- GSVA的簡介
Gene Set Variation Analysis，被稱為基因集變異分析,是一種非參數的無監督分析方法，主要用來評估芯片核轉錄組的基因集富集結果。主要是通過將基因在不同樣品間的表達量矩陣轉化成基因集在樣品間的表達量矩陣，從而來評估不同的代謝通路在不同樣品間是否富集。其實就是研究這些感興趣的基因集在不同樣品間的差異，或者尋找比較重要的基因集，作為一種分析方法，主要是是為了從生物信息學的角度去解釋導致表型差異的原因。它的主要輸入文件為表達量的矩陣和基因集的文件，通過gsva的方法就可以得出結果；既可以處理芯片的結果，也可以處理轉錄組的結果。
- GSVA的安裝
source("http://bioconductor.org/biocLite.R")
biocLite('GSVAdata')
biocLite('GSVA')
biocLite("limma")
biocLite("genefilter")
- GSVA的運行注意事項
- 如果是芯片的數據是需要對數據進行過濾的，可以參考下面的測試數據代碼，或者看官方的文檔
- GSVA本身提供了三個算法，一般使用默認的算法就可以了
- 對於RNA-seq的數據，如果是read count可以選擇Possion分布，如果是均一化后的表達量值，可以選擇默認參數高斯分布就可以了
- 讀入的數據格式可以參考最后的表達譜數據格式，需要自己制作分組信息
- 模擬數據的使用
#構造一個 30個樣本，2萬個基因的表達矩陣， 加上 100 個假定的基因集
library(GSVA)
p <- 20000 ## number of genes
n <- 30 ## number of samples
nGS <- 100 ## number of gene sets
min.sz <- 10 ## minimum gene set size
max.sz <- 100 ## maximum gene set size
X <- matrix(rnorm(p*n), nrow=p, dimnames=list(1:p, 1:n))
dim(X)
gs <- as.list(sample(min.sz:max.sz, size=nGS, replace=TRUE)) ## sample gene set sizes
gs <- lapply(gs, function(n, p) sample(1:p, size=n, replace=FALSE), p) ## sample gene sets
es.max <- gsva(X, gs, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)
es.dif <- gsva(X, gs, mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
pheatmap::pheatmap(es.max)
pheatmap::pheatmap(es.dif)
library(GSEABase)
library(GSVAdata)
data(c2BroadSets)
c2BroadSets
library(Biobase)
library(genefilter)
library(limma)
library(RColorBrewer)
library(GSVA)
#官方文檔有數據的預處理過程
cacheDir <- system.file("extdata", package="GSVA")
cachePrefix <- "cache4vignette_"
file.remove(paste(cacheDir, list.files(cacheDir, pattern=cachePrefix), sep="/"))
data(leukemia)
leukemia_eset
head(pData(leukemia_eset))
table(leukemia_eset$subtype)
data(leukemia)
leukemia_eset
filtered_eset <- nsFilter(leukemia_eset, require.entrez=TRUE, remove.dupEntrez=TRUE,var.func=IQR, var.filter=TRUE, var.cutoff=0.5, filterByQuantile=TRUE,feature.exclude="^AFFX")
leukemia_filtered_eset <- filtered_eset$eset
cache(leukemia_es <- gsva(leukemia_filtered_eset, c2BroadSets,min.sz=10, max.sz=500, verbose=TRUE),dir=cacheDir, prefix=cachePrefix)
adjPvalueCutoff <- 0.001
logFCcutoff <- log2(2)
design <- model.matrix(~ factor(leukemia_es$subtype))
colnames(design) <- c("ALL", "MLLvsALL")
fit <- lmFit(leukemia_es, design)
fit <- eBayes(fit)
allGeneSets <- topTable(fit, coef="MLLvsALL", number=Inf)
DEgeneSets <- topTable(fit, coef="MLLvsALL", number=Inf,
p.value=adjPvalueCutoff, adjust="BH")
res <- decideTests(fit, p.value=adjPvalueCutoff)
summary(res)
logFCcutoff <- log2(2)
