<p>Hi everybody,</p>

<p>I am currently working on several projects using Illumina 450k DNA Methylation Microarrays. In order to detect Differentially Methylated Probes (DMP), I usually employ the Empirical Bayes-based method in the <em>limma</em> package. Based on the <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3012676/?tool=pmcentrez&amp;report=abstract">paper by Pan Du et al</a>, I stick with M-values to fit the statistical model, and usually employ beta-values just in graphs and reports that are going to be read by fellow biologists.</p>

<p>In order to retain only results with a certain biological relevance, we usually apply a threshold on effect size, keeping only the probes that are significant and with a big effect size. Nothing new or fancy to this point. However, it is not uncommon to argue with fellows at the lab about the benefits or drawbacks of M-values against beta-values when trying to set a coherent effect size threshold.</p>

<p>Mathematical properties of M-values let us set a fixed threshold on effect size, while this is not that easy for betas. However, setting a threshold on M-values differences (say 1.4, as stated in the previous paper) usually results in a set of probes where a lot of them seem to present very small differences in beta-values (say, for example, 0.01), specially those near the minimum and maximum. This is very counterintuitive for a biologist, who is going to argue against it based on the fact that such a small difference means nothing from a biological point of view.</p>

<p>My mental idea of what&#39;s happening there is related to the technical bias of the 450k. This is, I try to convince people that a small difference in that region is as credible as a bigger difference in the middle region (around 0.5 in beta), due to the array design, but I do not think if I am right, or if I can even see correctly what is going on.</p>

<p>What do you usually do in your pipelines? Use M-values for the fit, and beta-values differences as thresholds for effect sizes? A first threshold on M-values and a second filtering step based on betas? Everything with betas? Nothing at all?</p>

<p>Any hint will be much appreciated.</p>

<p>Using M-values for statistics is by far the best way to go.</p>

<p>Regarding the utility of beta values, it&#39;s good to filter by those as well. Why? Because a 1% change in methylation is unlikely to be biologically meaningful. I&#39;ve seen a number of papers publish such changes, but it&#39;s always interesting to note that they never bother to show functional relevance (probably because there is none). If you can show the biological relevance of such small changes then go ahead and follow up on them. Personally, I want to prioritize the results on the likelihood that it&#39;s causing some phenotype and using methylation changes for that (and only that) makes sense.</p>

<p><strong>Edit:</strong> There&#39;s corollary to other types of data. For example, we can often detect small changes in highly-expressed genes when we do RNAseq. Some of these are meaningful, but the really small changes aren&#39;t. I wouldn&#39;t toss these results, but I also wouldn&#39;t recommend anyone pursue them first when doing a follow-up.</p>
