[BioC] Analyze miRNA experiment in Bioconductor

[Paul Geeleher]

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
>