7 Functional Enrichment Analysis

sclet provides RunEnrichment to seamlessly perform GO/KEGG enrichment analysis on marker genes.

7.1 Load data and find markers

library(sclet)
sce <- readRDS("data/pancreas_sub_sce.rds")
# Find markers for all clusters
markers <- FindAllMarkers(sce, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

## Calculating markers for all clusters using presto...

head(markers)

##                pval         padj avg_log2FC pct.1 pct.2
## Irx1   5.512395e-89 8.796681e-85   1.642432 0.615 0.035
## Tmem27 2.513287e-80 2.005352e-76  10.780989 0.985 0.280
## Peg10  4.295250e-73 2.284787e-69   3.802655 0.741 0.112
## Irx2   2.194301e-71 8.754163e-68   1.730250 0.637 0.067
## Ctxn2  3.279432e-59 1.046663e-55   0.841490 0.474 0.037
## Arx    8.099916e-55 2.154308e-51   1.898095 0.711 0.132
##        cluster   gene    avgExpr statistic       auc
## Irx1     Alpha   Irx1  1.7037037   92693.0 0.7937744
## Tmem27   Alpha Tmem27 11.9555556  109864.5 0.9408221
## Peg10    Alpha  Peg10  4.0962963   97572.5 0.8355598
## Irx2     Alpha   Irx2  1.8666667   92421.0 0.7914451
## Ctxn2    Alpha  Ctxn2  0.8888889   84262.0 0.7215757
## Arx      Alpha    Arx  2.2518519   93020.0 0.7965746

7.2 GO Enrichment

# Ensure you have org.Mm.eg.db installed for Mouse data
# or org.Hs.eg.db for Human
library(org.Mm.eg.db) 

## Loading required package: AnnotationDbi

## 
## Attaching package: 'AnnotationDbi'

## The following object is masked from 'package:dplyr':
## 
##     select

## 

go_res <- RunEnrichment(
    markers, 
    db = "GO", 
    orgDb = "org.Mm.eg.db", 
    keyType = "SYMBOL", 
    ont = "BP"
)

library(enrichplot)

## enrichplot v1.30.4 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
## 
## Please cite:
## 
## Guangchuang Yu, Fei Li, Yide Qin, Xiaochen Bo, Yibo
## Wu and Shengqi Wang. GOSemSim: an R package for
## measuring semantic similarity among GO terms and gene
## products. Bioinformatics. 2010, 26(7):976-978

dotplot(go_res, showCategory=5)

7.3 KEGG Enrichment

For KEGG, it is recommended to convert gene symbols to Entrez IDs first, or provide a column with Entrez IDs if available.

# Example if markers had ENTREZID
# kegg_res <- RunEnrichment(markers_with_entrez, db = "KEGG", organism = "mmu")
# dotplot(kegg_res)