Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
Division of Biostatistics, Department of Public Health Science, University of Miami Miller School of Medicine, Miami, FL, USA.
Bioinformatics. 2018 Sep 15;34(18):3220-3222. doi: 10.1093/bioinformatics/bty317.
Pathway analysis of alternative splicing would be biased without accounting for the different number of exons or junctions associated with each gene, because genes with higher number of exons or junctions are more likely to be included in the 'significant' gene list in alternative splicing. We present PathwaySplice, an R package that (i) Performs pathway analysis that explicitly adjusts for the number of exons or junctions associated with each gene; (ii) visualizes selection bias due to different number of exons or junctions for each gene and formally tests for presence of bias using logistic regression; (iii) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets; (iv) identifies the significant genes driving pathway significance and (v) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph.
https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html.
Supplementary data are available at Bioinformatics online.
如果不对每个基因相关的外显子或连接点数量进行分析，对可变剪接的通路分析将会产生偏差，因为具有更多外显子或连接点的基因更有可能被包含在可变剪接的“显著”基因列表中。我们提出了 PathwaySplice，这是一个 R 包，（i）执行通路分析，明确调整与每个基因相关的外显子或连接点的数量；（ii）可视化由于每个基因的外显子或连接点数量不同而导致的选择偏差，并使用逻辑回归正式测试是否存在偏差；（iii）支持基于基因本体论术语的基因集，以及更广泛定义的基因集（例如 MSigDB）或用户定义的基因集；（iv）识别驱动通路显著性的显著基因；（v）使用富集图谱组织显著通路，其中具有大量重叠基因的通路在网络图中被分组在一起。
https://bioconductor.org/packages/release/bioc/html/PathwaySplice.html。
补充数据可在 Bioinformatics 在线获取。
