[BioC] Theoretical Question

[James MacDonald]

Naomi,

The limma package fits an ANOVA with an adjusted denominator, based on an empirical Bayes procedure. Literature describing the procedure can be found here: http://www.statsci.org/smyth/pubs/ebayes.pdf

Best,

Jim



James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623

>>> Naomi Altman <naomi at stat.psu.edu> 05/30/04 12:25AM >>>
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
>
>_______________________________________________
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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

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