Hi Sean, you were right... there are some inf in my data. Thanks a lot for your help! On Thu, Jan 27, 2011 at 6:04 AM, Sean Davis <sdavis2@mail.nih.gov> wrote: > > > On Thu, Jan 27, 2011 at 12:00 AM, avehna <avhena@gmail.com> wrote: > >> Hi All, >> >> I'm trying to cluster 21657 genes that are differentially expressed in my >> microarray data, but it's actually not working for me. After reading the >> normalized signal and calculating the mean for each treatment I proceed to >> read the list of genes differentially expressed (previously calculated >> using >> limma). The problem occurs during "hclust" function (please see below my >> code and corresponding error). Is it possible for this error to be due to >> the number of genes? when I use the same code for only 1000 genes it works >> pretty well. >> >> How could I solve this problem? I need this figure for my paper... >> >> Thank you for your help! >> >> Sincerely, >> Avhena >> >> >> >> ************************************************ >> > signal<-signal[-grep("AFFX",rownames(signal)), ,drop=FALSE] >> > pDatam <- read.AnnotatedDataFrame('pdatam.txt', row.names = 1, header = >> TRUE, sep = '\t') >> > pData <- read.AnnotatedDataFrame('pdata.txt', row.names = 1, header = >> TRUE, sep = '\t') >> > expset <- new("ExpressionSet", exprs = signal, phenoData = pData) >> >> > means1 <- means(pairwise.comparison(expset, "Type", c("Control", "BMP"), >> method="logged", logged=FALSE)) >> > means2 <- means(pairwise.comparison(expset, "Type", c("BMP.VPA", >> "SHH.1D"), method="logged", logged=FALSE)) >> > means3 <- means(pairwise.comparison(expset, "Type", c("SHH.6H", >> "SHH.VPA.1D"), method="logged", logged=FALSE)) >> > all_means<-cbind(means1,means2,means3) >> > expmeans <- new("ExpressionSet", exprs = all_means, phenoData = pDatam) >> > subset<-get.array.subset(expmeans, "Type", c("Control", "BMP", "SHH.1D", >> "SHH.VPA.1D")) >> > genes<-read.table("affy_ids_diff_exprs05.dat") >> > mysubset<-exprs(subset)[match(levels(genes[,]), >> rownames(exprs(subset))),] >> >> > hr <- hclust(as.dist(1-cor(t(mysubset), method="spearman")), >> method="complete") >> >> Error in hclust(as.dist(1 - cor(t(mysubset), method = "spearman")), method >> = >> "complete") : >> NA/NaN/Inf in foreign function call (arg 11) >> > > Looks like you might have some NAs or Inf in your data. Try > summary(mysubset) to see. > > Sean > > >> Calls: hclust -> .Fortran >> In addition: Warning message: >> In cor(t(mysubset), method = "spearman") : the standard deviation is zero >> Execution halted >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > [[alternative HTML version deleted]]

Jim, thanks a lot! there were some inf in my data... I will try again after removing then. Avhena On Thu, Jan 27, 2011 at 9:43 AM, James W. MacDonald <jmacdon@med.umich.edu>wrote: > Hi Avhena, > > > On 1/27/2011 12:00 AM, avehna wrote: > >> Hi All, >> >> I'm trying to cluster 21657 genes that are differentially expressed in my >> microarray data, but it's actually not working for me. After reading the >> normalized signal and calculating the mean for each treatment I proceed to >> read the list of genes differentially expressed (previously calculated >> using >> limma). The problem occurs during "hclust" function (please see below my >> code and corresponding error). Is it possible for this error to be due to >> the number of genes? when I use the same code for only 1000 genes it works >> pretty well. >> >> How could I solve this problem? I need this figure for my paper... >> > > You need to remove the rows that have no variability. For example: > > > dat <- matrix(rnorm(1000), nc=10) > > dat[3,] <- rep(dat[3,3], 10) ## make row three have var=0 > > hclust(as.dist(1-cor(t(dat), method="spearman")), method="complete") > Error in hclust(as.dist(1 - cor(t(dat), method = "spearman")), method = > "complete") : > > NA/NaN/Inf in foreign function call (arg 11) > In addition: Warning message: > In cor(t(dat), method = "spearman") : the standard deviation is zero > > now again, without this row > > > hclust(as.dist(1-cor(t(dat[-3,]), method="spearman")), method="complete") > > Call: > hclust(d = as.dist(1 - cor(t(dat[-3, ]), method = "spearman")), method = > "complete") > > Cluster method : complete > Number of objects: 99 > > something like > > ind <- apply(mysubset, 1, var) == 0 > mysubset <- mysubset[!ind,] > > should do the trick. > > Best, > > Jim > > > >> Thank you for your help! >> >> Sincerely, >> Avhena >> >> >> >> ************************************************ >> >>> signal<-signal[-grep("AFFX",rownames(signal)), ,drop=FALSE] >>> pDatam<- read.AnnotatedDataFrame('pdatam.txt', row.names = 1, header = >>> >> TRUE, sep = '\t') >> >>> pData<- read.AnnotatedDataFrame('pdata.txt', row.names = 1, header = >>> >> TRUE, sep = '\t') >> >>> expset<- new("ExpressionSet", exprs = signal, phenoData = pData) >>> >> >> means1<- means(pairwise.comparison(expset, "Type", c("Control", "BMP"), >>> >> method="logged", logged=FALSE)) >> >>> means2<- means(pairwise.comparison(expset, "Type", c("BMP.VPA", >>> >> "SHH.1D"), method="logged", logged=FALSE)) >> >>> means3<- means(pairwise.comparison(expset, "Type", c("SHH.6H", >>> >> "SHH.VPA.1D"), method="logged", logged=FALSE)) >> >>> all_means<-cbind(means1,means2,means3) >>> expmeans<- new("ExpressionSet", exprs = all_means, phenoData = pDatam) >>> subset<-get.array.subset(expmeans, "Type", c("Control", "BMP", "SHH.1D", >>> >> "SHH.VPA.1D")) >> >>> genes<-read.table("affy_ids_diff_exprs05.dat") >>> mysubset<-exprs(subset)[match(levels(genes[,]), >>> rownames(exprs(subset))),] >>> >> >> hr<- hclust(as.dist(1-cor(t(mysubset), method="spearman")), >>> >> method="complete") >> >> Error in hclust(as.dist(1 - cor(t(mysubset), method = "spearman")), method >> = >> "complete") : >> NA/NaN/Inf in foreign function call (arg 11) >> Calls: hclust -> .Fortran >> In addition: Warning message: >> In cor(t(mysubset), method = "spearman") : the standard deviation is zero >> Execution halted >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > -- > James W. MacDonald, M.S. > Biostatistician > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should not be > used for urgent or sensitive issues [[alternative HTML version deleted]]