[BioC] Theoretical Question

[A.J. Rossini]

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
>
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