[BioC] vsn preprocess with oligo package
José López
jose.lopez at umh.es
Fri Jan 11 23:22:18 CET 2013
Ooops. Sorry for the question. I use to check CEL images and to
further analyze quality with arrayQualityMetrics but yes, I didn't
realize that the GeneFeatureSet was indicating the total number of
probes in the array.
Thank you again for your kind help,
Best,
Jose
El ene 11, 2013, a las 6:46 p.m., James W. MacDonald escribió:
> Hi Jose,
>
> On 1/11/2013 12:05 PM, José LÓPEZ wrote:
>> Hi,
>>
>> I have one more question. The GeneFeatureSet has 1102500 features
>> while the vsn2 is done on 899636 features no matter whether target
>> in pm is "core" (default method) or "probeset". Why vsn2 does not
>> use all the features? Is that correct/normal? After sumarization,
>> as expected, I have 35556 features. It is possible that the
>> normalization on only part of the features can introduce some kind
>> of bias in the summarization process (899636 in stead of 1102500)?
>
> I think you misunderstand a basic tenet of Affymetrix arrays. Not
> all features on an Affy array are used to measure expression of
> transcript. There are for instance thousands of features that go all
> the way around the outside of the array (the oligo-dT features) that
> are there only to help the scanner align itself to the chip. There
> are also two big blocks of features in the middle that are not
> measuring transcript either.
>
> You can see this if you do something like
>
> tmp <- log2(as.numeric(exprs(raw([,1]))))
> geom <- geometry(getPD(raw))
> ## convert back to a matrix
> tmp <- matrix(tmp, ncol = geom[1], nrow = geom[2])
> ## reorder because image() is weird
> tmp <- as.matrix(rev(as.data.frame(tmp)))
> image(tmp[1:100,1:100])
>
> This shows the (still transposed) top left corner of the chip. The
> checkerboard in the corner, and all the features along the top and
> side are oligo-dT probes used to align. The chip name is made up of
> oligo-dT features (and blanks) as well and there primarily I suppose
> to look cool.
>
> If you just do image(tmp), you will see the big blocks in the middle
> of the array.
>
> Does that help?
>
> Best,
>
> Jim
>
>
>
>
>>
>> I think the alternative to this option is i.e. make the CDF file
>> and environment (to avoid the unofficial CDF) and make vsnrma in
>> affy but, since oligo was designed ad hoc (to analyze Gene and Exon
>> affymetrix arrays), I though it makes sense to try to find your
>> help to combine vsn and oligo.
>>
>> Thank you for your answer,
>>
>> Jose
>>
>> El ene 11, 2013, a las 5:42 p.m., José LÓPEZ escribió:
>>
>>> Yes, the exprs() did the job and it is allowing me to combine vsn
>>> with oligo and Gene ST arrays.
>>>
>>> Thank you again for your kind help,
>>>
>>> Best,
>>>
>>> Jose
>>>
>>>
>>> > library(limma)
>>> > library(oligo)
>>> > Data=read.celfiles(list.celfiles())
>>> Loading required package: pd.mogene.1.0.st.v1
>>> Loading required package: RSQLite
>>> Loading required package: DBI
>>> Platform design info loaded.
