Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
Bioinformatics. 2019 Jun 1;35(11):1958-1959. doi: 10.1093/bioinformatics/bty892.
An important downstream analysis following differential expression from RNA sequencing (RNA-Seq) or DNA methylation analysis is the gene set testing to relate significant genes or CpGs to known biological properties. However, the traditional gene set testing approaches result in biased P-values due to the difference in gene length. Existing methods accounting for length bias were primarily developed for RNA-Seq data. For DNA methylation data profiled using the Illumina arrays, separate methods adjusting for the number of CpGs instead of gene length are necessary.
We developed methylGSA, a Bioconductor package for gene set testing in DNA methylation data. Our accompanying Shiny app provides an interactive way of accessing functions and visualizing the results in methylGSA package.
methylGSA is available at Bioconductor repository: https://bioconductor.org/packages/methylGSA and Shiny app is available at: http://www.ams.sunysb.edu/%7epfkuan/softwares.html#methylGSA.
Supplementary data are available at Bioinformatics online.
RNA 测序（RNA-Seq）或 DNA 甲基化分析后进行差异表达的一个重要下游分析是基因集测试，即将显著基因或 CpG 与已知的生物学特性相关联。然而，由于基因长度的差异，传统的基因集测试方法会导致偏置的 P 值。现有的考虑长度偏差的方法主要是为 RNA-Seq 数据开发的。对于使用 Illumina 阵列进行 DNA 甲基化数据的分析，需要单独的方法来调整 CpG 的数量，而不是基因长度。
我们开发了 methylGSA，这是一个用于 DNA 甲基化数据中基因集测试的 Bioconductor 包。我们的附带 Shiny 应用程序提供了一种交互式的方式来访问函数并可视化 methylGSA 包中的结果。
methylGSA 可在 Bioconductor 存储库中获得：https://bioconductor.org/packages/methylGSA，Shiny 应用程序可在：http://www.ams.sunysb.edu/%7epfkuan/softwares.html#methylGSA。
补充数据可在 Bioinformatics 在线获得。
