-
enrichplot
让你们对
cluste
rProfiler
系列包无法自拔
先来一段搞笑的街头卖膏药的视频，大家可以娱乐一下
再继续往下看
，因为我接下来就是要卖
Y
叔版「膏药」
！大
家喜欢
clusterProfiler
，除了功能强大、支持广泛之外，我想
还有一点必须是可视化，毕竟大家都是视
觉动物，颜值即正
义。然而这些都是我早期的代码，我其实一直想重新写，希
望可以全部用
gplot2
来实现，方便后续维护
、更好看、更强
大。而这在我博士毕业后，终于找了个时间重写了一遍，并
且也加入了部分新的图形，我把这些代码重新打包，已经在
Biocondu
ctor
上，叫
enrichplot
。有了这个包，你们更加对
clusterProfiler
系
列包无法自拔，
让其它的工具毫无颜色。
The
enrichplot package implements several
methods for enrichment
result
visualization to help interpretation. It supports
both
hypergeometric test and gene set
enrichment analysis. Both of
them are
widely used to characterize pathway/function
relationships to elucidate molecular
mechanisms from
high-throughput genomic
enrichplot package supports
visualizing
enrichment results obtained from
DOSE
(Yu et al. 2015), clusterProfiler (Yu et al.
2012),
ReactomePA (Yu and He 2016) and
d GO DAG
graphGene Ontology (GO) is
organized as a directed acyclic
graph.
An insighful way of looking at the results of the
analysis
is to investigate how the
significant GO terms are distributed
over the GO graph. The goplot function
shows subgraph
induced by most
significant GO y(clusterProfiler)
data(geneList, package='DOSE')
de abs(geneList) > 2]
ego
'', ont='BP', readable=TRUE)
library(enrichplot)
goplot(ego)
Bar plotBar plot
is the most widely used method to visualize
enriched terms. It depicts the
enrichment scores (e.g. p values)
and
gene count or ratio as bar height and t(ego,
showCategory=20)
Dot plotDot
plot is similar to bar plot with the capability to
encode another score as dot size. Both
barplot and dotplot
supports facetting
to visualize sub-ontologies
t(ego,
showCategory=30)
go '', ont='all')
dotplot(go, split='ONTOLOGY') +
facet_grid(ONTOLOGY~.,
scale='free')
Gene-Concept NetworkBoth the barplot
and dotplot only
displayed most
significant enriched terms, while users may want