>>> Reading in : ABRNA1.CEL
>>> Reading in : ABRNA2.CEL
>>> Reading in : ABRNA3.CEL
>>> Reading in : ABRNA4.CEL
>>> Reading in : ABRNA5.CEL
>>> Reading in : ABRNA6.CEL
>>> > Data
>>> GeneFeatureSet (storageMode: lockedEnvironment)
>>> assayData: 1102500 features, 6 samples
>>> element names: exprs
>>> protocolData
>>> rowNames: ABRNA1.CEL ABRNA2.CEL ... ABRNA6.CEL (6 total)
>>> varLabels: exprs dates
>>> varMetadata: labelDescription channel
>>> phenoData
>>> rowNames: ABRNA1.CEL ABRNA2.CEL ... ABRNA6.CEL (6 total)
>>> varLabels: index
>>> varMetadata: labelDescription channel
>>> featureData: none
>>> experimentData: use 'experimentData(object)'
>>> Annotation: pd.mogene.1.0.st.v1
>>> > class(Data)
>>> [1] "GeneFeatureSet"
>>> attr(,"package")
>>> [1] "oligoClasses"
>>> > raw=backgroundCorrect(Data,"rma")
>>> Background correcting... OK
>>> > pms=pm(raw)
>>> > head(pms)
>>> ABRNA1.CEL ABRNA2.CEL ABRNA3.CEL ABRNA4.CEL ABRNA5.CEL
>>> ABRNA6.CEL
>>> 2106 7.853059 7.635413 5.221570 5.160448 5.978796
>>> 5.767533
>>> 2107 5.990083 4.764031 4.659925 5.160448 7.067603
>>> 5.903628
>>> 2108 4.867173 4.587342 5.023095 4.762256 5.978796
>>> 6.045105
>>> 2109 10.657556 8.827158 7.679404 6.124607 11.523816
>>> 6.045105
>>> 2110 12.538396 13.906774 6.147958 5.860049 38.764281
>>> 6.843026
>>> 2111 10.241785 6.356910 4.836138 5.160448 7.067603
>>> 7.405633
>>> > class(pms)
>>> [1] "matrix"
>>> > pmsVSN=vsn::vsnMatrix(pms)
>>> vsn2: 899636 x 6 matrix (1 stratum). Please use 'meanSdPlot' to
>>> verify the fit.
>>> > class(pmsVSN)
>>> [1] "vsn"
>>> attr(,"package")
>>> [1] "vsn"
>>> > pmsVSN
>>> vsn object for n=899636 features and d=6 samples.
>>> sigsq=0.1
>>> hx: 899636 x 6 matrix.
>>> > pm(raw) <- exprs(pmsVSN)
>>> > rm(pms, pmsVSN)
>>> > ls()
>>> [1] "Data" "raw"
>>> > raw
>>> GeneFeatureSet (storageMode: lockedEnvironment)
>>> assayData: 1102500 features, 6 samples
>>> element names: exprs
>>> protocolData
>>> rowNames: ABRNA1.CEL ABRNA2.CEL ... ABRNA6.CEL (6 total)
>>> varLabels: exprs dates
>>> varMetadata: labelDescription channel
>>> phenoData
>>> rowNames: ABRNA1.CEL ABRNA2.CEL ... ABRNA6.CEL (6 total)
>>> varLabels: index
>>> varMetadata: labelDescription channel
>>> featureData: none
>>> experimentData: use 'experimentData(object)'
>>> Annotation: pd.mogene.1.0.st.v1
>>> > eset=rma(raw, background=FALSE,normalize=FALSE)
>>> Calculating Expression
>>> > eset
>>> ExpressionSet (storageMode: lockedEnvironment)
>>> assayData: 35556 features, 6 samples
>>> element names: exprs
>>> protocolData
>>> rowNames: ABRNA1.CEL ABRNA2.CEL ... ABRNA6.CEL (6 total)
>>> varLabels: exprs dates
>>> varMetadata: labelDescription channel
>>> phenoData
>>> rowNames: ABRNA1.CEL ABRNA2.CEL ... ABRNA6.CEL (6 total)
>>> varLabels: index
>>> varMetadata: labelDescription channel
>>> featureData: none
>>> experimentData: use 'experimentData(object)'
>>> Annotation: pd.mogene.1.0.st.v1
>>>
>>> El ene 11, 2013, a las 5:07 p.m., James W. MacDonald escribió:
>>>
>>>> Hi Jose,
>>>>
>>>> On 1/11/2013 10:31 AM, José LÓPEZ wrote:
>>>>> Dear Jim,
>>>>>
>>>>> Thank you for the advise on the background correction step.
>>>>> I already tryed before the whole Benilton's code but it doesn't
>>>>> work at the following step.
