Disease ontology (DO)疾病本体论是从疾病的角度对基因进行注释。DO对于从高通量测序结果到临床的对应关系的转换非常重要。DOSE包提供DO terms和基因的语义相似性分析，这些为生物学家探索疾病和基因功能的相似性提供了更大的可能。富集分析包括超几何分布和GSEA分析。
- in-house developed R package :室内开发的R包？
下面就来大致学习下DOSE包
DOSE提供5种基因语义相似性评价方法，两种富集分析方法：超几何分布和GSEA，以及疾病和基因集之间的比较方法。
1.语义相似性检测
1.1 doSim()
在DOSE中，用doSim来计算两个DO terms和两个 set of DO terms的语义相似性
rm(list = ls())
library(DOSE)
library(clusterProfiler)
a <- c("DOID:14095", "DOID:5844", "DOID:2044", "DOID:8432", "DOID:9146",
"DOID:10588", "DOID:3209", "DOID:848", "DOID:3341", "DOID:252")
b <- c("DOID:9409", "DOID:2491", "DOID:4467", "DOID:3498", "DOID:11256")
doSim(a[1],b[1],measure = "Wang")
## [1] 0.07142995
doSim() measure共有5种方法，“Wang”, “Resnik”, “Rel”, “Jiang”, and “Lin”.
s <- doSim(a,b,measure = "Wang")
s
## DOID:9409 DOID:2491 DOID:4467 DOID:3498 DOID:11256
## DOID:14095 0.07142995 0.05714393 0.03676194 0.03676194 0.52749870
## DOID:5844 0.14897652 0.11564838 0.02801328 0.02801328 0.06134327
## DOID:2044 0.14897652 0.11564838 0.02801328 0.02801328 0.06134327
## DOID:8432 0.17347273 0.13877811 0.03676194 0.03676194 0.07142995
## DOID:9146 0.07142995 0.05714393 0.03676194 0.03676194 0.17347273
## DOID:10588 0.13240905 0.18401515 0.02208240 0.02208240 0.05452137
## DOID:3209 0.14897652 0.11564838 0.02801328 0.02801328 0.06134327
## DOID:848 0.14897652 0.11564838 0.02801328 0.02801328 0.06134327
## DOID:3341 0.13240905 0.09998997 0.02208240 0.02208240 0.05452137
## DOID:252 0.06134327 0.04761992 0.02801328 0.02801328 0.06134327
# 语义相似性结果可视化
simplot(s,
color.low = "white",color.high = "red",
labs = TRUE,digits = 2,labs.size = 5,
font.size = 14,xlab = "",ylab = "")
1.2 geneSim()
DOSE还可以计算基因之间的相似性，有max, avg, rcmax和BMA多种合并方法。
g1 <- c("84842", "2524", "10590", "3070", "91746")
g2 <- c("84289", "6045", "56999", "9869")
gs <- geneSim(g1,g2,measure = "Wang",combine = "BMA")
simplot(gs,
color.low = "white",color.high = "red",
labs = TRUE,digits = 2,labs.size = 5,
font.size = 14,xlab = "",ylab = "")
1.3 clusterSim()
clusterSim()比较两个基因集间的语义相似性，mclusterSim()比较多个基因集间的语义相似性。
clusterSim(g1,g2,measure = "Wang",combine = "BMA")
## [1] 0.549
g3 <- c("57491", "6296", "51438", "5504", "27319", "1643")
clusters <- list(a=g1,b=g2,c=g3)
mclusterSim(clusters,measure = "Wang",combine = "BMA")
## a b c
## a 1.000 0.549 0.425
## b 0.549 1.000 0.645
## c 0.425 0.645 1.000
2.疾病-基因相关性网络
data(geneList,package = "DOSE")
gene <- names(geneList)[abs(geneList)>1]
x <- enrichDO(gene,ont="DO",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
universe = names(geneList),
minGSSize = 5,
readable = T)
cnetplot(x,categorySize="pvalue",foldChange = geneList)
barplot(x,showCategory = 10)
以上是超几何分布检验分析的结果，下面进行GSEA富集分析
y <- gseDO(geneList,
minGSSize= 120,
pvalueCutoff=0.2,
pAdjustMethod = "BH",
verbose = F)
library(enrichplot)
gseaplot2(y,1:4,pvalue_table = T)
3.彩蛋
Y叔在DOSE里自定义了theme_dose()主题,还是比较符合论文发表需求的，与ggsci的配色交叉使用会有不一样的感觉吆。
library(ggsci)
ggplot(x,aes(Count/810,fct_reorder(Description,Count)))+
geom_segment(aes(xend=0,yend=Description))+
geom_point(aes(size=Count,color=-log10(p.adjust)))+
scale_color_gsea()+
theme_dose(12)+
labs(x="",y="")
参考链接：
1.DOSE: Disease Ontology Semantic and Enrichment analysis
2.Biomedical Knowledge Mining using GOSemSim and clusterProfiler
