In hurlab/RichR: Function Enrichment analysis and Network construction

This document describes how to use the richR package. Below is a simple manual for using the package

Introduction

You can do enrichment analysis for different type of annotation data (GO,KEGG,Reactome(may need reactome.db if you work with Human). If you had bioAnno installed, you can do enrichment with all types of annotation data.

Quick tour

set.seed(123)   
library(richR)   
# To check if your the current species if supported !!!
showData()   
# Make the GO and KEGG Pathway data for your analysis
# find suitable species name by using showensemble()    
hsa_go<-buildAnnot(species="human",keytype="SYMBOL",anntype = "GO")
hsa_ko<-buildAnnot(species = "human",keytype="SYMBOL", anntype = "KEGG")

Support KEGG Module and MSIGDB anntation

hsamgi <- buildMSIGDB(species="human",keytype="SYMBOL",anntype="GO")
hsakom <- buildAnnot(species = "human",keytype="SYMBOL", anntype = "KEGGM")

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You can make annotation data from ensemble

• If had bioAnno installed, you can build annotation package with it

# library(bioAnno)
# fromKEGG(species="ath")
# athgo<-buildOwn(dbname="org.ath.eg.db",anntype="GO")  
# athko<-buildOwn(dbname="org.ath.eg.db",anntype="KEGG") 
# Please go over the bioAnno package webpage ("https://github.com/guokai8/bioAnno") to learn more

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Simple example for enrichment analysis

gene <- sample(unique(hsa_go$GeneID),1000)
res <- richGO(gene,godata = hsa_go,ontology ="BP")
head(res)
ggbar(res,top = 20,usePadj = F)

resko<-richKEGG(gene,hsa_ko,pvalue=0.05)
head(resko)
ggdot(resko,top=10,usePadj = F)

Support DAVID analysis (Online)

gene <- sample(unique(hsako$GeneID),1000)
res <- richDAVID(gene,keytype="ENTREZID",species="human")

You can also get network graphic for any type of enrichment analysis result and also combine different enrichment result

ggnetplot(res,top=20)
ggnetwork(res,top=20,weightcut = 0.01)

Directly support dplyr filter, select, mutate,group_by ... functions

filter(resko,Padj<0.05)%>%head()
select(resko,Term)

Generate figures with mutiple enrichment results for groups

gene1 <- sample(unique(hsako$GeneID),1000)
gene2 <- sample(unique(hsako$GeneID),1000)
resko1 <- richKEGG(gene1,kodata = hsako)
resko2 <- richKEGG(gene2,kodata = hsako)
res <- compareResult(list(S1=resko1,S2=resko2))
comparedot(res)

Generate figures with different enrichment results

ggnetmap(list(res,resko),top=50,visNet=TRUE,smooth=FALSE)

Contact information

For any questions please contact [e-mail]

Congratulations ! You just done all Enrichment analysis

hurlab/RichR documentation built on Feb. 5, 2024, 8:07 p.m.