>>>>>
>>>>>> pm(raw)<- pmVSN
>>>>> Error: object 'pmVSN' not found
>>>>>
>>>>> May I ask you what this step is doing? Does it replace the pm
>>>>> matrix in the raw ExpressionFeatureSet by the normalized one?
>>>>
>>>> Exactly. But the 'pm <-' function expects to be fed a matrix,
>>>> and the pmsVSN isn't a matrix. Instead, it is a 'vsn' object,
>>>> which is related to an ExpressionSet object. So you can extract
>>>> the matrix of normalized data as usual, with exprs():
>>>>
>>>> pm(raw) <- exprs(pmsVSN)
>>>>
>>>> and there was another error in my code, as summarize() won't work
>>>> on a GeneFeatureSet object. Instead, you want to use rma():
>>>>
>>>> eset <- rma(raw, normalize = FALSE, background = FALSE)
>>>>
>>>> Best,
>>>>
>>>> Jim
>>>>
>>>>
>>>>
>>>>>
>>>>> Thank you in advance for your time and your kind help,
>>>>>
>>>>> Jose
>>>>>
>>>>>> library(limma)
>>>>>> library(oligo)
>>>>>> Data=read.celfiles(list.celfiles())
>>>>> Loading required package: pd.mogene.1.0.st.v1
>>>>> Loading required package: RSQLite
>>>>> Loading required package: DBI
>>>>> Platform design info loaded.
>>>>> Reading in : ABRNA1.CEL
>>>>> Reading in : ABRNA2.CEL
>>>>> Reading in : ABRNA3.CEL
>>>>> Reading in : ABRNA4.CEL
>>>>> Reading in : ABRNA5.CEL
>>>>> Reading in : ABRNA6.CEL
>>>>>> pms=pm(Data)
>>>>>> raw=backgroundCorrect(Data,"rma")
>>>>> Background correcting... OK
>>>>>> pms=pm(raw)
>>>>>> pmsVSN=vsn::vsnMatrix(pms)
>>>>> vsn2: 899636 x 6 matrix (1 stratum). Please use 'meanSdPlot' to
>>>>> verify the fit.
>>>>>> pm(raw)<- pmVSN
>>>>> Error: object 'pmVSN' not found
>>>>>> pm(raw)<- pmsVSN
>>>>> Error in function (classes, fdef, mtable) :
>>>>> unable to find an inherited method for function ‘pm<-’ for
>>>>> signature ‘"GeneFeatureSet", "missing", "missing", "vsn"’
>>>>>> pmsVSN
>>>>> vsn object for n=899636 features and d=6 samples.
>>>>> sigsq=0.1
>>>>> hx: 899636 x 6 matrix.
>>>>>> head(pms)
>>>>> ABRNA1.CEL ABRNA2.CEL ABRNA3.CEL ABRNA4.CEL ABRNA5.CEL
>>>>> ABRNA6.CEL
>>>>> 2106 7.853059 7.635413 5.221570 5.160448 5.978796
>>>>> 5.767533
>>>>> 2107 5.990083 4.764031 4.659925 5.160448 7.067603
>>>>> 5.903628
>>>>> 2108 4.867173 4.587342 5.023095 4.762256 5.978796
>>>>> 6.045105
>>>>> 2109 10.657556 8.827158 7.679404 6.124607 11.523816
>>>>> 6.045105
>>>>> 2110 12.538396 13.906774 6.147958 5.860049 38.764281
>>>>> 6.843026
>>>>> 2111 10.241785 6.356910 4.836138 5.160448 7.067603
>>>>> 7.405633
>>>>>> class(pms)
>>>>> [1] "matrix"
>>>>>> ls()
>>>>> [1] "Data" "pms" "pmsVSN" "raw"
>>>>>
>>>>>> sessionInfo()
>>>>> R version 2.15.2 (2012-10-26)
>>>>> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>>>>>
>>>>> locale:
>>>>> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
