[BioC] Theoretical Question

[Naomi Altman]

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
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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