design <- model.matrix(~ factor(leukemia_eset$subtype))
colnames(design) <- c("ALL", "MLLvsALL")
fit <- lmFit(leukemia_filtered_eset, design)
fit <- eBayes(fit)
allGenes <- topTable(fit, coef="MLLvsALL", number=Inf)
DEgenes <- topTable(fit, coef="MLLvsALL", number=Inf,
p.value=adjPvalueCutoff, adjust="BH", lfc=logFCcutoff)
res <- decideTests(fit, p.value=adjPvalueCutoff, lfc=logFCcutoff)
#summary(res)
- RNAseq的數據
如果是RNA-seq的原始整數的read count 在使用gsva時需要設置kcdf="Possion",如果是取過log的RPKM,TPM等結果可以使用默認的值，所以如果我們在使用fpkm進行分析的時候使用默認參數局可以了
data(commonPickrellHuang)
stopifnot(identical(featureNames(huangArrayRMAnoBatchCommon_eset),featureNames(pickrellCountsArgonneCQNcommon_eset)))
stopifnot(identical(sampleNames(huangArrayRMAnoBatchCommon_eset),sampleNames(pickrellCountsArgonneCQNcommon_eset)))
canonicalC2BroadSets <- c2BroadSets[c(grep("^KEGG", names(c2BroadSets)),grep("^REACTOME", names(c2BroadSets)),grep("^BIOCARTA", names(c2BroadSets)))]
data(genderGenesEntrez)
MSY <- GeneSet(msYgenesEntrez, geneIdType=EntrezIdentifier(),collectionType=BroadCollection(category="c2"), setName="MSY")
XiE <- GeneSet(XiEgenesEntrez, geneIdType=EntrezIdentifier(),collectionType=BroadCollection(category="c2"), setName="XiE")
canonicalC2BroadSets <- GeneSetCollection(c(canonicalC2BroadSets, MSY, XiE))
#使用GSVA方法進行計算
esmicro <- gsva(huangArrayRMAnoBatchCommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500,mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
esrnaseq <- gsva(pickrellCountsArgonneCQNcommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500,kcdf="Poisson", mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
- 其實文檔中還比較了GSVA和其他不同方法，這里就略過了
實際的表達譜項目數據測試
表達量矩陣格式
gene AdA3_0h-1 AdA3_0h-2 AdA3_0h-3 AdA3_0h-4 AdA3_0h-5 AdA3_0h-6 AdA3_16h-3 AdA3_16h-4 AdA3_16h-5 AdA3_16h-6 AdGFP_0h-1 AdGFP_0h-2 AdGFP_0h-3 AdGFP_0h-4 AdGFP_0h-5 AdGFP_0h-6 AdGFP_16h-1 AdGFP_16h-2 AdGFP_16h-3 AdGFP_16h-4 AdGFP_16h-5 AdGFP_16h-6
Gnai3 73.842026 52.385326 73.034203 70.679092 67.094292 74.611313 74.853683 72.340401 73.230095 77.39888 70.019714 69.995277 72.172127 72.398514 74.176201 72.700516 60.29203 60.199333 58.043304 58.425671 61.812901 60.219734
Pbsn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Cdc45 3.653137 5.67695 3.560387 3.287101 4.034892 2.92406 11.427282 14.665288 11.368333 11.96515 4.289696 3.869604 4.055799 3.617629 4.800964 4.790279 20.065876 25.753405 19.732252 26.296944 21.906717 20.378906
Scml2 0.15765 0.022811 0.301977 0.056921 0.022823 0.104651 1.542295 0.370989 0.226674 0.426919 0.132548 0.028549 0.066578 0 0.069393 0.10476 0.981483 1.607109 0.84421 0.912878 1.281105 2.309552
