SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks.
SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks.
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DOI：
10.1371/journal.pcbi.1006369
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发表时间：
2018-08
影响因子：
4.3
通讯作者：
Chen W
中科院分区：
文献类型：
--
作者：
Zhang R;Ren Z;Chen W
基因共表达网络分析在解释复杂的生物过程中非常有用。最近的基于液滴的单细胞技术能够生成更大的基因表达数据，常规地使用数千个样本和数万个基因。为了分析如此大规模的基因-基因网络，高维高斯图形模型（GGM）的严格统计推断已经取得了显著进展。这些方法为基因对的条件依赖性提供了一个正式的置信区间或p值，而不仅仅是一个单点估计，对于识别可靠的基因网络更可取。为了促进它们的广泛使用，我们在这里介绍了一个广泛而高效的R包SILGGM（大规模高斯图形模型的统计推断），其中包括四种主要的高维高斯图形模型的统计推断方法。与现有的工具不同，SILGGM对单个基因对和整体基因对都提供了统计上有效的推断。它在所有四种方法中都具有新颖且一致的错误发现率（FDR）程序。基于人性化的设计，提供兼容多平台的输出，实现交互式网络可视化。此外，仿真对比表明，SILGGM可以将现有的MATLAB实现速度提高几个数量级，并进一步提高已经非常高效的R包FastGGM的速度。模拟数据的测试结果证实了SILGGM中所有方法的有效性，即使在变量或基因数量达到万级的非常大规模的设置中也是如此。我们还将我们的包应用于泛T细胞的新型单细胞RNA-seq数据集。结果表明，SILGGM中的方法在生物学意义上明显优于传统方法。该软件包可通过CRAN （https://cran.r-project.org/package=SILGGM）免费获得。
Gene co-expression network analysis is extremely useful in interpreting a complex biological process. The recent droplet-based single-cell technology is able to generate much larger gene expression data routinely with thousands of samples and tens of thousands of genes. To analyze such a large-scale gene-gene network, remarkable progress has been made in rigorous statistical inference of high-dimensional Gaussian graphical model (GGM). These approaches provide a formal confidence interval or a p-value rather than only a single point estimator for conditional dependence of a gene pair and are more desirable for identifying reliable gene networks. To promote their widespread use, we herein introduce an extensive and efficient R package named SILGGM (Statistical Inference of Large-scale Gaussian Graphical Model) that includes four main approaches in statistical inference of high-dimensional GGM. Unlike the existing tools, SILGGM provides statistically efficient inference on both individual gene pair and whole-scale gene pairs. It has a novel and consistent false discovery rate (FDR) procedure in all four methodologies. Based on the user-friendly design, it provides outputs compatible with multiple platforms for interactive network visualization. Furthermore, comparisons in simulation illustrate that SILGGM can accelerate the existing MATLAB implementation to several orders of magnitudes and further improve the speed of the already very efficient R package FastGGM. Testing results from the simulated data confirm the validity of all the approaches in SILGGM even in a very large-scale setting with the number of variables or genes to a ten thousand level. We have also applied our package to a novel single-cell RNA-seq data set with pan T cells. The results show that the approaches in SILGGM significantly outperform the conventional ones in a biological sense. The package is freely available via CRAN at https://cran.r-project.org/package=SILGGM.
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