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Re: Annotation of Human Exon 1.0 ST

rcaloger — 2013-05-19T12:15:09

I wonder if an annotation package used by oneChannelGUI could be useful 
to you.
The packages:

HuExExonProbesetLocation,MoExExonProbesetLocation andRaExExonProbesetLocation are derived
by Affymetrix data annotation and contains the following info:

library(HuExExonProbesetLocation)
as.data.frame(HuExExonProbesetLocation[1:3,])
EPROBESETID  CHR STRAND START  STOP GPROBESETID ANNLEVEL

2315101 chr1      + 11925 12167     2315100 extended
2315102 chr1      + 12657 12692     2315100 extended
2315104 chr1      + 13366 13888     2315100 extended
...

  Gene level annotation can be generated using the stand alone function 
standAloneBuildingLocalAnnotation present in oneChannelGUI package.
This annotation provides the following info:
"PROBESETID","ACC","SYMBOL","DESCRIPTION","CYTOBAND"

Annotation can also be added to a data frame using the stand alone function:
standAloneAddingAnnotation

library(oneChannelGUI)
?standAloneBuildingLocalAnnotation
standAloneBuildingLocalAnnotationpackage:oneChannelGUI R Documentation

Creates a data frame with gene-level annotation data for exon arrays
using the netaffx database

Description:

      Standalone oneChannelGUI function to create gene-level annotation
      data using netaffx database.

Usage:

         standAloneBuildingLocalAnnotation(libDirLocation = getwd(), 
netaffxUser = "myemail-oHC15RC7JGTNLxjTenLetw< at >public.gmane.org", netaffxUserPw = "yourpassword", 
whichAnnotation = c("HuEx", "MoEx", "RaEx"))

Arguments:

libDirLocation: Folder where to save the annotation object

netaffxUser: The email registered to Affymetrix netaffx web site

netaffxUserPw: The password to access to netaffx

whichAnnotation: Which annotation table should be used

Value:

      Location of the annotation data frame.


?standAloneAddingAnnotation
standAloneAddingAnnotation    package:oneChannelGUI    R Documentation

Attach to a data frame containing gene-level data derived from
Affymetrix exon arrays the annotations derived by netaffx

Description:

      Standalone oneChannelGUI function attaches gene-level annotation
      to a data frame.

Usage:

         standAloneAddingAnnotation(annotationdf, df.tobe.annotated, 
ids.column)

Arguments:

annotationdf: An annotation data frame generated with
           standAloneBuildingLocalAnnotation function

df.tobe.annotated: A data frame containing a gene-level data of any
           type. It is mandatory that onne of the column contains
           gene-level ids

ids.column: the column of the df.tobe.annotated containing gene-level
           ids

Value:

      A data frame.



Cheers
Raf

Re: summarizeOverlaps: ambiguous method dispatch

Martin Morgan — 2013-05-19T00:50:17

Yes, this will get addressed, thanks for the heads up. Martin

summarizeOverlaps: ambiguous method dispatch

Michael Lawrence — 2013-05-18T23:38:48

Entering this code:

library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(LungCancerLines)
exons <- exons(TxDb.Hsapiens.UCSC.hg19.knownGene, vals = list(gene_id =
"7157"))
counts <- summarizeOverlaps(exons, LungCancerBamFiles())

Yields:

Note: method with signature 'GAlignments#Vector' chosen for function
'countOverlaps',
 target signature 'GAlignments#GRanges'.
 "Vector#GenomicRanges" would also be valid
Note: method with signature 'GAlignments#Vector' chosen for function
'countOverlaps',
 target signature 'GAlignments#GRanges'.
 "Vector#GenomicRanges" would also be valid

While this is apparently harmless, a couple of questions:

- What is the rationale for defining countOverlaps methods on 'Vector'? If
this is a simple wrapper, why not go all the way to ANY?

- Would it be feasible to avoid these Notes (which probably unnecessarily
worry the user), for example by defining specific methods for GenomicRanges
and GRangesList? I realize that dual dispatch is inherently prone to
ambiguities, but some brute-forcing might be worth the effort.

