Closed ixxmu closed 2 years ago
在国内做生信的小伙伴或多或少都听过一个大名鼎鼎的名字---Y叔,想当初小编初入生信大门时还专门百度查了一下,他就是南方医科大学生物信息学系主任余光创教授。Y叔在国际也有很高的知名度,以至于ClusterProfiler 4在发出后有很多国际知名生信学家评论。
生物信息学软件很多都死在故纸堆里,发表文章之后,就走向毁灭,这叫publish and perish。clusterProfiler之所以能够成功,因为Y叔对它充满感情,10年如一日地维护,但是自2012年发表一篇文章以来,虽然软件一直在更新,但相应的文章一直没有再发一版。时隔9年,Y叔终于再出一篇文章,而且是把论文写在祖国大地上,clusterProfiler 4.0发表在中科院青促会主办的期刊The Innovation。下面附上文章信息:T Wu#, E Hu#, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo*, G Yu*. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141. doi: 10.1016/j.xinn.2021.100141。
截至目前,第一版的ClusterProfiler已经被引用了5000次,文章在G Yu, LG Wang, Y Han and QY He*. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287. doi: 10.1089/omi.2011.0118,国内很难再有一个单独R包可以达到这个高度。那么,新版的ClusterProfiler 4.0又给我们带来什么样的惊喜你额,今天小编就来简单介绍一下这个伟大R包的新版本。
ClusterProfiler最经典、最重要的一个功能就是GO富集分析了,新版的GO分析有groupGO(),enricogo()和gseGO()三个函数,而且支持OrgDb收录的所有物种。下面使用DOSE包中的geneList作为示例进行演示
library(clusterProfiler)
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
head(gene)
## [1] "4312" "8318" "10874" "55143" "55388" "991"
ego <- enrichGO(gene = gene,
universe = names(geneList),
OrgDb = org.Hs.eg.db,
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05,
readable = TRUE)#将输入基因转为Symbol
head(ego)
#因为有一些Symbol对应多个ENSEMBL,可以直接用ID进行富集分析
gene.df <- bitr(gene, fromType = "ENTREZID",
toType = c("ENSEMBL", "SYMBOL"),
OrgDb = org.Hs.eg.db)
ego2 <- enrichGO(gene = gene.df$ENSEMBL,
OrgDb = org.Hs.eg.db,
keyType = 'ENSEMBL',
ont = "CC",
pAdjustMethod = "BH",
pvalueCutoff = 0.01,
qvalueCutoff = 0.05)
head(ego2, 3)
#下面就是新增的GSEA富集分析了,需要同时输入基因名和变化倍数
ego3 <- gseGO(geneList = geneList,
OrgDb = org.Hs.eg.db,
ont = "CC",
minGSSize = 100,#限制最少基因数
maxGSSize = 500,#限制最多基因数
pvalueCutoff = 0.05,
verbose = FALSE)
#最后就是将富集的通路做一个有向无环图进行可视化了
goplot(ego)
从这个图中我们可以去除冗余通路的信息,找到关键通路的上下游,从而更准确的定位到关键信号通路。
KEGG相对于GO更加准确囊括了最关键的通路信息,在很多情况下都是我们最想关注的通路信号,下面就演示一下KEGG的通路富集分析。
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
kk <- enrichKEGG(gene = gene,
organism = 'hsa',
pvalueCutoff = 0.05)
head(kk)
kk2 <- gseKEGG(geneList = geneList,
organism = 'hsa',
minGSSize = 120,
pvalueCutoff = 0.05,
verbose = FALSE)
head(kk2)
#上面两个是和GO富集分析一样的常规操作,对于KEGG,还增添了对通路的模块分析,这对揭示一些功能单元有更直接的效果。
mkk <- enrichMKEGG(gene = gene,
organism = 'hsa',
pvalueCutoff = 1,
qvalueCutoff = 1)
head(mkk)
mkk2 <- gseMKEGG(geneList = geneList,
organism = 'hsa',
pvalueCutoff = 1)
head(mkk2)
#KEGG富集分析的可视化
browseKEGG(kk, 'hsa04110')
#BiocManager::install("pathview")
library("pathview")
hsa04110 <- pathview(gene.data = geneList,
pathway.id = "hsa04110",
species = "hsa",
limit = list(gene=max(abs(geneList)), cpd=1))
比如我们想研究hsa04110(Cell cycle)这个通路,通过对关键通路进行可视化我们可以找到差异变化的基因在通路中的位置和作用。
GO和KEGG是我们最常用的基因集,但是还有一些其它揭示细胞某些特征的基因集,如:WikiPathways,Reactome,Mesh和Disease等,在新版本的clusterProfiler 4都可以基于以上基因集进行富集分析。
#------------------WikiPathways----------------
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
enrichWP(gene, organism = "Homo sapiens")
gseWP(geneList, organism = "Homo sapiens")
#---------------------Reactome------------------
library(ReactomePA)
data(geneList, package="DOSE"
)
de <- names(geneList)[abs(geneList) > 1.5]
head(de)
x <- enrichPathway(gene=de, pvalueCutoff = 0.05, readable=TRUE)
y <- gsePathway(geneList,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
#可视化
viewPathway("E2F mediated regulation of DNA replication",
