[BioC] Is a change in kit significant?

[James W. MacDonald]

Hi Daniel,

I had a different interpretation of what you wanted than what Francois 
mentions here. Did the last third of the samples contain all sample 
types (e.g., they aren't all just experimental or control)?

If so, you could always fit a linear model to the data that includes a 
kit effect. You will then be able to test for each probeset if the 'kit' 
parameter is equal to zero or not.

When you mention putting a statistic on it, is this what you mean?

Best,

Jim

Francois Pepin wrote:
> Hi Daniel,
> 
> I'm assuming that there should not be any differences between the arrays
> with the different kits. If they did the healthy samples first and the
> diseased ones on the new kit, then you obviously won't be able to
> differentiate between the biological and the kit effect.
> 
> There are a few ways you could know if the differences are significant.
> If clustering clearly separates samples that should be similar, then you
> could use bootstrap (like the pvclust package) to determine
> significance. You could also look at the probability to get X
> differentially expressed probes/exons/genes between the kits compared to
> random permutations of your samples. There should be a number of other
> ways to get a p-value out of it.
> 
> I hope this helps,
> 
> Francois
> 
> On Wed, 2007-05-09 at 16:39 +0100, Daniel Brewer wrote: 
> 
>>Hi
>>
>>I have a set of Affymetrix Exon data which has about 40 samples.  The
>>last third of the samples have used a different kit for the experiment,
>>and I have been asked to determine whether the change in kit is significant.
>>
>>I have done clustering and PCA and the results suggest it does make a
>>different, but I would like to put some sort of statistic on it.  What
>>is the best way to do this?  I would think maybe this is a limma type
>>problem but I am not sure how to get an overall statistic rather than
>>just for individual probes.
>>
>>Many thanks
>>
>>Dan
>>
> 
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623


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