[BioC] probeset level visualization with standard deviation...

[oliveros at cnb.uam.es]

Dear Paul,

Starting from your example with 3 genes and 6 columns, stored in a file
named "testTable.txt", try the following code in R:

data<-read.table("testTable.txt",sep="\t",header=1,row.names=1);

sd_heart<-sd(t(data[1:3]));
sd_breast<-sd(t(data[4:6]));
avg_heart<-mean(as.data.frame(t(data[1:3])));
avg_breast<-mean(as.data.frame(t(data[4:6])));

write.table(file="result.txt",cbind(avg_heart,sd_heart,avg_breast,sd_breast),sep="\t",col.names=NA);

Now you should have a new file named "result.txt" with the averaged values
and standard deviations.

To plot these data (including error bars for SD) you can use FIESTA VIEWER:

http://bioinfogp.cnb.csic.es/tools/FIESTA

This on line tool transforms any text-tabulated file of microarray results
into an interactive web site containing interactive plots with error bars
and many filtering and visualizing options.

Note that FIESTA VIEWER will not work with only 3 genes (as your example).
It is designed to be used with the full microarray geneset.

I hope that helps,

Regards,

Juan Carlos Oliveros
BioinfoGP, CNB-CSIC, Spain.












> hi members,
>
> i am looking for a function which plots me average probeset levels with
> standard deviations (e.g. tissues 1 against tissue 2). Is there any
> functions which could do this. i work with affymetrix chips which have
> often three times determination.
>
> an example for a better understanding:
>
> after using the functions read.exon() and rma() i specify my probeset_ids:
>
>  > probeset = gene.to.probeset("ensemle_gene_id")
>  > exon_probesets = select.probewise(probeset, filter="exonic")
>
> now i would like to have the average values with the standard deviation
> for these probeset_id log levels to plot e.g. two tissues (here heart
> and breast) against later.
>
> probeset_id    heart_A.CEL       heart_B.CEL    heart_C.CEL
> breast_A.CEL    breast_B.CEL    breast_C.CEL
> 1.                   6.5432                6.7833              6.3234
>             4.6566               5.2121               4.7654
> 2.                   5.8422                6.0813              6.2334
>             5.2526               5.1236               4.9234
> 3.                   5.8422                6.0813              6.2334
>             5.2526               5.1236               4.9234
>
> is there any simple way? till now i extract all data from a data.frame
> given by splicing.index(), calculate average and sd for every
> probeset_id and tissue, which is quite complicated.
>
> thanks
> paul
>
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