>>>>>
>>>>> attached base packages:
>>>>> [1] stats graphics grDevices utils datasets methods
>>>>> base
>>>>>
>>>>> other attached packages:
>>>>> [1] pd.mogene.1.0.st.v1_3.8.0 RSQLite_0.11.2
>>>>> DBI_0.2-5 oligo_1.22.0
>>>>> [5] Biobase_2.18.0 oligoClasses_1.20.0
>>>>> BiocGenerics_0.4.0 limma_3.14.3
>>>>>
>>>>> loaded via a namespace (and not attached):
>>>>> [1] affxparser_1.30.0 affy_1.36.0
>>>>> affyio_1.26.0 BiocInstaller_1.8.3 Biostrings_2.26.2
>>>>> [6] bit_1.1-9 codetools_0.2-8
>>>>> ff_2.2-10 foreach_1.4.0 GenomicRanges_1.10.5
>>>>> [11] grid_2.15.2 IRanges_1.16.4
>>>>> iterators_1.0.6 lattice_0.20-10 parallel_2.15.2
>>>>> [16] preprocessCore_1.20.0 splines_2.15.2
>>>>> stats4_2.15.2 vsn_3.26.0 zlibbioc_1.4.0
>>>>>
>>>>>
>>>>> *****************************************************
>>>>> José P. LÓPEZ-ATAYALA
>>>>> Instituto de Neurociencias
>>>>> CSIC - UMH
>>>>> Avda. D. Santiago Ramón y Cajal, S/N
>>>>> E-03550, Sant Joan d'Alacant
>>>>> Alicante, Spain
>>>>> jose.lopez at umh.es <mailto:jose.lopez at umh.es>
>>>>> http://in.umh.es/grupos-detalle.aspx?grupo=30
>>>>> (34) 965 919 531
>>>>>
>>>>> El ene 11, 2013, a las 3:59 p.m., James W. MacDonald escribió:
>>>>>
>>>>>> Hi Jose,
>>>>>>
>>>>>> Let's say you followed Benilton's code from https://stat.ethz.ch/pipermail/bioconductor/2010-June/033936.html
>>>>>>
>>>>>> library(oligo)
>>>>>> cels = list.celfiles()
>>>>>> raw = read.celfiles(cels)
>>>>>> raw = backgroundCorrect(raw, "rma") ## I added this - you might
>>>>>> want BG correction
>>>>>> pms = pm(raw)
>>>>>> pmsVSN = vsn::vsnMatrix(pms)
>>>>>> pm(raw)<- pmVSN
>>>>>> rm(pms, pmsVSN)
>>>>>>
>>>>>> you now have an ExpressionFeatureSet with normalized data that
>>>>>> you want to summarize. You can then
>>>>>>
>>>>>> eset<- summarize(raw, method = "medianpolish")
>>>>>>
>>>>>> See
>>>>>>
>>>>>> ?summarizationMethods
>>>>>>
>>>>>> for more information.
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Jim
>>>>>>
>>>>>>
>>>>>> On 1/11/2013 6:17 AM, José LÓPEZ wrote:
>>>>>>> Dear Benilton,
>>>>>>>
>>>>>>> I am using oligo for Mouse Gene 1.0ST arrays. In addition to
>>>>>>> RMA, I would also like to pre-process with vsn. I have seen
>>>>>>> previous threads related to this question in the past, (https://stat.ethz.ch/pipermail/bioconductor/2010-January/031100.html
>>>>>>> , https://stat.ethz.ch/pipermail/bioconductor/2010-June/033936.html)
>>>>>>> , but unfortunately, I am not bioinformatician and, although I
>>>>>>> read oligo and vsn manuals, it is not easy to me to follow up
>>>>>>> to summarize the vsn object.
>>>>>>> May you (or someone else) please, give me some additional clue
>>>>>>> to sumarize the vsn object using the oligo package.
