Hi Alan
I am extremely sorry for being so late in the reply as my previous reply was
too big because of the attachment size and thus did not go through.
The error was :
Error Trace
..Processor 'AffyDataAnalysis' - Port 'histRawPlot' : RShell failed : invalid
'description' argument
..org.rosuda.Rengine.Rserve.RserveException: invalid 'description' argument
..net.sf.taverna.t2.activities.rshell.RshellActivity$1.run(RshellActivity.java:217)
...java.lang.Thread.run(unknownfile)
Also my input values were:
Normalization method: rma
arrayTypeAffy : hgu95av2
path : C:/
samples1 : A1.CEL
A2.CEL
sample2 : B1.CEL
B2.CEL
Rserve window seems to work fine because when i run the workflow i can see all
the stes such as "Normalization" e.t.c
Also for Taverna2.2 workbench i just installed R2.10.1 and Rserve from within
R.Do i need to do any changes in the taverna configuration to run R workflows.
Will appreciate your help.
Kind Regards
Rinku
> To: [email protected]
> From: [email protected]
> Date: Thu, 2 Dec 2010 20:35:38 +0000
> Subject: Re: [Taverna-users] R workflow error
>
> Hello Rinku,
>
> Can you please send just the text of the error together with example
> input values?
>
> Thanks,
>
> Alan
>
>
>
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<?xml version="1.0" encoding="UTF-8"?>
<s:scufl xmlns:s="http://org.embl.ebi.escience/xscufl/0.1alpha"; version="0.2" log="0">
<s:workflowdescription lsid="urn:lsid:net.sf.taverna:wfDefinition:d93a16c9-b599-48b4-b2c8-562127456fa3" author="Saeedeh Maleki-Dizaji and Paul Fisher" title="Microarray CEL file and R analysis">This workflow takes in a CEL file and a normalisation method then returns a series of images/graphs which represent the same output obtained using the MADAT software package (MicroArray Data Analysis Tool) [http://www.bioinf.manchester.ac.uk/MADAT/index.html]. Also retruned by this workflow are a list of the top differentialy expressed genes (size dependant on the number specified as input - geneNumber), which are then used to find the candidate pathways which may be influencing the observed changes in the microarray data. By identifying the candidate pathways, more detailed insights into the gene expression data can be obtained.
NOTE - You will also need to install R and Rserv on your machine and install the libaries required by the R script into you R library directory (see for basic info: http://www.cs.man.ac.uk/~fisherp/rlib.html)
The example inputs for this workflow are as follows:
Samples1 = one or more CEL files for cross-correlating with Samples2 CEL files (new line separated including the .CEL):
Liver_Day1_Mouse.CEL
Liver_Day2_Mouse.CEL
Samples2 = one or more CEL files for cross-correlating with Samples1 CEL files (new line separated including the .CEL):
Kideny_Day1_Mouse.CEL
Kidney_Day2_Mouse.CEL
geneNumber = the number of differentialy expressed gene to be returned above a given p-value, e.g. 20
arrayTypeAffy = the name of the Mouse AffyMetrix array used, e.g. mouse4302, hgu133a...
path = the direct path to the CEL file location, e.g. C:/Microarray_Data/CEL_FILES/ - note the forward slashes
NormalizationMethod = the type of normalisation to perfrom, e.g. rma, gcrma or mmgmos
testMethod = e.g. limma, mmtest or pplr
p-value = the p-value cut-off value for the array data, e.g. 0.05
foldChange = the fold change value for the microarray data, e.g. 1 (means greater than 1 or less than -1)</s:workflowdescription>
<s:processor name="AffyDataAnalysis">
<s:defaults>
<s:default name="dirPath">C:/affy data for Munich course/ShortCelFilesLiver</s:default>
<s:default name="arrayType">mouse4302</s:default>
</s:defaults>
<s:rshell s:hostname="localhost" s:port="6311" s:username="" s:password="" s:keepSessionAlive="false">
#...@to do: extract array type automatically then install relevant packages
# installing packages
#source("http://www.bioconductor.org/biocLite.R";)
#biocLite()
#biocLite(c("affy "," limma ","mmgmos"," ade4"," affyQCReport"," base","pplr " ," splines "," survival "," multtest "))
library(base)
library(affy)