基因集格式
G1/S-Specific Transcription NA Rrm2 Ccne1 E2f1 Fbxo5 Cdc6 Dhfr Cdc45 Pola1 Cdt1 Cdk1 Tyms
E2F mediated regulation of DNA replication NA Rrm2 Ccne1 E2f1 Fbxo5 Cdc6 Pola2 Dhfr Cdc45 Pola1 Cdt1 Cdk1 Ccnb1 Tyms
Formation of Fibrin Clot (Clotting Cascade) NA Fga F11 Fgb Proc Serping1 Thbd Tfpi F7 Serpine2 Fgg Serpind1 Serpina5 F8 Pf4
Resolution of D-loop Structures through Holliday Junction Intermediates NA Dna2 Bard1 Brca1 Rad51 Rad51c Brip1 Rad51b Exo1 Gen1 Wrn Blm Brca2 Eme1
Hemostasis NA Fga Serpinf2 Atp1b1 Sparc Slc16a3 Plaur Plat Gpc1 Tgfb1 F11 Fam49b Arrb2 Fgb Rapgef4 Proc Ahsg Wee1 Vav2 Serping1 Esam Kif2c Syk Thbd Kif11 Igf1 Tfpi Rad51c Serpinb2 Lgals3bp Dock11 Prkcg Timp3 Racgap1 Col1a2 Rad51b Pde5a Nos3 Dock8 F7 Serpine2 Kif4 Tek Ehd3 Col1a1 Cav1 Prkch Fgg Serpind1 Serpina5 Igf2 Cd74 Ctsw Irf1 Fcer1g Pde9a Plek Rapgef3 Lcp2 P2rx7 Pde10a Kif15 Kif23 Csf2rb2 Gnai1 Zfpm1 Slc7a8 Spp2 Kif5a Il2rg Lck Rac2 Hist2h3c1 Gng2 Pla2g4a Cyb5r1 F8 Gata3 Cd63 Pf4 Serpina3c Kif3c P2ry12 Slc16a8 F2rl3 Ppia Vav3 Ttn Kif18a Pde3a Csf2 Prkcb
Resolution of D-Loop Structures NA Dna2 Bard1 Brca1 Rad51 Rad51c Brip1 Rad51b Exo1 Gen1 Wrn Blm Brca2 Eme1
Common Pathway of Fibrin Clot Formation NA Fga Fgb Proc Thbd Serpine2 Fgg Serpind1 Serpina5 F8 Pf4
Kinesins NA Kif2c Kif11 Racgap1 Kif4 Kif15 Kif23 Kif5a Kif3c Kif18a
Xenobiotics NA Cyp2c50 Cyp2c37 Cyp2c54 Cyp2e1 Cyp2c29 Cyp2f2 Cyp2c38 Cyp2b9 Cyp2c55 Arnt2 Cyp2a4
Resolution of D-loop Structures through Synthesis-Dependent Strand Annealing (SDSA) NA Dna2 Bard1 Brca1 Rad51 Rad51c Brip1 Rad51b Exo1 Wrn Blm Brca2
Initial triggering of complement NA Crp C2 C4b C3 C1qc C1qa C1qb Cfp
Integrin cell surface interactions NA Fga Icam1 Itgb6 Spp1 Fgb Col5a2 Itga2 Tnc Col18a1 Col1a2 Itga9 Col1a1 Fgg Col7a1 Icam2 Fbn1 Itga8 Col8a1 Col4a4
CRMPs in Sema3A signaling NA Dpysl3 Dpysl2 Cdk5r1 Dpysl5 Plxna4 Sema3a Crmp1 Plxna3
Phase 1 - Functionalization of compounds NA Adh1 Cyp4a14 Cyp2c50 Cyp2c37 Cyp4a10 Maob Cyp2c54 Cyp2e1 Cyp4b1 Cyp2c29 Cyp39a1 Cyp2f2 Cyp3a25 Nr1h4 Marc1 Fmo2 Aoc2 Aldh1b1 Cyp2c38 Cmbl Cyp27b1 Adh7 Aoc3 Cyp2b9 Cyp2c55 Fdx1l Arnt2 Cyp2a4
Meiotic Recombination NA Brca1 Rad51 Hist1h2bc Hist1h2bg Blm Brca2 Hist2h2aa2 Hist2h2aa1 Hist1h2bh Hist1h2bj Hist1h2bl Hist1h2bp Hist1h2bk Hist1h2ba H2afv Hist3h2a Hist1h2bn Hist1h2bf Hist1h2bm Hist2h3c1 Hist1h2bb Prdm9 Msh4 Hist2h2ac Hist1h4h
具體命令如下
#讀取基因集文件
geneSets <- getGmt("test.geneset")
#讀取表達量文件並去除重復
