[BioC] filtering probes in affymetrix data
James W. MacDonald
jmacdon at uw.edu
Thu Feb 13 21:44:22 CET 2014
Hi Julia,
On 2/13/2014 3:23 PM, Sabet, Julia A wrote:
> Thank you Jim. I think my R version is up to date and I am making sure to use "library()". I started the whole thing over and now I have this new error message, at an earlier step:
>
> 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
>> table(dbGetQuery(con, "select type from featureSet;")[,1])
> 1 2 4 7 9
> 263551 18 23 5331 18
>> antigm <- dbGetQuery(con, "select meta_fsetid from core_mps inner join
> + featureSet on core_mps.fsetid=featureSet.fsetid where
> + featureSet.type='4';")
>> bkg <- apply(exprs(eset)[as.character(antigm[,1]),], 2, quantile,
> + probs=0.95)
>> library(genefilter)
>> minval <- max(bkg)
>> ind <- genefilter(eset, filterfun(kOverA(5, minval))) eset.filt <-
The above line has a bit extra at the end that R doesn't like.
> Error: unexpected symbol in "ind <- genefilter(eset, filterfun(kOverA(5, minval))) eset.filt"
And this is your hint. Error messages are your friends.
Best,
Jim
>> ind <- genefilter(eset, filterfun(kOverA(12, minval))) eset.filt <- eset[ind,]
> Error: unexpected symbol in "ind <- genefilter(eset, filterfun(kOverA(12, minval))) eset.filt"
>> ind <- genefilter(eset, filterfun(kOverA(12, minval))) eset.filt <-
> Error: unexpected symbol in "ind <- genefilter(eset, filterfun(kOverA(12, minval))) eset.filt"
> Here is my sessionInfo() output:
>
> R version 3.0.2 (2013-09-25)
> Platform: x86_64-w64-mingw32/x64 (64-bit)
>
> locale:
> [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
> [4] LC_NUMERIC=C LC_TIME=English_United States.1252
>
> attached base packages:
> [1] parallel stats graphics grDevices utils datasets methods base
>
> other attached packages:
> [1] BiocInstaller_1.12.0 genefilter_1.44.0 mogene20sttranscriptcluster.db_2.13.0
> [4] org.Mm.eg.db_2.10.1 AnnotationDbi_1.24.0 pd.mogene.2.0.st_2.12.0
> [7] RSQLite_0.11.4 DBI_0.2-7 oligo_1.26.1
> [10] Biostrings_2.30.1 XVector_0.2.0 IRanges_1.20.6
> [13] Biobase_2.22.0 oligoClasses_1.24.0 BiocGenerics_0.8.0
>
> loaded via a namespace (and not attached):
> [1] affxparser_1.34.0 affyio_1.30.0 annotate_1.40.0 bit_1.1-11 codetools_0.2-8 ff_2.2-12
> [7] foreach_1.4.1 GenomicRanges_1.14.4 iterators_1.0.6 preprocessCore_1.24.0 splines_3.0.2 stats4_3.0.2
> [13] survival_2.37-7 tools_3.0.2 XML_3.98-1.1 xtable_1.7-1 zlibbioc_1.8.0
> I appreciate your help...
> Julia
>
> -----Original Message-----
> From: James W. MacDonald [mailto:jmacdon at uw.edu]
> Sent: Thursday, February 13, 2014 12:08 PM
> To: Sabet, Julia A
> Cc: bioconductor at r-project.org
> Subject: Re: [BioC] filtering probes in affymetrix data
>
> 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
--
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099
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