[BioC] filtering probes in affymetrix data
James W. MacDonald
jmacdon at uw.edu
Fri Feb 14 00:21:25 CET 2014
OK, well that's probably excessive. I sort of wonder about Affy and
their control probes. I originally wrote getMainProbes because the
intronic controls were always popping up in lists of differentially
expressed probesets. I wonder if these antigenomic probes are actually
measuring something.
You might be better served to just filter out the controls right after
running rma(), and then use something less conservative like
filt <- filterfun(kOverA(12, 6)
or you could look at the other options in genefilter like varFilter().
Best,
Jim
On Thursday, February 13, 2014 5:48:59 PM, Sabet, Julia A wrote:
> It gives me this:
> Features Samples
> 2426 36
>
> -----Original Message-----
> From: James W. MacDonald [mailto:jmacdon at uw.edu]
> Sent: Thursday, February 13, 2014 5:47 PM
> To: Sabet, Julia A
> Cc: bioconductor at r-project.org
> Subject: Re: [BioC] filtering probes in affymetrix data
>
> How many probesets do you have left after the genefilter step (e.g., what does dim(eset.filt) give you)?
>
> On Thursday, February 13, 2014 4:19:11 PM, Sabet, Julia A wrote:
>> Could it be because I did the kOverA function twice, since the first time I put a 5 and I meant to put a 12? I thought it would just get overrided but maybe it didn't?
>>
>> 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)
>>> minval <- max(bkg)
>>> ind <- genefilter(eset, filterfun(kOverA(5, minval))) eset.filt <-
>>> eset[ind,] ind <- genefilter(eset, filterfun(kOverA(12, minval)))
>>> eset.filt <- eset[ind,]
>>> library(affycoretools)
>> eset.filt <- getMainProbes(eset.filt)
>> Error in orig[[nm]][i, , ..., drop = drop] :
>> (subscript) logical subscript too long
>>
>> -----Original Message-----
>> From: James W. MacDonald [mailto:jmacdon at uw.edu]
>> Sent: Thursday, February 13, 2014 4:05 PM
>> To: Sabet, Julia A
>> Cc: bioconductor at r-project.org
>> Subject: Re: [BioC] filtering probes in affymetrix data
>>
>> OK, what do you get if you type
>>
>> getMainProbes
>>
>> at an R prompt? It shouldn't be subsetting the ExpressionSet twice like that.
>>
>> Jim
>>
>>
>>
>> On Thursday, February 13, 2014 3:59:38 PM, Sabet, Julia A wrote:
>>> I get this:
>>>
>>>> traceback()
>>> 3: input[ind, ]
>>> 2: input[ind, ]
>>> 1: getMainProbes(eset.filt)
>>>>
>>>
>>> -----Original Message-----
>>> From: James W. MacDonald [mailto:jmacdon at uw.edu]
>>> Sent: Thursday, February 13, 2014 3:57 PM
>>> To: Sabet, Julia A
>>> Cc: bioconductor at r-project.org
>>> Subject: Re: [BioC] filtering probes in affymetrix data
>>>
>>> What do you get when you run
>>>
>>> traceback()
>>>
>>> right after that error?
>>>
>>> Jim
>>>
>>>
>>> On 2/13/2014 3:51 PM, Sabet, Julia A wrote:
>>>> Thank you both. Solved that problem. Now when I do the next line of code, I get another error message:
>>>>
>>>>> eset.filt <- getMainProbes(eset.filt)
>>>> Error in orig[[nm]][i, , ..., drop = drop] :
>>>> (subscript) logical subscript too long
>>>>
>>>>
>>>>
>>>> -----Original Message-----
>>>> From: James W. MacDonald [mailto:jmacdon at uw.edu]
>>>> Sent: Thursday, February 13, 2014 3:44 PM
>>>> To: Sabet, Julia A
>>>> Cc: bioconductor at r-project.org
>>>> Subject: Re: [BioC] filtering probes in affymetrix data
>>>>
>>>> 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]]
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> Bioconductor mailing list
>>>>>>> Bioconductor at r-project.org
>>>>>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>>>>>> Search the archives:
>>>>>>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>>>>> --
>>>>>> 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
>>>>
>>>
>>> --
>>> 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
--
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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