>>>>>>>
>>>>>>> Thank you very much in advance for your time and your kind help,
>>>>>>>
>>>>>>> Jose LOPEZ
>>>>>>>
>>>>>>> **************************
>>>>>>>
>>>>>>>> list.files()
>>>>>>> [1] "ABRNA1.CEL"
>>>>>>> "ABRNA2.CEL" "ABRNA3.CEL"
>>>>>>> [4] "ABRNA4.CEL"
>>>>>>> "ABRNA5.CEL" "ABRNA6.CEL"
>>>>>>> [7] "Limma_FilterBefore_H2BGFP_jla_vsn.R"
>>>>>>>> library(limma)
>>>>>>>> library(oligo)
>>>>>>> Loading required package: BiocGenerics
>>>>>>>
>>>>>>> Attaching package: ‘BiocGenerics’
>>>>>>>
>>>>>>> The following object(s) are masked from ‘package:stats’:
>>>>>>>
>>>>>>> xtabs
>>>>>>>
>>>>>>> The following object(s) are masked from ‘package:base’:
>>>>>>>
>>>>>>> anyDuplicated, cbind, colnames, duplicated, eval, Filter,
>>>>>>> Find, get, intersect, lapply, Map, mapply,
>>>>>>> mget, order, paste, pmax, pmax.int, pmin, pmin.int,
>>>>>>> Position, rbind, Reduce, rep.int, rownames, sapply,
>>>>>>> setdiff, table, tapply, union, unique
>>>>>>>
>>>>>>> Loading required package: oligoClasses
>>>>>>> Loading package bit 1.1-9
>>>>>>> package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)
>>>>>>> creators: bit bitwhich
>>>>>>> coercion: as.logical as.integer as.bit as.bitwhich which
>>>>>>> operator: !& | xor != ==
>>>>>>> querying: print length any all min max range sum summary
>>>>>>> bit access: length<- [ [<- [[ [[<-
>>>>>>> for more help type ?bit
>>>>>>> Loading package ff2.2-10
>>>>>>> - getOption("fftempdir")=="/var/folders/U+/U
>>>>>>> +SFMmqcEbKkSysJQ3OYbk+++TQ/-Tmp-//RtmpKgQzWD"
>>>>>>>
>>>>>>> - getOption("ffextension")=="ff"
>>>>>>>
>>>>>>> - getOption("ffdrop")==TRUE
>>>>>>>
>>>>>>> - getOption("fffinonexit")==TRUE
>>>>>>>
>>>>>>> - getOption("ffpagesize")==65536
>>>>>>>
>>>>>>> - getOption("ffcaching")=="mmnoflush" -- consider
>>>>>>> "ffeachflush" if your system stalls on large writes
>>>>>>>
>>>>>>> - getOption("ffbatchbytes")==16777216 -- consider a different
>>>>>>> value for tuning your system
>>>>>>>
>>>>>>> - getOption("ffmaxbytes")==536870912 -- consider a different
>>>>>>> value for tuning your system
>>>>>>>
>>>>>>> Welcome to oligoClasses version 1.20.0
>>>>>>> Loading required package: Biobase
>>>>>>> Welcome to Bioconductor
>>>>>>>
>>>>>>> Vignettes contain introductory material; view with
>>>>>>> 'browseVignettes()'. To cite Bioconductor, see
>>>>>>> 'citation("Biobase")', and for packages
>>>>>>> 'citation("pkgname")'.
>>>>>>>
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>>>>>>> ================================================================
>>>>>>> Welcome to oligo version 1.22.0
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>>>>>>> ================================================================
>>>>>>>
>>>>>>> Attaching package: ‘oligo’
>>>>>>>
>>>>>>> The following object(s) are masked from ‘package:limma’:
>>>>>>>
>>>>>>> backgroundCorrect
>>>>>>>
>>>>>>>> Data=read.celfiles(list.celfiles())
>>>>>>> Loading required package: pd.mogene.1.0.st.v1
>>>>>>> Loading required package: RSQLite
>>>>>>> Loading required package: DBI
>>>>>>> Platform design info loaded.