mydata <- read.table(file = "all.genes.fpkm.xls",header=T)
name=mydata[,1]
index <- duplicated(mydata[,1])
fildup=mydata[!index,]
exp=fildup[,-1]
row.names(exp)=name
#將數據框轉換成矩陣
mydata= as.matrix(exp)
#使用gsva方法進行分析，默認mx.diff=TRUE，min.sz=1,max.zs=Inf，這里是設置最小值和最大值
res_es <- gsva(mydata, geneSets, min.sz=10, max.sz=500, verbose=FALSE, parallel.sz=1)
pheatmap(res_es)
#mx.diff=FALSE es值是一個雙峰的分布
es.max <- gsva(mydata, geneSets, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)
pheatmap(es.max)
#mx.diff=TURE es值是一個近似正態分布
es.dif <- gsva(mydata, geneSets, mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
pheatmap(es.dif)
#可以看一下兩種不同分布的效果,前者是高斯分布，后者是二項分布
par(mfrow=c(1,2), mar=c(4, 4, 4, 1))
plot(density(as.vector(es.max)), main="Maximum deviation from zero",xlab="GSVA score", lwd=2, las=1, xaxt="n", xlim=c(-0.75, 0.75), cex.axis=0.8)
axis(1, at=seq(-0.75, 0.75, by=0.25), labels=seq(-0.75, 0.75, by=0.25), cex.axis=0.8)
plot(density(as.vector(es.dif)), main="Difference between largest\npositive and negative deviations",xlab="GSVA score", lwd=2, las=1, xaxt="n", xlim=c(-0.75, 0.75), cex.axis=0.8)
axis(1, at=seq(-0.75, 0.75, by=0.25), labels=seq(-0.75, 0.75, by=0.25), cex.axis=0.8)
group_list <- factor(c(rep("control",2), rep("siSUZ12",2)))
design <- model.matrix(~group_list)
colnames(design) <- levels(group_list)
rownames(design) <- colnames(counts)
#設置分組
col = names(exp)
sample=col[1:10]
group=c(rep('control',6),rep('treat',4))
phno=data.frame(sample,group)
Group<-factor(phno$group,levels=levels(phno$group))
design<-model.matrix(~0+Group)
colnames(design) <- c("control", "treat")
#獲取需要進行差異分析的分組
res=es.max[,1:10]
#定義閾值
logFCcutoff <- log2(1.5)
adjPvalueCutoff <- 0.001
#進行差異分析
fit <- lmFit(res, design)
fit <- eBayes(fit)
allGeneSets <- topTable(fit, coef="treat", number=Inf)
DEgeneSets <- topTable(fit, coef="treat", number=Inf,p.value=adjPvalueCutoff, adjust="BH")
res <- decideTests(fit, p.value=adjPvalueCutoff)
#summary(res)
#畫火山圖
DEgeneSets$significant="no"
DEgeneSets$significant=ifelse(DEgeneSets$logFC>0|DEgeneSets$logFC<0,"up","down")
ggplot(DEgeneSets,aes(logFC,-1*log10(adj.P.Val)))+geom_point(aes(color =significant)) + xlim(-4,4) + ylim(0,30)+labs(title="Volcanoplot",x="log[2](FC)", y="-log[10](FDR)")+scale_color_manual(values =c("#00ba38","#619cff","#f8766d"))+geom_hline(yintercept=1.3,linetype=4)+geom_vline(xintercept=c(-1,1),linetype=4)
#獲取差異基因集的表達量
DEgeneSetspkm = merge(DEgeneSets,es.max,by=0,all.x=TRUE)[,c(1,11:20)]
degsetsp=DEgeneSetspkm[,-1]
name=DEgeneSetspkm[,1]
row.names(degsetsp)=name
pheatmap(degsetsp)
