[BioC] library (sma)

[James W. MacDonald]

Hi Sook Wah,

On 11/1/2010 10:39 PM, Yee, Sook Wah wrote:
> Hi Bioconductor,
>
>    The library(sma) is no longer supported on R-Console.  I was told to download library(limma) instead.  However, I am encountering problem when I need to work on the exercises following the examples given in this website link below.  Could you please let me know what I should do instead of using the code given in the examples in the link below for question 3 (Q3).  Thank you.
>
> http://bioinf.wehi.edu.au/marray/ibc2004/lab4/lab4.html#5.%20R%20and%20Bioconductor%20WWW%20resources
>
> Q3. You might also like to create a cluster image using the function heatmap in the package stats. We will create a clustering image that is commonly seen in many microarray literature.  By default, this function performs hierarchical clustering on both genes and samples and the function will slow down considerably if the number of genes are too large. For illustration purposes, we have selected 100 variable genes.
>
> library(sma)
> golubvar<- apply(golub, 1, var, na.rm = TRUE)
> top100<- stat.gnames(golubvar, 1:length(golubvar), crit = 100)$gnames
> heatmap(golub[top100, ])

If I am not mistaken, these data can be found in the golubEsets 
experimental data package. In addition, the only thing that sma is used 
for here is to get the top 100 probesets, based on variance. We don't 
need sma for that.

 > biocLite("golubEsets")
 > library(golubEsets)
 > data(package="golubEsets")
Data sets in package 'golubEsets':

Golub_Merge             Combined Test and Training Sets from the Golub
                         Paper
Golub_Test              Test Set Data from the Golub Paper
Golub_Train             Training Set from the Golub Paper
golubMerge              Combined Test and Training Sets from the Golub
                         Paper
golubTest               Test Set Data from the Golub Paper
golubTrain              Training Set from the Golub Paper

 > data(Golub_Merge)
 > golubvar <- esApply(Golub_Merge, 1, var, na.rm = TRUE)
 > ord <- order(golubvar, decreasing = TRUE)
 > top100 <- exprs(Golub_Merge)[ord,][1:100,]
 > heatmap(top100)

Best,

Jim


>
>
> Regards,
>
> Sook Wah
>
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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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