The fact that the dye effect is often highly significant is the reason that it is recommended to include it in the model. Gordon > Date: Wed, 20 Jul 2005 08:21:23 +1000 > From: Mark Pinese <z3062573 at="" student.unsw.edu.au=""> > Subject: [BioC] Significant dye bias using limma > To: bioconductor at stat.math.ethz.ch > > Hello all, > > I have some questions regarding whether the significant dye bias I'm finding in > my analyses could be an artefact of my analysis method. > > I've been using limma to analyse a simple design comparing treatment and control > cases using dye swaps. As per suggestions in the recent limma Users' Guide, > I've added an intercept term to the design, and used it to find genes with > significant dye effects. limma reports very many significantly dye- biased genes > (B-values as high as 12.7, 205 genes with B > 5), and very few significantly > differentially-expressed genes (highest B = 3.1). > > I'm using three biological replicates, each hybridised to two dye- swapped arrays > as technical replicates, on Compugen human 19k cDNA slides. > > Is such a strong result plausible, or due to me incorrectly analysing the data? > If so, what major pitfalls could I have blundered into? What sort of > diagnostics can I try to test how reliable the model results are? > > > Thanks for your time, > > Mark Pinese

Will such a significant bias affect the validity of my treatment effect results? In other words, can I just appreciate that dye bias is rampant, then ignore it and confidently extract meaningful statistics from my treatment vs control coefficient? Mark Gordon K Smyth wrote: >The fact that the dye effect is often highly significant is the reason that it is recommended to >include it in the model. > >Gordon > > >> >>Is such a strong result plausible, or due to me incorrectly analysing the data? >> If so, what major pitfalls could I have blundered into? What sort of >>diagnostics can I try to test how reliable the model results are? >> >> >>