Greetings I have an experiment: > design(dse) ~ factor1 + factor2 + factor3 where factor1 has two levels, factor2 has three levels and factor3 has three levels. I extract a gene of interest from the results for each term (I've changed the indices to reflect the condition): > lapply(resultsNames(dse),function(u) results(dse,u)["gene_A",]) [["Intercept"]] baseMean log2FoldChange pvalue FDR gene_A 1596.548 10.77485 3.309439e-216 7.025442e-216 [["factor1_level2"]] baseMean log2FoldChange pvalue FDR gene_A 1596.548 0.3386776 0.1307309 0.3587438 [["factor2_level2"]] baseMean log2FoldChange pvalue FDR gene_A 1596.548 -0.6882543 0.0613569 0.1007896 [["factor2_level3"]] baseMean log2FoldChange pvalue FDR gene_A 1596.548 0.2393368 0.513216 0.6589575 [["factor3_level2"]] baseMean log2FoldChange pvalue FDR gene_A 1596.548 0.1584153 0.6423634 0.8503163 [["factor3_level3]] baseMean log2FoldChange pvalue FDR gene_A 1596.548 -1.627898 1.823141e-06 0.001409384 I want to be sure I understand the output format. Is it true that the coefficients (the vector beta) from the fit are the baseMean value scaled by the log2FoldChange? Is the true intercept value 1596.548*2^10.77485=2797274.13? mcols() tells me that the baseMean term is calculated over "all rows". The baseMean is different for different genes although it is the same for each gene across all the conditions, I'm not seeing how the rows are selected. Thanks Mike Michael Muratet, Ph.D. Senior Scientist HudsonAlpha Institute for Biotechnology mmuratet at hudsonalpha.org (256) 327-0473 (p) (256) 327-0966 (f) Room 4005 601 Genome Way Huntsville, Alabama 35806

Hi Michael, The baseMean column is not on the log scale; it is the mean of normalized counts for a gene. The intercept from the GLM is labelled intercept in mcols(dse). Mike On Mar 28, 2013 5:00 PM, "Michael Muratet" <mmuratet@hudsonalpha.org> wrote: > Greetings > > I have an experiment: > > > design(dse) > ~ factor1 + factor2 + factor3 > > where factor1 has two levels, factor2 has three levels and factor3 has > three levels. I extract a gene of interest from the results for each term > (I've changed the indices to reflect the condition): > > > lapply(resultsNames(dse),function(u) results(dse,u)["gene_A",]) > [["Intercept"]] > baseMean log2FoldChange pvalue FDR > gene_A 1596.548 10.77485 3.309439e-216 7.025442e-216 > [["factor1_level2"]] > baseMean log2FoldChange pvalue FDR > gene_A 1596.548 0.3386776 0.1307309 0.3587438 > [["factor2_level2"]] > baseMean log2FoldChange pvalue FDR > gene_A 1596.548 -0.6882543 0.0613569 0.1007896 > [["factor2_level3"]] > baseMean log2FoldChange pvalue FDR > gene_A 1596.548 0.2393368 0.513216 0.6589575 > [["factor3_level2"]] > baseMean log2FoldChange pvalue FDR > gene_A 1596.548 0.1584153 0.6423634 0.8503163 > [["factor3_level3]] > baseMean log2FoldChange pvalue FDR > gene_A 1596.548 -1.627898 1.823141e-06 0.001409384 > > I want to be sure I understand the output format. Is it true that the > coefficients (the vector beta) from the fit are the baseMean value scaled > by the log2FoldChange? Is the true intercept value > 1596.548*2^10.77485=2797274.13? > > mcols() tells me that the baseMean term is calculated over "all rows". The > baseMean is different for different genes although it is the same for each > gene across all the conditions, I'm not seeing how the rows are selected. > > Thanks > > Mike > > Michael Muratet, Ph.D. > Senior Scientist > HudsonAlpha Institute for Biotechnology > mmuratet@hudsonalpha.org > (256) 327-0473 (p) > (256) 327-0966 (f) > > Room 4005 > 601 Genome Way > Huntsville, Alabama 35806 > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]