[BioC] Limma analysis of focused arrays vs. whole genome arrays

Gordon Smyth smyth at wehi.edu.au
Wed Jun 8 12:23:24 CEST 2005

>Date: Tue, 7 Jun 2005 09:33:51 -0400
>From: Mike Schaffer <mschaff at bu.edu>
>Subject: [BioC] Limma analysis of focused arrays vs. whole genome
>         arrays
>To: bioconductor at stat.math.ethz.ch
>The lab I work with has used "whole genome" human arrays (~18,000
>genes) for a couple years and I have helped with the analysis using
>Limma.  Now, due to costs, they are now considering switching from
>whole genome arrays to focused arrays with ~400 genes of interest
>(selected from the whole-genome array results).
>The obvious analysis problems with a focused array where most genes are
>changing are:
>1. LOESS normalization assumes most genes are not changing.  If most of
>the genes are expected to change, there is no basis to recenter the
>data around zero.  The response from the lab was that they would be
>willing to include 100-150 genes that are not expected to change.
>2. The B-statistic in Limma requires a parameter indicating a certain
>fraction of genes are changing.  The corresponding moderated
>t-statistic uses the data from all genes to moderate the standard error
>in the t calculation.  Both of these could change dramatically if most
>of the genes on the array are changing.
>My questions are:
>1. Are my concerns valid and are there ways around around them?  Are
>there other analysis pitfalls with this scenario?
>2. Can Limma handle situations where most of an array is expected to
>change?  What modifications, if any, need to be made to the Limma
>analysis to account for this?

To quote from the Limma User's Guide (page 15):

"In such a situation, the best strategy is to include on the arrays a 
series of non-differentially
expressed control spots, such as a titration series of whole-library-pool 
spots, and to use the
up-weighting method discussed below. In the absence of the such control 
spots, normalization
of boutique arrays requires specialist advice."

>3. Alternatively, is there a more appropriate statistical package to
>use in this case?

I don't know of any other available methods. In my opinion, you have to put 
down control spots, "house-keeping" genes if that is all you can get, but 
preferably constructed spots as described above.



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