Abstract
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.
Highlights
Gene co-expression network is an undirected graph, where each node represents a gene and each edge between two genes shows a significant co-expression relationship [1]
Unlike the marginal correlation-based approaches and high-dimensional Gaussian graphical model (GGM) estimation, there are in practice few efficient packages or algorithms for the aforementioned approaches of rigorous statistical inference with the partial correlations that are supposed to be more powerful in large-scale gene-gene network analysis
To enhance the influence of these cutting-edge statistical inference works in practical usage and address the computational challenge in high-dimensional settings even with large sample sizes, we develop a more comprehensive package called SILGGM (Statistical Inference of Large-scale Gaussian Graphical Model) that includes bivariate nodewise scaled Lasso (B_NW_SL), D-S_NW_SL, de-sparsified graphical Lasso (D-S_GL) and GFC_SL or GFC_L
Summary
Gene co-expression network is an undirected graph, where each node represents a gene and each edge between two genes shows a significant co-expression relationship [1]. Unlike the marginal correlation-based approaches and high-dimensional GGM estimation, there are in practice few efficient packages or algorithms for the aforementioned approaches of rigorous statistical inference with the partial correlations that are supposed to be more powerful in large-scale gene-gene network analysis.
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