Gene-set analysis tools, which make use of curated sets of molecules grouped based on their shared functions, aim to identify which gene-sets are over-represented in the set of features that have been associated with a given trait of interest. Such tools are frequently used in gene-centric approaches derived from RNA-sequencing or microarrays such as Ingenuity or GSEA, but they have also been adapted for interval-based analysis derived from DNA methylation or ChIP/ATAC-sequencing. Gene-set analysis tools return, as a result, a list of significant gene-sets. However, while these results are useful for the researcher in the identification of major biological insights, they may be complex to interpret because many gene-sets have largely overlapping gene contents. Additionally, in many cases the result of gene-set analysis consists of a large number of gene-sets making it complicated to identify the major biological insights. We present GeneSetCluster, a novel approach which allows clustering of identified gene-sets, from one or multiple experiments and/or tools, based on shared genes. GeneSetCluster calculates a distance score based on overlapping gene content, which is then used to cluster them together and as a result, GeneSetCluster identifies groups of gene-sets with similar gene-set definitions (i.e. gene content). These groups of gene-sets can aid the researcher to focus on such groups for biological interpretations. GeneSetCluster is a novel approach for grouping together post gene-set analysis results based on overlapping gene content. GeneSetCluster is implemented as a package in R. The package and the vignette can be downloaded at https://github.com/TranslationalBioinformaticsUnit

中文翻译：

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GeneSetCluster：用于汇总和整合基因组分析结果的工具

基因组分析工具利用了基于共享功能分组的分子精选集，旨在识别哪些基因组在与给定特征相关的一组特征中被过度表达。此类工具常用于衍生自RNA测序或微阵列（如Ingenuity或GSEA）的以基因为中心的方法中，但也适用于源自DNA甲基化或ChIP / ATAC测序的基于间隔的分析。结果，基因组分析工具会返回重要的基因组列表。然而，尽管这些结果对于研究人员在鉴定主要生物学见解方面很有用，但由于许多基因集的基因含量存在很大的重叠，因此解释起来可能很复杂。另外，在许多情况下，基因组分析的结果由大量的基因组组成，这使得鉴定主要生物学见解变得很复杂。我们介绍了GeneSetCluster，这是一种新颖的方法，可基于一个或多个实验和/或工具，基于共享的基因对已识别的基因集进行聚类。GeneSetCluster基于重叠的基因内容计算距离得分，然后将其聚类在一起，因此，GeneSetCluster可以识别具有相似基因集定义（即基因含量）的基因集组。这些基因组可以帮助研究人员集中精力进行生物学解释。GeneSetCluster是一种基于重叠基因内容将基因组分析后结果分组在一起的新颖方法。GeneSetCluster在R中作为一个包实现。