Hi I have a question about data analysis after normalisation I have normalised in limma and applied the generalized least squares linear models to my data with very nice results! My experiment is a Three sample experiment (Three different treatments)compared to a commmon reference. The Three samples have six slides per experiment, 3 in one dye orientation and 3 dye swapped to give 18 slides in total. I have approx 3500 genes in duplicate on my array at present. Currently I have normalised all three sets of data seperately but would like to be able to compare the three data sets. I was thinking of using the mva functions like dist/hclust etc. My questions: Is this the best way of comparing this data or are there other/better methods that could be used that anyone has had experience with. e.g. similar to the two-sample experiment example in limma user guide where results from the linear model and ebayes are displayed with a heatmap ? (sorry I'm presuming that this is the kind of thing I should be doing ?) Also I'm presuming the data I want to use for these methods are the normalised $M values ? OR do I want to use the results from gls.series/lm.series and ebayes for a 3 sample comparison ? please could someone give me an example of the best method they recommend with some commands that I could try using... Thanks very much to anyone who can help Jason -- -------------------------------- Jason Skelton Pathogen Microarrays Wellcome Trust Sanger Institute Hinxton Cambridge CB10 1SA Tel +44(0)1223 834244 Ext 7123 Fax +44(0)1223 494919

At 02:20 AM 14/10/2003, Jason Skelton wrote: >I have a question about data analysis after normalisation >I have normalised in limma and applied the generalized least squares >linear models to my data with very nice results! > >My experiment is a Three sample experiment >(Three different treatments)compared to a commmon reference. >The Three samples have six slides per experiment, 3 in one dye orientation >and 3 dye swapped to give 18 slides in total. >I have approx 3500 genes in duplicate on my array at present. > >Currently I have normalised all three sets of data seperately but would >like to be able to compare the three data sets. >I was thinking of using the mva functions like dist/hclust etc. > >My questions: >Is this the best way of comparing this data or are there other/better >methods that could be used that anyone has had experience with. I would use the limma commands lmFit (or lm.series or gls.series) followed by makeContrasts, eBayes and classifyTests. See the earliers posts: https://stat.ethz.ch/pipermail/bioconductor/2003-September/002406.html https://stat.ethz.ch/pipermail/bioconductor/2003-September/002405.html This would allow you to answer the following questions: 1. Which genes are differentially expressed between each pair of treatments? 2. Which genes are differentially expressed between each treatment and the reference? 3. Which genes show _any_ differences between the treatments? Your problem doesn't sound like a cluster analysis problem to me. Cheers Gordon >e.g. similar to the two-sample experiment example in limma user guide >where results from the linear model and ebayes are displayed with a heatmap ? >(sorry I'm presuming that this is the kind of thing I should be doing ?) > >Also I'm presuming the data I want to use for these methods are the >normalised $M values ? OR do I want to use the results from >gls.series/lm.series and ebayes for a 3 sample comparison ? > >please could someone give me an example of the best method they recommend >with some commands that I could try using... > >Thanks very much to anyone who can help > >Jason