[BioC] help to proceed with the interpretation of an analysis

[Sean Davis]

On Tue, Jul 26, 2011 at 5:50 AM, Alberto Goldoni
<alberto.goldoni1975 at gmail.com> wrote:
> Dear Samuel,
> i have affymetrix rattus norvegius chip.
>
> i have found 1000 interesting genes in the comparison 1 between
> "hypertensive rats (A)" vs "standard rats (B)" and i would like to
> understand the behaviour of these 1000 genes in the comparison 2
> "hypertensive rats+omega3 (C)" vs "standard rats (B)"
>
> After that i would like to obtain a sort of "value" in order to estimate the
> "% change" of each gene from the comparison 2 (C vs B) respect the
> comparison 1 (A vs B).

Hi, Alberto.

It sounds like the biologic hypothesis that you want to test is that
omega3 has an effect on some genes in hypertensive rats?  If that is
correct, I would suggest doing the comparison of C vs A using limma or
some other package for differential expression (perhaps the same way
you compared A with B and C with B).

Sean

> best regards
>
> 2011/7/26 Samuel Wuest <wuests at tcd.ie>
>
>> Dear Alberto,
>>
>> I am not quite sure how your experimental design looks like in detail,
>> but it seems to me that you have dual-label microarray data and an
>> unconnected design with two factors (hence the two separate
>> comparison?). If this is the case, then there is a very nice
>> functionality in the limma package that is designed to make
>> separate-channel analyses from unconnected designs, including
>> appropriate between-array normalizations etc; thus, you can test any
>> contrast you like and also interactions....
>>
>> Everything is nicely described in the limma users guide; simply type:
>>
>> library(limma)
>> limmaUsersGuide()
>>
>> and check Chapter 9 " Separate Channel Analysis of Two-Color Data".
>>
>> Would this answer the question? Hope it helps anyway,
>>
>> Best, Sam
>>
>> On 26 July 2011 10:12, Alberto Goldoni <alberto.goldoni1975 at gmail.com>
>> wrote:
>> > Dear all,
>> > i have a doubt on the way to proceed in analyzing my data.
>> >
>> > Let me explain what i would like to obtain: i have 1000 genes obtained
>> from
>> > the comparison 1 (A vs B) and i would like to obtain the Fold Change and
>> > pvalue of this 1000 genes in the comparison 2 (C vs B).
>> >
>> > After that what i'm gonna to do is to obtain a sort of "value" in order
>> to
>> > estimate the "% change" of each gene from the comparison 2 respect the
>> > comparison 1 and i don't think that comparing the FC (experiment A) with
>> the
>> > FC (experiment B) is enought!
>> >
>> > Has anyone any comments or suggestions about how to extract a value
>> > that allows me to evaluate the change of each gene obtained in the
>> > comparison 2 respect the comparison 1?
>> >
>> > Thanks to anyone who will help me.
>> >
>> > Best regards.
>> >
>> > --
>> > -----------------------------------------------------
>> > Dr. Alberto Goldoni
>> > Parma, Italy
>> > -----------------------------------------------------
>> >
>> >        [[alternative HTML version deleted]]
>> >
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>> >
>> >
>>
>>
>>
>> --
>> -----------------------------------------------------
>> Samuel Wuest
>> Smurfit Institute of Genetics
>> Trinity College Dublin
>> Dublin 2, Ireland
>> Phone: +353-1-896 2444
>> Web: http://www.tcd.ie/Genetics/wellmer-2/index.html
>> Email: wuests at tcd.ie
>> ------------------------------------------------------
>>
>
>
>
> --
> -----------------------------------------------------
> Dr. Alberto Goldoni
> Parma, Italy
> -----------------------------------------------------
>
>        [[alternative HTML version deleted]]
>
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