>>>>>>> Reading in : ABRNA1.CEL
>>>>>>> Reading in : ABRNA2.CEL
>>>>>>> Reading in : ABRNA3.CEL
>>>>>>> Reading in : ABRNA4.CEL
>>>>>>> Reading in : ABRNA5.CEL
>>>>>>> Reading in : ABRNA6.CEL
>>>>>>>> pms=pm(Data)
>>>>>>>> class(pms)
>>>>>>> [1] "matrix"
>>>>>>>> head(pms)
>>>>>>> ABRNA1.CEL ABRNA2.CEL ABRNA3.CEL ABRNA4.CEL ABRNA5.CEL
>>>>>>> ABRNA6.CEL
>>>>>>> 2106 46 43 36 36
>>>>>>> 37 33
>>>>>>> 2107 38 32 33 36
>>>>>>> 43 34
>>>>>>> 2108 31 31 35 34
>>>>>>> 37 35
>>>>>>> 2109 54 46 45 40
>>>>>>> 58 35
>>>>>>> 2110 58 55 40 39
>>>>>>> 94 40
>>>>>>> 2111 53 39 34 36
>>>>>>> 43 43
>>>>>>>> pmsVSN=vsn::vsnMatrix(pms)
>>>>>>> vsn2: 899636 x 6 matrix (1 stratum). Please use 'meanSdPlot'
>>>>>>> to verify the fit.
>>>>>>>> class(pmsVSN)
>>>>>>> [1] "vsn"
>>>>>>> attr(,"package")
>>>>>>> [1] "vsn"
>>>>>>>> pmsVSN
>>>>>>> vsn object for n=899636 features and d=6 samples.
>>>>>>> sigsq=0.026
>>>>>>> hx: 899636 x 6 matrix.
>>>>>>>> eset=rma(pmsVSN, background=FALSE,normalize=FALSE)
>>>>>>> Error in function (classes, fdef, mtable) :
>>>>>>> unable to find an inherited method for function ‘rma’ for
>>>>>>> signature ‘"vsn"’
>>>>>>>
>>>>>>>> library(vsn)
>>>>>>>> meanSdPlot(pmsVSN)
>>>>>>> KernSmooth 2.23 loaded
>>>>>>> Copyright M. P. Wand 1997-2009
>>>>>>>
>>>>>>>> sessionInfo()
>>>>>>> R version 2.15.2 (2012-10-26)
>>>>>>> Platform: i386-apple-darwin9.8.0/i386 (32-bit)
>>>>>>>
>>>>>>> locale:
>>>>>>> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/
>>>>>>> en_US.UTF-8
>>>>>>>
>>>>>>> attached base packages:
>>>>>>> [1] stats graphics grDevices utils datasets
>>>>>>> methods base
>>>>>>>
>>>>>>> other attached packages:
>>>>>>> [1] vsn_3.26.0 pd.mogene.1.0.st.v1_3.8.0
>>>>>>> RSQLite_0.11.2 DBI_0.2-5
>>>>>>> [5] oligo_1.22.0 Biobase_2.18.0
>>>>>>> oligoClasses_1.20.0 BiocGenerics_0.4.0
>>>>>>> [9] limma_3.14.3
>>>>>>>
>>>>>>> loaded via a namespace (and not attached):
>>>>>>> [1] affxparser_1.30.0 affy_1.36.0
>>>>>>> affyio_1.26.0 BiocInstaller_1.8.3 Biostrings_2.26.2
>>>>>>> [6] bit_1.1-9 codetools_0.2-8
>>>>>>> ff_2.2-10 foreach_1.4.0 GenomicRanges_1.10.5
>>>>>>> [11] grid_2.15.2 IRanges_1.16.4
>>>>>>> iterators_1.0.6 KernSmooth_2.23-8 lattice_0.20-10
>>>>>>> [16] parallel_2.15.2 preprocessCore_1.20.0
>>>>>>> splines_2.15.2 stats4_2.15.2 tools_2.15.2
>>>>>>> [21] zlibbioc_1.4.0
>>>>>>>
>>>>>>>
>>>>>>> [[alternative HTML version deleted]]
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> Bioconductor mailing list
>>>>>>> Bioconductor at r-project.org <mailto: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
>
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