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我不知道是否有人熟悉Bioconductor RankProduct软件包的排名和获得差异表达的基因。有关该软件的一些信息如下paper,manual,documentation。基因排名微阵列
我在使用该程序时遇到了一些问题,可能是因为我对R语言知之甚少。我试图用我自己的数据复制上述pdf文件中的步骤。虽然我自己的数据集不像示例中那样位于afffy .cel文件中,但仅作为制表符分隔文件中的行和列。我有两个条件(1和2中,复制为每个= 4)
这是我的代码:
library(RankProd)
library(preprocessCore)
#Read expression data
#gdata <- read.table(file="data2.txt", sep="\t", header=T) #9000 rows of genes X 8 columns of chips
gdata <- read.table(file="data2.txt", sep="\t", header=T, row.names=1) #9000 rows of genes X 8 columns of chips
#colnames(gdata)
# This vector contains the microarray sample names
SampleNames= names(data.frame(gdata[,-1]))
#names(datExpr)=gdata[,1]
# This vector contains the gene names
datExpr.gnames= gdata$GeneName
# Since the first column contains the gene names, exclude it.
# dataExp is then the matix required
datExpr=data.frame(gdata[,-1])
#convert data into matrix form
datExpr <- as.matrix(datExpr)
#data normalization - quantile normalization
#datExpr.log.norm <- normalize.quantiles((log2(datExpr)),copy=TRUE) #with logged data
datExpr <- datExpr.log.norm
#datExpr.norm <- normalize.quantiles(datExpr,copy=TRUE) #without logged data
#datExpr <- datExpr.norm
# Identify two class data - control/treatment (or condition 1/condition2)
nl <- 4
n2 <- 4
cl <- rep(c(0,1), c(nl, n2))
datExpr.cl <- cl
# data were generated under identical or very similar conditions except the
# factor of interest (e.g., control and treatment),
origin <- rep(1, nl + n2)
datExpr.origin <- origin
# Data anslysis
datExpr.sub <- datExpr[,which(datExpr.origin == 1)]
datExpr.cl.sub <- datExpr.cl[which(datExpr.origin == 1)]
datExpr.origin.sub <- datExpr.origin[which(datExpr.origin == 1)]
#Rank product analysis and output
#RP.out <- RP(datExpr.sub, datExpr.cl.sub, num.perm = 100, logged = TRUE,na.rm = FALSE, plot = FALSE, rand = 123)
RP.out <- RPadvance(datExpr.sub, datExpr.cl.sub, datExpr.origin.sub, num.perm = 100,logged = TRUE,
na.rm = FALSE, gene.names = datExpr.gnames, plot = FALSE,rand = 123)
# Output a table of the identified genes based on user-specified selection criteria
topGene(RP.out, cutoff = 0.05, method = "pfp", logged = TRUE,logbase = 2, gene.names = datExpr.gnames)
我并运行该代码,但在一个条件VS的差异表达基因的我的倍数变化其他都是0或无限。我想知道有这方面经验的人能否帮助我。
谢谢Csgillespie。我决定改用RankProdIt(交互式程序),这对我的需求很有用。 – 2013-02-15 03:51:15