Michael

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Annotation of Human Exon 1.0 ST

Laura [guest] — 2013-05-18T18:56:48

Hi list,

I am having trouble trying to annotate a Human Exon 1.0 ST expression set. I know little about R and bioconductor and I find it hard to understand the instructions I find on the web.

 When analyzing HGU 133 Plus 2.0 arrays, I just had to follow these instructions:
library("hgu133plus2.db")
symbol <- hgu133plus2SYMBOL
genename <- hgu133plus2GENENAME
symbols <- unlist(as.list(hgu133plus2SYMBOL))
genenames <- unlist(as.list(hgu133plus2GENENAME))
results <-  cbind(symbols,genenames,exprs.eset)
And the âresultsâ would be the expression matrix with genenames and genesymbols associated to each affymetrix gene ID. I am trying to get the same results (the same matrix) for the Human Exon chip.

I have summarized my data to the gene level using the oligo package and created the expression matrix:

geneSummaries <- rma(abatch.raw, target="core")
expressionMatrix <- exprs (geneSummaries)

Now I understand that the best option is to use the biomaRt package to annotate, I have been reading the vignette but I am completely lost as for what I should do to just attach genenames, symbols, or some other identifier to the expression matrix. 

Could somebody tell me which commands or which steps I should follow?

Thank you very much

 -- output of sessionInfo(): 

no sessioninfo

--
Sent via the guest posting facility at bioconductor.org.

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Re: [Bioc] RNAseq less sensitive than microarrays? Is it astatistical issue?

Lucia — 2013-05-18T12:01:21

Dear Simon,

To obtain my count matrix I use each Ucsc gene model as one " transcript", I limit my comparisons with the microarray data to those Ucsc gene models that have a unique RefSeq match with an Affy probe set 

Regarding multiple testing, I guess there is no reason why I can't use the uncorrected pvalues produced by DESeq and run locfdr right?

Thanks for the help
Lucia

Sent from my iPad

On May 16, 2013, at 3:02 PM, Simon Anders <anders-ge2Rx4WF3PI< at >public.gmane.org> wrote:


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Re: R crash on getBM query

Sean Davis — 2013-05-18T11:29:35

Hi, Francesco.

If you could send the output of sessionInfo() after loading biomaRt,
that would be helpful.  Also, could you send the error output?  Note
that the biomaRt has been down at least intermittently for the last
day or so, so your query might not complete.  The error output will
hopefully help to determine what the issue is, though.

Sean


On Fri, May 17, 2013 at 11:27 AM, Mazzarotto, Francesco
<f.mazzarotto-AQ/gCgVxFfnQzY9nttDBhA< at >public.gmane.org> wrote:

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problem with aveLogCPM.default in edgeR

Gordon K Smyth — 2013-05-18T09:57:11

You can avoid the problem by forming a DGEList:

   y <- DGEList(counts=exprs(dataNormgcOff))
   y$offset <- -offst(dataNormgcOff)

Then

   y <- estimateGLMCommonDisp(y, design)

etc.

Gordon





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analyze three factors in Limma

Gordon K Smyth — 2013-05-18T09:30:53

Dear Ingrid,



It's easy to include three, or indeed any number, of factors in a limma 
model.


You could, but why would you want to?


Hard to give any specific advice on the basis of the information you give. 
However, if you are not familiar with setting up model formula in R for 
factorial experiments, then the limma User's Guide strongly recommends 
that you setup your experiment as a oneway layout as per Section 8.5.2. 
This works for any number of factors.

Best wishes
Gordon


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Re: Creating an OrganismDbi package with a few transcriptannotations

Michael Lawrence — 2013-05-18T04:56:24

Cool, thanks Martin. I'll wait for Marc to get back. If what you say is
correct, it would be nice to have a simple data frame implementation. I'm
getting the annotations from a biomart, so a biomart implementation would
be ideal, although that might be tricky semantically.

Michael




On Fri, May 17, 2013 at 5:43 PM, Martin Morgan <mtmorgan-q9hIisBwmLrYtjvyW6yDsg< at >public.gmane.org> wrote:


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Re: Creating an OrganismDbi package with a few transcript annotations

Martin Morgan — 2013-05-18T00:43:28

Hi Michael -- Marc is away for a few days. I *think* the idea is that the 
details in NewSchema are no longer required, rather, implement your extra data 
in any fashion to provide a 'select' interface, i.e.,

   keytypes
   keys
   cols
   select

following the implied API of ?keytypes. Then create an OrgDb package with

   AnnotationDbi::makeOrganismPackage

Sorry not to be more definitive in my help.

