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

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@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