Hi Shahenda,
On 1/6/2013 1:50 AM, Shahenda El-Naggar wrote:
> Hi
> I am trying to combine data that was generated from different
microarray platforms (MoGene_10_st and mouse4302)
> when trying to filter genes using P/A calls, I ran into problems
with MoGene since it does not have MM probes to run functions like
mas5call
> instead I used Oligo package. The problem is when I run the function
paCalls and then subset into normalized data I end up with control
probes. To avoid that I am trying to remove control probes first then
apply paCalls function, however, so far I could not find any function
that allow me to do that with GeneFeatureSet which is initially
generated through reading CEL files by oligo library
> this is my code
Removing the controls is fairly simple. I have a function in the devel
version of affycoretools that you can use. Rather than installing the
devel version (for which you should also install the devel version of
R), you can just copy paste the following into your R session, and
then use.
getMainProbes <- function (input) {
if (is(input, "ExpressionSet")) {
pdinfo <- annotation(input)
if (length(grep("^pd", pdinfo)) != 1)
stop(paste("The file", pdinfo, "does not appear to have
been processed using",
"the oligo package.\nIn this case the argument to
this
function should",
"be the name of the correct pd.info package (e.g.,
pd.hugene.1.0.st.v1.\n"),
call. = FALSE)
}
else {
if (is.character(input))
pdinfo <- input
else if (!is.character(input))
stop(paste("The input argument for this function should
either be an ExpressionSet",
"that was generated using oligo, or the name of the
pd.info package",
"that corresponds to your data.\n"), call. = FALSE)
}
require(pdinfo, character.only = TRUE)
con <- db(get(pdinfo))
types <- dbGetQuery(con, paste("select distinct meta_fsetid, type
from featureSet inner join core_mps",
"on featureSet.fsetid=core_mps.fsetid;"))
ind <- types$type %in% 1
dbDisconnect(con)
if (is(input, "ExpressionSet"))
return(input[ind, ])
else return(ind)
}
Note that this is intended for use on summarized data. I don't see
where
you are summarizing the mogene expression data below, and quite
frankly
have no idea what you are trying to accomplish with that code. In
general, I would do something like
eset <- rma(t)
eset <- getMainProbes(eset)
and then eset would contain only probes with a 'main' designation.
Best,
Jim
>
> library(oligo)
> library(pd.mogene.1.0.st.v1)
> geneCELs<- list.celfiles("C:
\Documents and Settings
\All
Users
\Desktop
\Mouse data
\GSE37832_RAW
\MENSC", full.names = TRUE)
> t<-read.celfiles(geneCELs)
> probein<- getProbeInfo(t, probeType = "pm", target = "probeset",
sortBy = "none")
> p<-paCalls(t, method="DABG")
> indsum<- apply(p [,1:6], 1, function(x) sum(x[1:3]< 0.05)> 2 ||
sum(x[4:6]< 0.05)> 2)
> pfinal<- p[insum,]
> probesetids<- subset(probein, probein$fid %in%
as.character(rownames(pfinal)))
> dataset.1.mensc<-subset(mendsc.eset, rownames(mensc.eset) %in%
as.character(probesetids$man_fsetid)) #mensc.est is normalized using
expresso function
> write.csv(dataset.1.mensc, "dataset.1.mensc.csv")
> dim(dataset.1.mensc)
>
>
> Shahenda
>
> [[alternative HTML version deleted]]
>
>
>
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--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099