Martin

Re: QuasR: how to use an indexed reference genome?

Martin Morgan — 2013-05-18T00:31:06

We've been experimenting with AnnotationHub (the package, and the cloud resource

Re: QuasR: how to use an indexed reference genome?

Tim Triche, Jr. — 2013-05-17T23:53:15

same here, I would like to use QuasR more often, the reason I use Rsubread
(instead of say gmapR or quasR) is simply that I know where my indexed
genomes are and how to regenerate them if need be

this is a big deal for lazy people like me ;-)



On Fri, May 17, 2013 at 7:51 AM, Paul Shannon <
paul.thurmond.shannon-Re5JQEeQqe8AvxtiuMwx3w< at >public.gmane.org> wrote:

Re: [devteam-bioc] sort error in GRange

Valerie Obenchain — 2013-05-17T23:20:55

Hi Emily,

We are phasing out the RangedData class for the GRanges class.

You can coerce your RangedData to a GRanges and then sort.

     gr <- as(allorigins, "GRanges")
     sort(gr)

You probably read the BED file in with a function from rtracklayer. In 
the future you can set 'asRangedData=FALSE' and a GRAnges will be 
created instead of a RangedData.

Let me know if you have trouble sorting the GRanges.

Valerie

On 04/30/2013 11:37 AM, Maintainer wrote:

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Re: QuasR: how to use an indexed reference genome?

Paul Shannon — 2013-05-17T14:51:17

Thanks, Michael.

May I cast my one vote, as a new user of Quasr, for an extra measure of transparency?  I have lost a few days to my confusion.

My reasoning -- which might be idiosyncratic, and which you may find unconvincing -- is that if qAlign, in some invocations (but not all) needs to spend a few hours creating an index, and writes it to a place which is not entirely obvious (lib.loc), then I as a user am mightily confused.  As I have been! :}

I would have been much better off  if qAlign had told me:

  1) you specified a genome package by name for your reference
  2) in order to use that, qAlign needs for it to be indexed
  3) we cannot find an indexed version of that genome in your R library or any lib.loc directory
  4) please specify an alternative lib.loc, and explicit path, or…
  5) create a new index for your genome using this command:
        buildIndexAsPackage(genomePackageName, destinationDir)  # or some such method call
  6) you can then specify that newly-built index package in subsequent calls to qAlign this way:
        qAlign(samplesFile, genome="~/path/to/genome-index-package.tar.gz")

 - Paul

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Re: QuasR: how to use an indexed reference genome?

Paul Shannon — 2013-05-17T13:01:29

Hi Michael,

Thanks for your quick and clarifying response. 

Since it is not possible to use pre-built indices from the bowtie developers, I would be glad to have a small recipe (perhaps featured prominently in the vignette?) which

  1) explains the need for custom-built indices
  2) provides (perhaps) a standalone QuasR-specific command for creating one

My somewhat fuzzy grasp of the current approach is that 

  1) QuasR sees the string "BSgenome.Hsapiens.UCSC.hg19" on a call to qAlign
  2) QuasR then spends a few hours building a new package with the proper index
  3) and saves this package somewhere (I could not figure out where)

I agree that a one-time cost to create a QuasR index is quite reasonable, given all the benefits of the package.

Maybe it's just my own muddle-headedness, but I could not -- and still cannot :} -- figure out how to do it, where the result is written, and thus how to reuse the index on subsequent runs.

Can you give me some assistance?

Many thanks,

 - Paul




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R crash on getBM query

Mazzarotto, Francesco — 2013-05-17T15:27:24

Good afternoon,

I am contacting you for an information regarding biomaRt, I have already contacted the BioMart helpdesk but they suggested to email you, as this is probably a bug of the biomaRt package.

