ArticlePLoS computational biology2018
SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Article
- Single-cell analysis of gene regulatory networks in the mammary glands of P4HA1-knockout mice.PLoS genetics · 2025Article
- AJGM: joint learning of heterogeneous gene networks with adaptive graphical model.Bioinformatics (Oxford, England) · 2025Article
- NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.Bioinformatics advances · 2025Article
- ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks.Briefings in bioinformatics · 2024Article
- Spatial Cellular Networks from omics data with SpaCeNet.Genome research · 2024Article
- SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.Cell reports methods · 2024Article
- Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.Nature communications · 2023Article
- Structure learning for gene regulatory networks.PLoS computational biology · 2023Article
- Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.G3 (Bethesda, Md.) · 2023Article
- Gaussian graphical models with applications to omics analyses.Statistics in medicine · 2022Article
- Information enhanced model selection for Gaussian graphical model with application to metabolomic data.Biostatistics (Oxford, England) · 2022Article
- Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data.Journal of the American Statistical Association · 2022Article
- NetCoMi: network construction and comparison for microbiome data in R.Briefings in bioinformatics · 2021Article
- LARGE-SCALE MULTIPLE INFERENCE OF COLLECTIVE DEPENDENCE WITH APPLICATIONS TO PROTEIN FUNCTION.The annals of applied statistics · 2021Article
- Network Analysis of Gene Transcriptions ofGenes · 2021Article
- Comparing Statistical Tests for Differential Network Analysis of Gene Modules.Frontiers in genetics · 2021Article
- Capturing the transcription factor interactome in response to sub-lethal insecticide exposure.Current research in insect science · 2021Article
- Transcriptome-wide and differential expression network analyses of childhood asthma in nasal epithelium.The Journal of allergy and clinical immunology · 2020Article
- Signaling networks in inflammatory pathways and risk for suicidal behavior.Brain, behavior, & immunity - health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.