Easy and efficient ensemble gene set testing with EGSEA.
Easy and efficient ensemble gene set testing with EGSEA.
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DOI：
10.12688/f1000research.12544.1
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发表时间：
2017
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影响因子：
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通讯作者：
Ritchie ME
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文献类型：
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作者：
Alhamdoosh M;Law CW;Tian L;Sheridan JM;Ng M;Ritchie ME
基因集富集分析是一种流行的方法，用于对基因组数据集中受到干扰的生物过程进行优先级排序。 Bioconductor 项目拥有 80 多个能够进行基因组分析的软件包。这些软件包中的大多数都在差异调节基因中寻找丰富的特征，以揭示更高水平的生物学主题，而当仅关注单个基因的证据时可能会错过这些主题。由于提供了如此多的不同方法，选择最佳算法和可视化方法可能具有挑战性。 EGSEA 软件包通过组合多达 12 个著名基因集测试算法的结果来解决这个问题，以获得生物学相关结果的共识排名。该工作流程演示了 EGSEA 如何使用对研究乳腺癌起源重要的 3 个不同细胞群进行分析的实验，扩展基于 limma 的 RNA-seq 和微阵列数据的差异表达分析。在数据标准化和建立适当的差异表达分析线性模型之后，EGSEA 构建了基因特征特定索引，将从 MSigDB、GeneSetDB 和 KEGG 获得的各种小鼠或人类基因集集合与正在研究的基因表达数据联系起来。然后配置 EGSEA 并运行集成富集分析，返回一个可以使用多种 S4 方法查询的对象，对基因集进行排序，并通过热图、KEGG 路径视图、GO 图、散点图和条形图可视化结果。 最后，结合这些显示的 HTML 报告可以快速跟踪与合作者共享结果，从而加快下游生物验证。 EGSEA 使用简单，可以轻松与人类和小鼠数据的现有基因表达分析流程集成。
Gene set enrichment analysis is a popular approach for prioritising the biological processes perturbed in genomic datasets. The Bioconductor project hosts over 80 software packages capable of gene set analysis. Most of these packages search for enriched signatures amongst differentially regulated genes to reveal higher level biological themes that may be missed when focusing only on evidence from individual genes. With so many different methods on offer, choosing the best algorithm and visualization approach can be challenging. The EGSEA package solves this problem by combining results from up to 12 prominent gene set testing algorithms to obtain a consensus ranking of biologically relevant results.This workflow demonstrates how EGSEA can extend limma-based differential expression analyses for RNA-seq and microarray data using experiments that profile 3 distinct cell populations important for studying the origins of breast cancer. Following data normalization and set-up of an appropriate linear model for differential expression analysis, EGSEA builds gene signature specific indexes that link a wide range of mouse or human gene set collections obtained from MSigDB, GeneSetDB and KEGG to the gene expression data being investigated. EGSEA is then configured and the ensemble enrichment analysis run, returning an object that can be queried using several S4 methods for ranking gene sets and visualizing results via heatmaps, KEGG pathway views, GO graphs, scatter plots and bar plots. Finally, an HTML report that combines these displays can fast-track the sharing of results with collaborators, and thus expedite downstream biological validation. EGSEA is simple to use and can be easily integrated with existing gene expression analysis pipelines for both human and mouse data.