Every time I try to get annotation info on a SNP using biomaRt, the R console crashes. The session info are:

R version 3.0.1 (2013-05-16)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base


And the commands that I use are:

library(biomaRt)
snp.db = useMart("ENSEMBL_MART_SNP",host="www.ensembl.org<http://www.ensembl.org>",dataset="hsapiens_snp")
variant = list(9,133271654)
var_info = getBM(c("chr_name","chrom_start","allele","minor_allele","minor_allele_freq"),c("chr_name","chrom_start"),variant,snp.db)

R always crashes at this point, also on other computers. I am using R 3.0.1 and the biomaRt package is up to date.

Where could the problem be?

Thanks a lot for the assistance!
Francesco Mazzarotto


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Re: PAM: Applying published classifiers

Ryan C. Thompson — 2013-05-17T22:14:16

Take my advice with a grain of salt. I've just started working with PAM 
and I'm not certain of all the particulars.

On Fri 17 May 2013 03:02:23 PM PDT, Ed Siefker wrote:

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Re: PAM: Applying published classifiers

Ed Siefker — 2013-05-17T22:02:23


Thank you, this is why I got so stuck.  The table listed in their paper
that's labeled "classifier" is not actually a classifier.   Do you have any
idea what the list of centroids is used for if not to create a classifier?
Wouldn't it be more useful to publish something like
"pamrtrained.RData.gz", so people can just download it, load the object and
start classifying?




One of the papers I'm working off of (DeSousa 2013, doi:10.1038/nm.3174)
has a flow chart in the supplementary figures that shows how they trained
the classifier from one dataset of 90 patients, and then applied that
classifier to 5 different datasets from several different platforms.  There
are no loops in the flow chart that indicate they retrained the classifier
for each dataset.  Are they doing it wrong, or is this a valid procedure?

I hope DeSousa 2013 is on topic, as they even provided a bioconductor
package to repeat their analysis.  I can use that package to recreate their
classifier pretty easily, but others aren't so convenient.  Thanks a bunch
for the clarification.
-Ed

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Re: PAM: Applying published classifiers

Ryan C. Thompson — 2013-05-17T20:43:51

I can't see how the output of pamr.listgenes would be sufficient to 
reproduce a trained classifier. I think your only choice would be to 
re-run PAM starting from the CEL files.

Also, consider whether their classifier would even be applicable to 
your microarray samples, since your samples and theirs are normalized 
separately. If you have a bunch of your own samples that you wish to 
classify, the correct approach might be to normalize the training 
samples and your samples together as one dataset and then re-train the 
classifier, rather than use the exact centroids computed on the 
original normalized data.  In other words, repeating the training 
yourself may be the only statistically valid choice anyway.

On Fri 17 May 2013 01:28:46 PM PDT, Ed Siefker wrote:

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Re: PAM: Applying published classifiers

James W. MacDonald — 2013-05-17T20:43:43

Hi Ed,

There are multiple reasons why you are getting no traction.

1.) The pamr package isn't a Bioconductor package. Just because you are 
using microarrays doesn't mean this is a BioC question.

2.) You are basically telling us that you read something and the authors 
of what you read created something and you want it to be acceptable as 
input for a given function.

Setting aside the non-BioC nature of your question, how is anybody on 
this list supposed to help? I guess we could track down the papers, or 
perhaps load up the pamr package and try to replicate what you are 
trying to do, but to quote Sweet Brown, "ain't nobody got time fo dat" 
(http://www.youtube.com/watch?v=8cT_Ulmcrys). This is why you are 
requested to include a small, complete code example of what you are 
trying to do, which would help people see what the problem is.

You will probably get more traction on R-help, or by contacting the 
authors of the paper you are reading, or perhaps the authors of pamr 
(although good luck with that...).

Best,

Jim



On 5/17/2013 4:28 PM, Ed Siefker wrote:

Re: PAM: Applying published classifiers

Ed Siefker — 2013-05-17T20:28:46

Can someone nudge me in the right direction here?   Am I trying to do
something
that isn't possible?  Am I trying to do something that's so obvious it
hasn't been
documented?  Am I just unaware of where the appropriate documentation is?
Any advice would be greatly appreciated.  Thanks
-Ed


On Wed, May 15, 2013 at 1:24 PM, Ed Siefker <ebs15242-Re5JQEeQqe8AvxtiuMwx3w< at >public.gmane.org> wrote:


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