疫细胞异质性、谱系分化和组织特异性中的调节子重新布线。总的来说，基于活性的scMINER是一种广泛适用、高度准确、可重复和可扩展的方法，用于从scRNA-seq数据推断每种细胞状态下的细胞转录和信号网络。scMINER软件可通过https://github.com/jyyulab/scMINER公开访问。
The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as “hidden drivers”, are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.