README.md

In guokai8/richR: Function Enrichment analysis and Network construction

richR

Description

richR provide functions richGO, richKEGG,and enrich to do functional enrichment analysis.

Installation

library(devtools)
install_github("guokai8/richR")

Quick tour

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

You can make annotation data for all species from bioAnno package

• 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

Simple example for enrichment analysis

```{r,fig.height=6,fig.width=6,fig.align="center",dpi=100} gene <- sample(unique(hsago$GeneID),1000) resgo <- richGO(gene,godata = hsago,ontology ="BP") head(resgo) ggbar(resgo,top = 20,usePadj = F)

extract gene and related term

head(detail(resgo))

### cluster GO enrichment result
```{r}
resc<-richCluster(resgo)
ggdot(resc)

```{r,fig.height=6,fig.width=6,fig.align="center"} resko<-richKEGG(gene,hsako,pvalue=0.05) head(resko) ggdot(resko,top=10,usePadj = F)

GSEA

set.seed(123) hsako <- buildAnnot(species="human",keytype="SYMBOL",anntype = "KEGG") name <- sample(unique(hsako$GeneID),1000) gene<-rnorm(1000) names(gene) <- name res <- richGSEA(gene,object = hsako)

### 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
```{r,fig.height=6,fig.width=6,fig.align="center",dpi=100}
ggnetplot(resko,top=20)
ggnetwork(resgo,top=20,weightcut = 0.01)

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

library(dplyr)
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(resgo,resko),top=50,visNet=TRUE,smooth=FALSE)

Contact information

For any questions please contact [e-mail]

guokai8/richR documentation built on June 10, 2025, 4:51 a.m.