[BioC] filtering probes in affymetrix data

[James W. MacDonald]

Hi Julia,

You should always include the output from sessionInfo() with any 
questions, so we can see what versions you are running, and what you 
have loaded.

My guess is you are using an old version of R, prior to the 
introduction of that function, or you forgot to do 
library(affycoretools).

Best,

Jim

On Thursday, February 13, 2014 12:03:54 PM, Sabet, Julia A wrote:
> Thank you so much, Jim.  I did everything you recommended and everything seemed to be working and then I installed the affycoretools package and when I did:
> eset.filt <- getMainProbes(eset.filt)
>
> This error resulted:
> Error: could not find function "getMainProbes"
>
> What should I do?
> Thanks!
> Julia
>
>
> -----Original Message-----
> From: James W. MacDonald [mailto:jmacdon at uw.edu]
> Sent: Thursday, February 13, 2014 9:36 AM
> To: Sabet, Julia A
> Cc: bioconductor at r-project.org
> Subject: Re: [BioC] filtering probes in affymetrix data
>
> Hi Julia,
>
> There are several different things you can do. I'll show you one possibility.
>
> First, note that there are multiple different control probes on this array that aren't intended to measure differential expression, and should be excluded. So first let's look at the possible types of
> probesets:
>
>> library(pd.mogene.2.0.st)
>> con <- db(pd.mogene.2.0.st)
>> dbGetQuery(con, "select * from type_dict;")
>     type                   type_id
> 1     1                      main
> 2     2             control->affx
> 3     3             control->chip
> 4     4 control->bgp->antigenomic
> 5     5     control->bgp->genomic
> 6     6            normgene->exon
> 7     7          normgene->intron
> 8     8  rescue->FLmRNA->unmapped
> 9     9  control->affx->bac_spike
> 10   10            oligo_spike_in
> 11   11           r1_bac_spike_at
>
> These are all the possible types of probesets, but we don't have all of them on this array. To see which ones we do have we can do:
>
>
>> table(dbGetQuery(con, "select type from featureSet;")[,1])
>
>       1      2      4      7      9
> 263551     18     23   5331     18
>
> So we only have these probeset types:
>
> 1     1                      main
> 2     2             control->affx
> 4     4 control->bgp->antigenomic
> 7     7          normgene->intron
> 9     9  control->affx->bac_spike
>
> And the 'main' probesets are those that we want to use for differential
> expression. Now one thing you could do is to say that the antigenomic
> probesets should give a good measure of background, as they are
> supposed to have sequences that don't exist in mice. So you could just
> extract those probesets, get some measure and use that as the lower
> limit of what you think is expressed or not. That's pretty naive, as a
> probe with higher GC content will have higher background than one with
> a lower GC content, but worrying about that is way beyond what I am
> prepared to go into.
>
> Now we can get the probeset IDs for the antigenomic probesets
>
> antigm <- dbGetQuery(con, "select meta_fsetid from core_mps inner join
> featureSet on core_mps.fsetid=featureSet.fsetid where
> featureSet.type='4';")
>
> And then extract those probesets and get a summary statistic.
>
> bkg <- apply(exprs(eset)[as.character(antigm[,1]),], 2, quantile,
> probs=0.95)
>
> Which will give us the 95th percentile of these background probes. You
> could then use the kOverA function in genefilter to filter out any
> probesets where all samples are below the background values. The idea
> being that you want to filter out any probesets unless k samples have
> expression levels >= A. So if you have 10 samples, where 5 are controls
> and 5 are treated, you would do something like
>
> minval <- max(bkg)
> ind <- genefilter(eset, filterfun(kOverA(5, minval)))
> eset.filt <- eset[ind,]
>
> You should also filter out all the non-main probesets. You can do that
> using getMainProbes() in the affycoretools package
>
> eset.filt <- getMainProbes(eset.filt)
>
> Best,
>
> Jim
>
>
>
>
> On Wednesday, February 12, 2014 10:16:31 PM, Sabet, Julia A wrote:
>> Hello all,
>> I am totally new to R/Bioconductor and have begun processing data from my Affymetrix Mouse Gene 2.0 ST arrays.  I normalized the data like this:
>>
>> library(pd.mogene.2.0.st)
>> eset <- rma(affyRaw)
>>
>> and added gene annotation and I am following the limma user's guide, which recommends removing "probes that appear not be
>> expressed in any of the experimental conditions."  I have read on previous posts that filtering may not be necessary.  Should I filter, and if so, how?  Using what code?
>>
>> Thank you!
>> Julia Sabet
>>
>> 	[[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

--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099