[Bioc-devel] Appropriate linear model analysis for microarray time course comparison
sdavis2 at mail.nih.gov
Fri Jun 2 12:40:19 CEST 2006
I think this probably belongs on the bioconductor users list, rather than
the development list. I am cross-posting to there.
On 6/1/06 5:28 PM, "Stephen Park" <stephen.park at ucd.ie> wrote:
> Dear Bioconductor users,
> I'm looking to use linear modelling in Limma (all new to me) to identify
> genes responding differently to infection in two different cattle breeds,
> and would welcome advice on the correct model to use.
> Our experiment took eight animals from each of two genetically distinct
> breeds of cattle varying in susceptibility to a parasite and infected them
> with the parasite. RNA was isolated pre-infection and at five time-points
> post-infection and hybridised to two-colour oligo arrays. A common reference
> RNA obtained by pooling samples from both breeds and all timepoints was
> labelled with the second dye and hybridised to each array.
> We're interested in seeing which genes respond differently to infection in
> the different breeds. I believe the model we should be using is something
> Gene expression level = intercept + (breed effect) + (time effect) + (time *
> breed interaction effect)
> Assuming this is the appropriate model, I would like to correct for innate
> pre-infection differences between breeds and effects due to infection that
> are common to both breeds so that I can find those genes that respond
> differently to infection between breeds, either at a given time-point or
> across the entire timecourse.
> I'd like to know if I ought to set up a different design matrix from that
> generated automatically using the modelMatrix function on my targets and
> identifying the reference
> i.e. modelMatrix(targets,ref="Ref")
> which generates
> T0_B1 T0_B2 ... T0_B2 ...
> T0_B1_A1 1 0 ... 0 ...
> T0_B1_A2 1 0 ... 0 ...
> T1_B1_A1 0 1 ... 0 ...
> T0_B2_A9 0 0 ... 1 ...
> where T=time, B=breed, A=animal
> If this is OK, am I right in thinking that contrasts
> (T1_B1 - T0_B1) - (T1_B2 - T0_B2)
> will remove both the breed component of the model for each breed (within
> brackets) and the component due to infection common to both breeds, leaving
> me with the interaction component?
> Alternatively, should the design matrix contain additional coefficients to
> account for time, breed and interaction components?
> I'd be very grateful for any advice you could offer.
> Dr. Stephen Park
> Animal Genomics Lab
> School of Agriculture, Food Science & Vet. Medicine
> University College Dublin
> Dublin 4
> Tel: +353 (0)1 716 7767
> Fax: +353 (0)1 716 1103
> Mob: +353 (0)87 7666850
> E-mail: stephen.park at ucd.ie
> Web: http://animalgenomics.ucd.ie/sdepark/
> Bioc-devel at stat.math.ethz.ch mailing list
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