ORA

Biomedical Knowledge Mining using GOSemSim and clusterProfiler

KEGG

keggapi

devtools::install_github("BioinfoFungi/enrichplot2")

library(clusterProfiler)
library(org.Mm.eg.db)
library(KEGG.db)

annotations_orgDb <- AnnotationDbi::select(org.Mm.eg.db, 
                                     keys = rownames(wt_control_deg),  
                                     columns = c("ENTREZID","GENENAME"), 
                                     keytype = "SYMBOL")
kegg<- clusterProfiler::enrichKEGG(gene =annotations_orgDb$ENTREZID,  organism = "mmu",
                                    keyType = "SYMBOL",
                                    pvalueCutoff = 0.05,
                                    pAdjustMethod = "BH",
                                    minGSSize = 10,
                                    maxGSSize = 500,
                                    qvalueCutoff = 10000,
                                    use_internal_data = TRUE)
kegg <- setReadable(kegg, 'org.Mm.eg.db', 'ENTREZID')
png(filename = paste0(wt_control_vs_ko_control,"/KEGG.png"),width = 7,height = 6,res = 300,units = "in")
dotplot(kegg,showCategory=20,color="pvalue")
dev.off()
png(filename = paste0(wt_control_vs_ko_control,"/heat.png"),width = 10,height =5,res = 300,units = "in")
heatplot(kegg,showCategory=20,color="pvalue")
dev.off()
write_tsv(arrange(kegg@result,pvalue), file=paste0(wt_control_vs_ko_control,"/KEGG.tsv"))

注意pvalueCutoff=10000，dotplot默认使用在enrichKEGG函数中设置的pvalueCutoff = 0.05筛选显著富集的通路。及使用0.05的阈值筛选矫正后的pvalue，进行可视化的。因此如果基因太少则不能富集到通路，此时可以将改值调大，或者修改dotplot函数的源码实现根据pvalue进行矫正。有关dotplot源码相关内容见enrichplot源码阅读。

Previous Next

查看感兴趣基因与通路基因的交集

suppressMessages(library(KEGG.db))
suppressMessages(library(clusterProfiler))
suppressMessages(library(org.Mm.eg.db))
ls("package:KEGG.db")
frame = toTable(KEGGPATHID2EXTID)
x <- as.list(KEGG.db::KEGGPATHID2EXTID)
x["mmu04020"][[1]] |> length()
annotations <- AnnotationDbi::select(org.Mm.eg.db, 
                                     keys = x["mmu04020"][[1]] ,  
                                     columns = c("ENTREZID","SYMBOL"), 
                                     keytype = "ENTREZID") |> 
    na.omit()
annotations$SYMBOL |> length()
intersect(rownames(deg),annotations_foldchanges$SYMBOL)
ggvenn(
    list("Calcium-signaling-pathway" = annotations_foldchanges$SYMBOL ,"mcherry-PKH vs mcherry deg" = rownames(deg) ),
    stroke_size = 0.6, set_name_size = 2.8, text_size = 5,show_percentage=F
)

GO KEGG

library(clusterProfiler)
library(org.Hs.eg.db)
entrez_id <- bitr(top20_gene, fromType = "SYMBOL",
                    toType = c("ENTREZID"),
                    OrgDb = org.Hs.eg.db)%>%
                    pull("ENTREZID")%>%unique()
kegg<- clusterProfiler::enrichKEGG(gene =entrez_id,organism = 'hsa',pAdjustMethod="none")
kegg <- setReadable(kegg, 'org.Hs.eg.db', 'ENTREZID')
png(filename = "figure/KEGG.png",width = 7,height = 6,res = 300,units = "in")
dotplot(kegg,showCategory=20)
dev.off()
ego<-enrichGO(gene = entrez_id,OrgDb = org.Hs.eg.db,keyType = 'ENTREZID',ont = 'all',readable = T ,pAdjustMethod="BH")
png(filename = "figure/GO.png",width = 10,height = 9,res = 300,units = "in")
colorSel="qvalue"
dotplot(ego,showCategory = 10,split="ONTOLOGY",orderBy = "GeneRatio", color = colorSel) + facet_grid(ONTOLOGY~., scale='free')
dev.off()

KEGG本地化

https://github.com/YuLab-SMU/createKEGGdb

remotes::install_github("YuLab-SMU/createKEGGdb")
library(createKEGGdb)
species <-c("hsa","rno","mmu")
create_kegg_db(species)
install.packages("./KEGG.db_1.0.tar.gz", repos=NULL)
library(KEGG.db)
library(clusterProfiler)

data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]

kk <- enrichKEGG(gene         = gene,
                 organism     = 'hsa',
                 pvalueCutoff = 0.05,
                 qvalueCutoff = 0.05,
                 use_internal_data =T)

合并GO结果

ALL

BP

Reference

• http://software.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats
• https://guangchuangyu.github.io/cn/2018/11/msigdf_clusterprofiler/
• https://clauswilke.com/
• http://www.bioconductor.org/packages/release/bioc/vignettes/GSVA/inst/doc/GSVA.html