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
>>
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>>
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>
> --
> 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
> **********************************************************
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> used for urgent or sensitive issues

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