Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
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DOI：
10.1101/082099
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发表时间：
2016-10
期刊：
影响因子：
9.3
通讯作者：
Thalia E. Chan;M. Stumpf;A. Babtie
中科院分区：
文献类型：
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作者：
Thalia E. Chan;M. Stumpf;A. Babtie
虽然单细胞基因表达实验给数据处理带来了新的挑战，但观察到的细胞间的可变性也揭示了信息论可以使用的统计关系。在这里，我们使用多元信息理论来探索单细胞基因表达数据集中三个基因之间的统计相关性。我们开发了PIDC，这是一种快速、高效的算法，它使用部分信息分解(PID)来识别基因之间的调控关系。我们对算法的性能进行了深入的评估，并证明了PIDC捕获的高阶信息使其在恢复模拟数据中存在的真实关系时优于基于成对互信息的算法。我们还从三个实验性的单细胞数据集中推断出基因调控网络，并说明了网络环境、分析过程中所做的选择和可变性来源如何影响网络推理。PIDC教程和用于估计PID的开源软件可在此处获得：https://github.com/Tchanders/network_inference_tutorials.PIDC应该有助于从单细胞转录数据中识别假定的功能关系和机制假说。
While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell data sets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available here: https://github.com/Tchanders/network_inference_tutorials. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.