关于三个工具的具体描述（尤其GOrilla）,见 GO富集分析-对比Gorilla, clusterProfiler, topGO三种工具part1 ~ GSEA 篇
Interesting gene vs universe~ (ORA )对比
Gene list preparation
保证输入一致， GOrilla 输入 symbol, clusterProfiler 及 topGO 输入 ENTREZ ID.
# gene list preparation for ORA
# GSE76275
library(hgu133plus2.db)
library(magrittr)
load("GSE76275_DEGs.Rdata")
# universe gene list
universeprobe <- row.names(DEGdf)
universeentrez <- AnnotationDbi::select(hgu133plus2.db,
universeprobe,
"ENTREZID",
"PROBEID")[,2] %>%
na.omit()%>%
unique()
save(universeentrez, file = "universe_entrezidlist.Rdata")
### for GOrilla
universesymbol <- AnnotationDbi::select(hgu133plus2.db,
universeentrez,
"SYMBOL",
"ENTREZID")[,2] %>%
na.omit()%>%
unique()
write.table(universesymbol, file = "universe_symbollist.txt",
quote = F, col.names = F, row.names = F)
## interesting gene list
interesprobe <- row.names(DEGdf[abs(DEGdf$logFC) > 1,])
interesentrez <- AnnotationDbi::select(hgu133plus2.db,
interesprobe,
"ENTREZID",
"PROBEID")[,2] %>%
na.omit()%>%
unique()
save(interesentrez, file = "interesting_entrezidlist.Rdata")
### for GOrilla
interessymbol <- AnnotationDbi::select(hgu133plus2.db,
interesentrez,
"SYMBOL",
"ENTREZID")[,2] %>%
na.omit()%>%
unique()
write.table(interessymbol, file = "interesting_symbollist.txt",
quote = F, col.names = F, row.names = F)
clusterProfiler
ORA GO-BP
# clusterProfiler ORA
## GO-BP
orago_BP <- enrichGO(gene = interesentrez,
universe = universeentrez,
OrgDb = hgu133plus2.db,
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.05,
qvalueCutoff = 0.05,
readable = TRUE)
cpORAGOres_BP <- orago_BP@result
dim(cpORAGOres_BP)
# [1] 2001 9
save(cpORAGOres_BP, file = "cpORAGOres_BP.Rdata")
topGO
predefined list of interesting genes
topGO 需要先将输入的 genelist 和 GO 数据库匹配，基因数已经减少到了17762个。
construct a geneID2GO object (a list)
### construct a geneID2GO object (a list)
library(dplyr)
geneID2GOtb <- na.omit(AnnotationDbi::select(hgu133plus2.db,
universeentrez,
"GO", "ENTREZID"))
geneID2GOmg <- geneID2GOtb %>%
group_by(ENTREZID) %>%
summarise(GO=list(GO))
geneID2GO <- geneID2GOmg$GO
names(geneID2GO) <- geneID2GOmg$ENTREZID
构建一个表示在 universe gene 中，哪些基因是 interesting gene & 哪些不是的 factor，并用这个 factor 代表两个 gene list.
construct a genelist (a factor)
### construct a genelist (a factor)
geneNames <- names(geneID2GO)
myInterestingGenes <- geneNames[match(interesentrez, geneNames)]
ORAgeneList <- factor(as.integer(geneNames %in% myInterestingGenes))
names(ORAgeneList) <- geneNames
str(ORAgeneList)
# Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
# - attr(*, "names")= chr [1:17762] "1" "10" "100" "1000" ...
### enrichment analysis
ORAGOdata <- new("topGOdata",
ontology = "BP",
allGenes = ORAgeneList,
annot = annFUN.gene2GO,
gene2GO = geneID2GO)
ORAGOdata
#
# ------------------------- topGOdata object -------------------------
#
# Description:
# -
#
# Ontology:
# - BP
#
# 17762 available genes (all genes from the array):
# - symbol: 1 10 100 1000 10000 ...
# - 121 significant genes.
#
# 15869 feasible genes (genes that can be used in the analysis):
# - symbol: 1 10 100 1000 10000 ...
# - 111 significant genes.
#
# GO graph (nodes with at least 1 genes):
# - a graph with directed edges
# - number of nodes = 15845
# - number of edges = 37664
#
# ------------------------- topGOdata object -------------------------
经典Fisher test:
### classic Fisher TEST
ORAresultFisher <- runTest(ORAGOdata, algorithm = "classic", statistic = "fisher")
#
# -- Classic Algorithm --
#
# the algorithm is scoring 2826 nontrivial nodes
# parameters:
# test statistic: fisher
ORAresultFisher
#
# Description:
# Ontology: BP
# 'classic' algorithm with the 'fisher' test
# 15845 GO terms scored: 176 terms with p < 0.01
# Annotation data:
# Annotated genes: 15869
# Significant genes: 111
# Min. no. of genes annotated to a GO: 1
# Nontrivial nodes: 2826
### summary table
ORAallRes <- GenTable(ORAGOdata,classicFisher = ORAresultFisher,
orderBy = "classicFshier",ranksOf = "classicFshier",
topNodes = 176)
save(ORAallRes, file = "topORAGOres_BP.Rdata")
结果对比
# comparison
## get GOrilla res
GOrillaORA <- read.csv("GOrillares_2lists.txt", header = T,
sep = "\t")
load("cpORAGOres_BP.Rdata")
load("topORAGOres_BP.Rdata")
可以看出在数量上差异也很明显。
nrow(cpORAGOres_BP)
# [1] 2001
nrow(ORAallRes)
# [1] 176
nrow(GOrillaORA)
# [1] 12
取三个结果的交集，得到5个共有 GO term.
cpvstop <- intersect(cpORAGOres_BP$ID,ORAallRes$GO.ID)
triple <- intersect(cpvstop, GOrillaORA$GO.term)
row.names(GOrillaORA) <- GOrillaORA$GO.term
row.names(ORAallRes) <- ORAallRes$GO.ID
thefive <- cbind(GOrillaORA[triple,], cpORAGOres_BP[triple,], ORAallRes[triple,])
finetable <- subset(thefive, select = c("Description",
"P.value","FDR.q.value", "Enrichment..N..B..n..b.", ## GOrilla
"GeneRatio","BgRatio""pvalue", "p.adjust", "qvalue", ## cp
"Annotated", "Significant", "classicFisher")) ## topGO
可以看出，针对ORA法，GOrilla 和 clusterProfiler 得到的 p.val 较为接近。（GSEA法，clusterProfiler 和 topGO 得到的 p.val 更接近。
最后，向大家隆重推荐生信技能树的一系列干货！
- 生信技能树全球公益巡讲：https://mp.weixin.qq.com/s/E9ykuIbc-2Ja9HOY0bn_6g
- B站公益74小时生信工程师教学视频合辑：https://mp.weixin.qq.com/s/IyFK7l_WBAiUgqQi8O7Hxw
- 招学徒：https://mp.weixin.qq.com/s/KgbilzXnFjbKKunuw7NVfw
