Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions
Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions
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DOI：
10.1007/978-1-4939-9224-9_10
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Babtie, Ann C.
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文献类型：
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作者：
Chan, Thalia E.;Stumpf, Michael P. H.;Babtie, Ann C.
单细胞实验技术现在允许我们量化多达数千个单个细胞中的基因表达。这些数据揭示了细胞在发育过程中发生的转录状态的变化，并采用专门的细胞命运。在本章中，我们将详细介绍如何使用我们的网络推理算法（PIDC）和相关的软件包NetworkInference。从观察到的基因表达模式推断基因之间的功能相互作用。我们利用单细胞数据的大样本量和固有的变异性来检测基因之间的统计依赖性，这些基因表明假定的（共）调节关系，使用可以捕获复杂统计关系的多变量信息度量。我们提供了如何最好地结合联合收割机这种分析与其他互补的方法，旨在探索单细胞数据，以及如何解释所产生的基因调控网络模型，以深入了解调节细胞分化的过程。
Single cell experimental techniques now allow us to quantify gene expression in up to thousands of individual cells. These data reveal the changes in transcriptional state that occur as cells progress through development and adopt specialized cell fates. In this chapter we describe in detail how to use our network inference algorithm (PIDC)-and the associated software package NetworkInference. jl-to infer functional interactions between genes from the observed gene expression patterns. We exploit the large sample sizes and inherent variability of single cell data to detect statistical dependencies between genes that indicate putative (co-)regulatory relationships, using multivariate information measures that can capture complex statistical relationships. We provide guidelines on how best to combine this analysis with other complementary methods designed to explore single cell data, and how to interpret the resulting gene regulatory network models to gain insight into the processes regulating cell differentiation.