readable = TRUE,
foldChange = geneList)
#---------------------Mesh------------------
library(meshes)
data(geneList, package="DOSE")
de <- names(geneList)[1:100]
x <- enrichMeSH(de, MeSHDb = db, database='gendoo', category = 'C')
y <- gseMeSH(geneList, MeSHDb = db, database = 'gene2pubmed', category = "G")
#---------------------Disease------------------
library(DOSE)
data(geneList)
gene <- names(geneList)[abs(geneList) > 1.5]
head(gene)
x <- enrichDO(gene = gene,
ont = "DO",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe = names(geneList),
minGSSize = 5,
maxGSSize = 500,
qvalueCutoff = 0.05,
readable = FALSE)
y <- gseDO(geneList,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
#专门对癌基因进行富集分析
gene2 <- names(geneList)[abs(geneList) < 3]
ncg <- enrichNCG(gene2)
head(ncg)
上述提到的基因集都是非常经典的基因集,而在实际应用中我们还需要对基于自己数据构建的基因集进行富集分析,clusterProfiler 4的EnrichR函数就能实现这个目的。
data(geneList, package="DOSE")
head(geneList)
## 4312 8318 10874 55143 55388 991
## 4.572613 4.514594 4.418218 4.144075 3.876258 3.677857
gene <- names(geneList)[abs(geneList) > 2]
head(gene)
## [1] "4312" "8318" "10874" "55143" "55388" "991"
#cell marker
cell_marker_data <- vroom::vroom('http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Human_cell_markers.txt')
## instead of `cellName`, users can use other features (e.g. `cancerType`)
cells <- cell_marker_data %>%
dplyr::select(cellName, geneID) %>%
dplyr::mutate(geneID = strsplit(geneID, ', ')) %>%
tidyr::unnest()
x <- enricher(gene, TERM2GENE = cells)
y <- GSEA(geneList, TERM2GENE = cells)
要说这次更新最大的亮点,就是接下来要说的多组平行样本间的富集分析。对于一般只有两组实验(实验组和对照组),我们只需要计算出差异基因直接进行富集分析即可。但是实际实验中,如药物处理的样本,我们往往会设置多个梯度的平行分组,如何同时对所有组直接进行比较,clusterProfiler 4给出了完美的解决方式。
data(gcSample)
str(gcSample) #这里有8组数据
## List of 8
## $ X1: chr [1:216] "4597" "7111" "5266" "2175" ...
## $ X2: chr [1:805] "23450" "5160" "7126" "26118" ...
## $ X3: chr [1:392] "894" "7057" "22906" "3339" ...
## $ X4: chr [1:838] "5573" "7453" "5245" "23450" ...
## $ X5: chr [1:929] "5982" "7318" "6352" "2101" ...
## $ X6: chr [1:585] "5337" "9295" "4035" "811" ...
## $ X7: chr [1:582] "2621" "2665" "5690" "3608" ...
## $ X8: chr [1:237] "2665" "4735" "1327" "3192" ...
ck <- compareCluster(geneCluster = gcSample, fun = enrichKEGG)
ck <- setReadable(ck, OrgDb = org.Hs.eg.db, keyType="ENTREZID")
mydf <- data.frame(Entrez=names(geneList), FC=geneList)
mydf <- mydf[abs(mydf$FC) > 1,]
mydf$group <- "upregulated"
mydf$group[mydf$FC < 0] <- "downregulated"
mydf$othergroup <- "A"
mydf$othergroup[abs(mydf$FC) > 2] <- "B"
formula_res <- compareCluster(Entrez~group+othergroup, data=mydf, fun="enrichKEGG")
head(formula_res)
#对结果进行可视化
dotplot(ck)
dotplot(formula_res)
如果你觉得这样不方便观察,还可以分开作图
dotplot(formula_res, x="group") + facet_grid(~othergroup)
#我们还可以对所有基因做一个网状图
cnetplot(ck)
上面对clusterProfiler 4所有的功能做了个简要的介绍,大家还可以通过阅读原文去深入学习。最后我们对新版的clusterProfiler的可视化功能做一个总结。
library(DOSE)
data(geneList)
de <- names(geneList)[abs(geneList) > 2]
edo <- enrichDGN(de)
library(enrichplot)
barplot(edo, showCategory=20)
mutate(edo, qscore = -log(p.adjust, base=10)) %>%
barplot(x="qscore")
## convert gene ID to Symbol
edox <- setReadable(edo, 'org.Hs.eg.db', 'ENTREZID')
p1 <- cnetplot(edox, foldChange=geneList)
## categorySize can be scaled by 'pvalue' or 'geneNum'
p2 <- cnetplot(edox, categorySize="pvalue", foldChange=geneList)