# make sure you are in the directory
# where the CEL files are
getwd()
#setwd("C:/affy data for Munich course/ShortCelFilesLiver")
# dirPath input from workflow
setwd(dirPath)
fnames = dir(pattern="CEL")
#fnamess = dir(pattern="tab")
# read in CEL files
#library(mouse4302cdf, lib="C:/Program Files/R/rw2001/library" )
#library(mouse4302cdf)
#library(mouse4302, lib="C:/Program Files/R/rw2001/library")
#library(mouse4302);
library(arrayType,,character.only = TRUE)
library(sub(" ","",paste(arrayType,'cdf')), character.only = TRUE)
affyID1 <- NULL
#************ Loading data
data.raw = ReadAffy(filenames=fnames)
sName = sampleNames(data.raw)
# check whether it read in things correctly
#data.raw
histRawPlot = 'histRawPlot.png'
png(histRawPlot)
image(data.raw, col=rainbow(100, start=0, end=0.7)[100:1])
boxplot(data.raw, col=rainbow(50))
dev.off()
# hist(data.raw, col = 1:length(sName))
# legend('right',sName, lt = 1:length(sName),col = 1:length(sName), cex = 0.6)
#dev.off()
#for (i in 1:5) gc()
#************* Normalization Method
normalization <- function (data,method) {
switch(method,
rma = rma(data),
gcrma = gcrma(data),
mmgmos = mmgmos(data)
)
}
data.norm <- normalization(data.raw, normMethod)
# Write result into the text file
normText = 'normText.txt'
temp = exprs(data.norm)
#write.exprs(temp, normText)
write(temp, normText)
# create box plot
boxNormPlot = 'boxNormPlot.png'
png(boxNormPlot)
boxplot(data.frame(exprs(data.norm)), col=rainbow(30))
dev.off()
#class(data.rma)
#********************************creation of genelist
fnamess = dir(pattern="tab")
genelist <-read.table(fnamess)
geneindex <- geneNames(data.norm) %in% genelist[,1]
#******************writeresult to text file
genetext = 'tavernaanno.txt'
temp = exprs(data.norm) [geneindex, ]
write.table(temp, genetext)
#*************************************end
<s:rshellInputPortList>
<s:rshellInputPort s:syntacticType="l('text/plain')" s:symanticType="STRING_LIST">dirPath</s:rshellInputPort>
<s:rshellInputPort s:syntacticType="l('text/plain')" s:symanticType="STRING_LIST">arrayType</s:rshellInputPort>
<s:rshellInputPort s:syntacticType="l('text/plain')" s:symanticType="STRING_LIST">normMethod</s:rshellInputPort>
<s:rshellInputPort s:syntacticType="l('text/plain')" s:symanticType="STRING_LIST">group1</s:rshellInputPort>
<s:rshellInputPort s:syntacticType="l('text/plain')" s:symanticType="STRING_LIST">group2</s:rshellInputPort>
</s:rshellInputPortList>
<s:rshellOutputPortList>
<s:rshellOutputPort s:syntacticType="'image/png'" s:symanticType="PNG_FILE">histRawPlot</s:rshellOutputPort>
<s:rshellOutputPort s:syntacticType="'image/png'" s:symanticType="PNG_FILE">boxNormPlot</s:rshellOutputPort>
</s:rshellOutputPortList>
</s:rshell>
</s:processor>
<s:processor name="String_Constant" boring="true">
<s:stringconstant>\n</s:stringconstant>
</s:processor>
<s:processor name="Split_sample2">
<s:local>org.embl.ebi.escience.scuflworkers.java.SplitByRegex</s:local>
</s:processor>
<s:processor name="Split_sample1">
<s:local>org.embl.ebi.escience.scuflworkers.java.SplitByRegex</s:local>
</s:processor>
<s:link source="NormalizationMethod" sink="AffyDataAnalysis:normMethod" />
<s:link source="arrayTypeAffy" sink="AffyDataAnalysis:arrayType" />
<s:link source="path" sink="AffyDataAnalysis:dirPath" />
<s:link source="samples1" sink="Split_sample1:string" />
<s:link source="Split_sample1:split" sink="AffyDataAnalysis:group1" />
<s:link source="Split_sample2:split" sink="AffyDataAnalysis:group2" />
<s:link source="String_Constant:value" sink="Split_sample1:regex" />
<s:link source="String_Constant:value" sink="Split_sample2:regex" />
<s:link source="samples2" sink="Split_sample2:string" />
<s:link source="AffyDataAnalysis:boxNormPlot" sink="boxNorm" />
<s:link source="AffyDataAnalysis:histRawPlot" sink="hist" />
<s:source name="arrayTypeAffy" />
<s:source name="path" />
<s:source name="NormalizationMethod" />
<s:source name="samples1" />
<s:source name="samples2" />
<s:sink name="hist" />
<s:sink name="boxNorm" />
</s:scufl>
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