[BioC] Theoretical Question

[A.J. Rossini]

Q1: Limma, yes.   The others, I should know, sigh...

Q2: Limma will compute an FDR.  I need to recall which one though (you'd
think I'd remember it....).

--tony

Naomi Altman <naomi at stat.psu.edu> writes:

> I know that you can get adjusted denominators for the F-tests from
> these packages, but what about for the contrasts?



> Also, suppose you do something like "multiple comparisons with
> control" or "all pairwise comparisons".  Should you feed the adjusted
> p-values into FDR, or feed all of your p-values to FDR?



>
> --Naomi
>
> At 07:44 AM 6/1/2004 -0700, A.J. Rossini wrote:
>
>>Some tools that help:
>>
>>1. limma will do empirical bayes adjustments for the linear models
>>    (ANOVA), so that would be one approach.
>>2. EBarrays as well (different methodology).
>>3. there is always siggenes for doing SAM-style analyses within R.
>>
>>best,
>>-tony
>>
>>Naomi Altman <naomi at stat.psu.edu> writes:
>>
>> > I would use ANOVA  (lm or lme) followed by a contrast.  It would
>> > likely be better to adjust the denominator (like SAM) but I don't
>> > think there is any software for this (or literature on exactly how to
>> > do it).  So, probably the best thing for now is to treat this as a
>> > 1-way ANOVA with say a Bonferroni correction (for each gene). Once you
>> > have the Bonferroni-corrected p-values, you use FDR to determine an
>> > appropriate p-value to select genes.
>> >
>> > --Naomi
>> >
>> > At 02:10 PM 5/19/2004 -0400, Luckey, John wrote:
>> >> I posted a similar question last week and received some help with
>> >> this problem, but I am still a bit unclear on the best way to
>> >> proceed- any insights would be greatly appreciated.
>> >>
>> >> I want to identify a set of genes that are co-regulated with a given
>> >> phenotype that is observed across various tissue types -to ID the
>> >> 'signature' that corresponds to the phenotype regardless of tissue-
>> >>
>> >>
>> >>
>> >> Here is the simplest set up: (all data is affymetrix and has been
>> >> pre-processed/normalized by rma)
>> >>
>> >>
>> >>
>> >>Tissue type A has 3 conditions: 1A, 2A, 3A
>> >>
>> >>Type B has 4 conditions: 1B, 2B, 3B, 4B
>> >>
>> >>
>> >>
>> >>My phenotype of interest is observed only in 1A and 1B.
>> >>
>> >>
>> >>
>> >> I am interested in knowing what is common (both up and down
>> >> regulated) between 1A (relative only to 2A and 3A) and 1B (relative
>> >> to 2B, 3B, and 4B).  I have varying numbers of replicates per
>> >> condition (2-5).
>> >>
>> >>
>> >>
>> >> I have done unsupervised clustering using all genes, and 1A and 1B
>> >> don't cluster together (not really surprising since they are quite
>> >> different in many respects , I am interested only in their
>> >> overlapping phenotypes). I am not entirely sure how best to proceed.
>> >>
>> >>
>> >>
>> >> I have used straight fold change to ID unique genes in 1A vs 2A and
>> >> 1A vs 3A. I then select those genes up (or down) in 1A in both
>> >> comparisons. I then look at how the ‘1A specific’ genes are
>> >> expressed in 1B vs all other B's- and there is a general positive
>> >> skewing- but the concern is where to draw cutoffs- how to estimate
>> >> FDR, etc in such a comparison. Basically, how does one go about
>> >> saying that the skewing in a different comparison of a subset of
>> >> genes is significant?
>> >>
>> >>
>> >>
>> >>Any insights you might have would be appreciated.
>> >>
>> >>
>> >>
>> >>Thx
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>John Luckey, MD PhD
>> >>
>> >>Clinical Pathology Resident - Brigham and Womens Hospital
>> >>
>> >>Post Doctoral Fellow  -          Mathis - Benoist Lab
>> >>
>> >>Joslin Diabetes Center
>> >>
>> >>One Joslin Place, Rm. 474
>> >>
>> >>Boston, MA  02215
>> >>
>> >>_______________________________________________
>> >>Bioconductor mailing list
>> >>Bioconductor at stat.math.ethz.ch
>> >>https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>> >
>> > Naomi S. Altman                                814-865-3791 (voice)
>> > Associate Professor
>> > Bioinformatics Consulting Center
>> > Dept. of Statistics                              814-863-7114 (fax)
>> > Penn State University                         814-865-1348 (Statistics)
>> > University Park, PA 16802-2111
>> >
>> > _______________________________________________
>> > Bioconductor mailing list
>> > Bioconductor at stat.math.ethz.ch
>> > https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>> >
>>
>>--
>>rossini at u.washington.edu            http://www.analytics.washington.edu/
>>Biomedical and Health Informatics   University of Washington
>>Biostatistics, SCHARP/HVTN          Fred Hutchinson Cancer Research Center
>>UW (Tu/Th/F): 206-616-7630 FAX=206-543-3461 | Voicemail is unreliable
>>FHCRC  (M/W): 206-667-7025 FAX=206-667-4812 | use Email
>>
>>CONFIDENTIALITY NOTICE: This e-mail message and any attachments may be
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>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
>
>

-- 
rossini at u.washington.edu            http://www.analytics.washington.edu/ 
Biomedical and Health Informatics   University of Washington
Biostatistics, SCHARP/HVTN          Fred Hutchinson Cancer Research Center
UW (Tu/Th/F): 206-616-7630 FAX=206-543-3461 | Voicemail is unreliable
FHCRC  (M/W): 206-667-7025 FAX=206-667-4812 | use Email

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