参考自：https://yulab-smu.top/biomedical-knowledge-mining-book/semantic-similarity-overview.html
1、背景
（1）两大语义数据库
- GO， Gene Ontology 基因本体库，分为MF、CC、BP三类。可使用
GO.db包获取数据； - DO，Disease Ontology 疾病本体库；与GO一样也是directed acyclic graph结构。
（2）Term 两两相似度分析方法
- Information content-based methods：Resnik method、Lin method、Rel method、Jiang method
- Graph-based method：Wang method
（3）Terms集之间相似度整合思路
- max
- avg
- rcmax
- BMA
2、GO相似度分析
-
GOSemSim包
分析之前，首先确定待分析GO TERM属于MF、CC、BP中的哪一类
library(GOSemSim)
hsGO <- godata('org.Hs.eg.db', ont="MF")
2.1 计算GO terms间相关性
-
goSim()between two GO terms
goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Jiang")
## [1] 0.16
goSim("GO:0004022", "GO:0005515", semData=hsGO, measure="Wang")
## [1] 0.116
-
mgoSim()between two sets of GO terms.
# combine=NULL 表示计算pair-wise相关性
go1 = c("GO:0004022","GO:0004024","GO:0004174")
go2 = c("GO:0009055","GO:0005515")
mgoSim(go1, go2, semData=hsGO, measure="Wang", combine=NULL)
## GO:0009055 GO:0005515
## GO:0004022 0.368 0.116
## GO:0004024 0.335 0.107
## GO:0004174 0.663 0.119
# combine为"max", "avg", "rcmax", "BMA"四者之一，表示计算两个Term Set间的整体相似度
mgoSim(go1, go2, semData=hsGO, measure="Wang", combine="BMA")
## [1] 0.43
2.2 基于关联GO term计算基因的相似性
- 例如基因A关联GO BP term有3个，基因B关联GO BP term有2个。基因A与B的相似性也就转换为前3个term与后2个term的整体相似性
-
geneSim()函数：单个基因两两间相似性
# 基因ID需与semData参数提供的基因ID类型保持一致
# hsGO2 <- godata('org.Hs.eg.db', keytype = "SYMBOL", ont="MF", computeIC=FALSE)
GOSemSim::geneSim("241", "251", semData=hsGO, measure="Wang", combine="BMA")
## $geneSim
## [1] 0.149
##
## $GO1
## [1] "GO:0004364" "GO:0004464" "GO:0004602" "GO:0005515" "GO:0047485"
## [6] "GO:0050544"
##
## $GO2
## [1] "GO:0004035"
mgeneSim(genes=c("835", "5261","241", "994"),
semData=hsGO, measure="Wang",verbose=FALSE)
## 835 5261 241 994
## 835 1.000 0.478 0.451 0.578
## 5261 0.478 1.000 0.433 0.499
## 241 0.451 0.433 1.000 0.452
## 994 0.578 0.499 0.452 1.000
-
clusterSim()函数：基因cluster间相似性
gs1 <- c("835", "5261","241", "994", "514", "533")
gs2 <- c("578","582", "400", "409", "411")
clusterSim(gs1, gs2, semData=hsGO, measure="Wang", combine="BMA")
## [1] 0.613
library(org.Hs.eg.db)
x <- org.Hs.egGO
hsEG <- mappedkeys(x)
set.seed <- 123
clusters <- list(a=sample(hsEG, 20), b=sample(hsEG, 20), c=sample(hsEG, 20))
mclusterSim(clusters, semData=hsGO, measure="Wang", combine="BMA")
## a b c
## a 1.000 0.718 0.697
## b 0.718 1.000 0.720
## c 0.697 0.720 1.000
3、DO相似度分析
- 整体分析思路同上；
- 主要区别在于分析包
DOSE内置了DO的数据，不需要单独准备了
3.1 DO term相似性
library(DOSE)
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(a[1], b[1], measure="Resnik")
## [1] 0
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
3.2 基于关联DO term计算基因的相似性
g1 <- c("84842", "2524", "10590", "3070", "91746")
g2 <- c("84289", "6045", "56999", "9869")
DOSE::geneSim(g1[1], g2[1], measure="Wang", combine="BMA")
## [1] 0.051
gs <- DOSE::geneSim(g1, g2, measure="Wang", combine="BMA")
gs
## 84289 6045 56999 9869
## 84842 0.051 0.135 0.355 0.103
## 2524 0.284 0.172 0.517 0.517
## 10590 0.150 0.173 0.242 0.262
## 3070 0.573 0.517 1.000 1.000
## 91746 0.351 0.308 0.527 0.496
DOSE::clusterSim(g1, g2, measure="Wang", combine="BMA")
g3 <- c("57491", "6296", "51438", "5504", "27319", "1643")
clusters <- list(a=g1, b=g2, c=g3)
DOSE::mclusterSim(clusters, measure="Wang", combine="BMA")
