[BioC] Is a change in kit significant?

Daniel Brewer daniel.brewer at icr.ac.uk
Fri May 18 18:00:53 CEST 2007

Sorry this has taken me so long, but I have been away.  Unfortunately
the last third contains all experimental, which makes life a bit tricky.
  That said, the rest are a mixture of controls and experimental. If I
did use the linear model would it be fair to say that if say 50% of
probes have a "kit" effect then the kit effect is significant?

Many thanks


James W. MacDonald wrote:
> 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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Daniel Brewer, Ph.D.
Institute of Cancer Research
Email: daniel.brewer at icr.ac.uk

The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP.

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