一样的函数，不一样的upset

upsetplot大家见得多，首先来个富集分析的实例：

library(DOSE)
data(geneList)
de <- names(geneList)[abs(geneList) > 2]
edo <- enrichDGN(de)

library(enrichplot)
upsetplot(edo)

在v >= 1.5.2版本的enrichplot中，我加入了对GSEA结果的支持，于是你现在也可以用upsetplot来画，但风格不一样哦。

再来个实例：

library(clusterProfiler
kk2 <- gseKEGG(geneList     = geneList,
               organism     = 'hsa',
               nPerm        = 1000,
               minGSSize    = 120,
               pvalueCutoff = 0.05,
               verbose      = FALSE)
upsetplot(kk2) 

Gene set variation analysis (GSVA). Pathway analyses were predominantly performed on the 50 hallmark pathways described in the molecular signature database 16 , exported using the GSEABase package (version 1.36.0). We also assessed metabolic pathway activities using a described curated dataset 48 . To reduce pathway overlaps and pathway redundancies, each gene set associated with a pathway was trimmed to only contain unique genes, and all genes associated to two or more pathways were removed. Most gene sets retained >70% of their associated genes. Next, to assign pathway activity estimates to individual cells, we applied GSVA 49 using standard settings, as implemented in the GSVA package (version 1.22.4).

针对上面这篇文章所采用的策略，似乎我们现在所呈现的图，更好，也能给出更多的信息，你可能还可以发现一些有趣的cross-talk。PS:画图的集合，也是可选的。

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