[BioC] Read single channel GenePix in limma [was: Analyze miRNA experiment in Bioconductor]

Wed May 14 14:29:53 CEST 2008

On Wed, May 14, 2008 at 7:54 AM, Paul Geeleher <paulgeeleher at gmail.com> wrote:
> Hi Gordon,
>
> Thanks for you email. I've followed your steps and am getting some output now.
>
> One problem though. When should the summarization step occur? What I
> mean is that, between miRNA and control signals, my GPR file contains
> about 3000 entries and when I am done with analysis topTable will
> return all of these individually. But many of the miRNAs have multiple
> entries in the ".gpr" file. So how, and when, should I go about
> combining these into one value?

Paul,

What is the manufacturer of these arrays?  The summarization method
may depend on that somewhat.

Sean
> On Sun, May 11, 2008 at 4:59 AM, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>> Dear Paul,
>>
>>  The limma User's Guide doesn't discuss how to read single channel data, but
>> how to do this has been described half a dozen times on this mailing list.
>> Since limma is designed for two colours, you can fool it by giving two
>> column names and ignoring the one you don't need.  If you only have the Cy3
>> channel foreground for example you might use
>>
>>   Cy3 <- "F532 Mean"
>>   RG <- read.maimages(source="genepix",columns=list(R=Cy3,G=Cy3))
>>
>>  then
>>
>>   RG$R <- NULL
>>
>>  to remove the extraneous values.
>>
>>  Then RG$G could be given as input to vsnMatrix() and the output analysed
>> with lmFit().
>>
>>  Please don't edit your GenePix files manually, there's no need.  It's prone
>> to introducing errors and is non-reproducible.
>>
>>  The error message "number of items to replace is not a multiple of
>> replacement length" is not caused by having only one channel.  limma gives a
>> far more informative message in that case.  The most likely explanation is
>> that your GenePix files are not of equal lengths.  If that is indeed the
>> problem, then the limma package doesn't offer any easy solution.  Your only
>> approach would be to read the files in individually, then align the
>> expression values yourself.
>>
>>  You cannot use read.maimages() with source="imagene" because you do not
>> have ImaGene files.
>>
>>  Best wishes
>>  Gordon
>>
>>
>>
>> > Date: Fri, 9 May 2008 15:54:39 +0100
>> > From: "Paul Geeleher" <paulgeeleher at gmail.com>
>> > Subject: Re: [BioC] Analyze miRNA experiment in Bioconductor
>> > To: "Wolfgang Huber" <huber at ebi.ac.uk>
>> > Cc: bioconductor at stat.math.ethz.ch
>> >
>> > Doesn't seem to be anything in the users guide specific to this kind
>> > of analysis unfortunately.
>> >
>> > -Paul
>> >
>> > On Thu, May 8, 2008 at 10:31 AM, Wolfgang Huber <huber at ebi.ac.uk> wrote:
>> >
>> > > Dear Paul,
>> > >
>> > >
>> > > > Hmm interesting. I might try introducing the extra columns into the
>> > > > files and specifying all the values as 0. I can't see why that
>> > > > shouldn't work?
>> > > >
>> > >
>> > > It might, but Narendra's suggestion of reading the limma users guide is
>> a
>> > > worthwhile option to consider.
>> > >
>> > >  Best wishes
>> > >       Wolfgang
>> > >
>> > > ------------------------------------------------------------------
>> > > Wolfgang Huber  EBI/EMBL  Cambridge UK  http://www.ebi.ac.uk/huber
>> > >
>> > >
>> > > >
>> > > > -Paul
>> > > >
>> > > > On Wed, May 7, 2008 at 1:39 PM, Narendra Kaushik
>> > > > <kaushiknk at cardiff.ac.uk> wrote:
>> > > >
>> > > > >
>> > > > > You can specify your red channel like this:
>> > > > >
>> > > > >  RG <- read.maimages(files,source="genepix",  columns=list(R="F635
>> > > > > Median",G="F532
>> > > > >  Median",Rb="B635",Gb="B532"))
>> > > > >
>> > > > >  I will suggest you read limma guide.