p3 <- cnetplot(edox, foldChange=geneList, circular = TRUE, colorEdge = TRUE)
p1|p2|p3
p1 <- cnetplot(edox, node_label="category",
cex_label_category = 1.2)
p2 <- cnetplot(edox, node_label="gene",
cex_label_gene = 0.8)
p3 <- cnetplot(edox, node_label="all")
p4 <- cnetplot(edox, node_label="none",
color_category='firebrick',
color_gene='steelblue')
cowplot::plot_grid(p1, p2, p3, p4, ncol=2, labels=LETTERS[1:4])
set.seed(123)
x <- list(A = letters[1:10], B=letters[5:12], C=letters[sample(1:26, 15)])
p1 <- cnetplot(x)
set.seed(123)
d <- setNames(rnorm(26), letters)
p2 <- cnetplot(x, foldChange=d) +
scale_color_gradient2(name='associated data', low='darkgreen', high='firebrick')
cowplot::plot_grid(p1, p2, ncol=2, labels=LETTERS[1:2])
p1 <- heatplot(edox, showCategory=5)
p2 <- heatplot(edox, foldChange=geneList, showCategory=5)
cowplot::plot_grid(p1, p2, ncol=1, labels=LETTERS[1:2])
edox2 <- pairwise_termsim(edox)
p1 <- treeplot(edox2)
p2 <- treeplot(edox2, hclust_method = "average")
aplot::plot_list(p1, p2, tag_levels='A')
edo <- pairwise_termsim(edo)
p1 <- emapplot(edo)
p2 <- emapplot(edo, cex_category=1.5)
p3 <- emapplot(edo, layout="kk")
p4 <- emapplot(edo, cex_category=1.5,layout="kk")
cowplot::plot_grid(p1, p2, p3, p4, ncol=2, labels=LETTERS[1:4])
library(clusterProfiler)
data(gcSample)
xx <- compareCluster(gcSample, fun="enrichKEGG",
organism="hsa", pvalueCutoff=0.05)
xx <- pairwise_termsim(xx)
p1 <- emapplot(xx)
p2 <- emapplot(xx, legend_n=2)
p3 <- emapplot(xx, pie="count")
p4 <- emapplot(xx, pie="count", cex_category=1.5, layout="kk")
cowplot::plot_grid(p1, p2, p3, p4, ncol=2, labels=LETTERS[1:4])
upsetplot(edo)
upsetplot(kk2)
ridgeplot(edo2)
p1 <- gseaplot(edo2, geneSetID = 1, by = "runningScore", title = edo2$Description[1])
p2 <- gseaplot(edo2, geneSetID = 1, by = "preranked", title = edo2$Description[1])
p3 <- gseaplot(edo2, geneSetID = 1, title = edo2$Description[1])
cowplot::plot_grid(p1, p2, p3, ncol=1, labels=LETTERS[1:3])
gseaplot2(edo2, geneSetID = 1, title = edo2$Description[1])
gseaplot2(edo2, geneSetID = 1:3)
p1 <- gseaplot2(edo2, geneSetID = 1:3, subplots = 1)
p2 <- gseaplot2(edo2, geneSetID = 1:3, subplots = 1:2)
cowplot::plot_grid(p1, p2, ncol=1, labels=LETTERS[1:2])
library(ggplot2)
library(cowplot)
pp <- lapply(1:3, function(i) {
anno <- edo2[i, c("NES", "pvalue", "p.adjust")]
lab <- paste0(names(anno), "=", round(anno, 3), collapse="\n")
gsearank(edo2, i, edo2[i, 2]) + xlab(NULL) +ylab(NULL) +
annotate("text", 10000, edo2[i, "enrichmentScore"] * .75, label = lab, hjust=0, vjust=0)
})
plot_grid(plotlist=pp, ncol=1)
terms <- edo$Description[1:5]
p <- pmcplot(terms, 2010:2020)
p2 <- pmcplot(terms, 2010:2020, proportion=FALSE)
plot_grid(p, p2, ncol=2)
高通量组学数据功能解读中,功能富集分析是至关重要的一步,相关软件繁多但大多数仅针对极少量的模式生物开发,无法支持大量非模式生物的分析诉求。功能分析依赖准确的功能注释,但许多软件在发表文章之后并未及时更新内置的功能注释。2016年,Nature Methods文章指出,高达42%的相关工具内置注释超过五年未更新,用户基于此类工具的数据挖掘,结论反应的仅是学界五年前的生物学知识积累,颇有时光倒流的感觉。尤为重要的是,基于旧有注释,大约只能捕获到最新数据库中26%的生物学过程或通路。
ClusterProfiler 4相对于前一版不仅更新了参考基因集,而且新加入了很多功能,如GSEA分析、DO富集分析以及多组间样本的平行比较等。新版本尤其实现多组数据间自由比较,如不同条件、处理等,并内置系列流行辅助工具,如数据处理包dplyr、可视化包ggplot2等,方便分析人员用熟悉的方式自由探索,实现数据高效解读。目前,clusterProfiler已被整合进超过30个的同行分析软件中,致力于解决不同场景下的功能分析,相信clusterProfiler4.0未来将发挥更大的作用,助力研究者更高效地解读生物医学数据及建立更可靠的机制假说。
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