>> > > > >
>> > > > >  But I think your have data from Imagene package which gives one
>> file for
>> > > > > each channel, you can:
>> > > > >
>> > > > >  files <- targets[,c("FileNameCy3","FileNameCy5")]
>> > > > >  RG <- read.maimages(files, source="imagene")
>> > > > >
>> > > > >  Hope, this helps
>> > > > >
>> > > > >  Narendra
>> > > > >
>> > > > > >>> "Paul Geeleher" <paulgeeleher at gmail.com> 07/05/2008 13:24:01 >>>
>> > > > >
>> > > > >
>> > > > > Hi Deepayan,
>> > > > >
>> > > > >  Thanks for your reply. I suppose my main concern is how I should
>> read
>> > > > >  in the data initially in order to be able to use the normal tools
>> to
>> > > > >  analyze the data. Reading the data normally like this:
>> > > > >
>> > > > >  RG <- read.maimages( files, source="genepix")
>> > > > >
>> > > > >  Gives the following error:
>> > > > >
>> > > > >  Error in RG[[a]][, i] <- obj[, columns[[a]]] :
>> > > > >  number of items to replace is not a multiple of replacement length
>> > > > >
>> > > > >
>> > > > >  I'm assuming this is down to the fact that the files only contain
>> > > > >  intensity data for one color rather than two?
>> > > > >
>> > > > >  How should I go about reading the data?
>> > > > >
>> > > > >  Thanks alot,
>> > > > >
>> > > > >  -Paul.
>> > > > >
>> > > > >  On Tue, May 6, 2008 at 10:15 PM, Deepayan Sarkar
>> > > > >  <deepayan.sarkar at gmail.com> wrote:
>> > > > > > On 5/6/08, Paul Geeleher <paulgeeleher at gmail.com> wrote:
>> > > > > > > Dear Members,
>> > > > > > >
>> > > > > > >  I've inherited a bunch of GenePix files from an miRNA
>> experiment.
>> > > > > They
>> > > > > > >  are single color arrays, ( as opposed to 2 color as is the norm
>> > > > > for
>> > > > > > >  GenePix I think). There is a subset of 7 arrays and I wish to
>> > > > > compare
>> > > > > > >  a group of 4 of these to the other group of 3 and analyze
>> > > > > differential
>> > > > > > >  expression between the two groups. I was hoping somebody could
>> > > > > point
>> > > > > > >  me in the right direction of how I'd go about doing this with
>> > > > > > >  Bioconductor? Is it possible using the Limma package? Is there
>> any
>> > > > > > >  code out there to assist me?
>> > > > > > >
>> > > > > > >  I've experience in analyzing Affymetrix data using Limma and
>> PUMA,
>> > > > > but
>> > > > > > >  not GenePix, and the Limma Users Guide seems to focus on
>> analyzing
>> > > > > two
>> > > > > > >  dye experiments.
>> > > > > >
>> > > > > >  Any analysis ultimately boils down to some sort of normalization,
>> and
>> > > > > >  the actual differential expression analysis. The second part in
>> limma
>> > > > > >  (lmFit, etc.) can work with any expression matrix, irrespective
>> of
>> > > > > >  whether it's 2-color or 1-color (or affy).
>> > > > > >
>> > > > > >  We have been working with a miRNA array dataset recently, and we
>> used
>> > > > > >  limma to read in the GPR files and do the differential expression
>> > > > > >  analysis (on one channel). For normalization, many of the
>> standard
>> > > > > >  microarray algorithms probably don't make much sense, but VSN
>> seems
>> > > > > to
>> > > > > >  work fine.
>> > > > > >
>> > > > > >  We don't really have code (beyond what's already in limma and
>> vsn)
>> > > > > >  that is generally useful; most of the work is in figuring out
>> which
>> > > > > >  rows are of interest (i.e., those representing human miRNAs),
>> > > > > >  combining the replicates (you seem to have four of each), etc.
>> I'm
>> > > > > >  happy to give you more details if you are interested.
>> > > > > >
>> > > > > >  -Deepayan
>> > > > >
>> > > >
>> > >
>> >
>>
>
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