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MethylMix is an R package that identifies hyper and hypomethylated genes using a beta mixture modeling approach.
In this regard we have also verified that using a beta mixture model with more than three states does not improve the overall type2 fit.
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Given two types of probes designed on the 450 k array display differing β value distributions, adjustment for probe design bias was applied using a beta-mixture quantile normalization method (BMIQ) 19.
We used strict marker filtering, followed by an HMM where transition and emission probabilities were estimated sample-wise using a beta-mixture model on the estimated parental allele frequencies within sliding windows.
We modeled the resulting P values using a beta-uniform mixture model.
A recent work by (Paisley and Carin, 2009) implemented such a model for factor mixtures using a beta process prior.
The PM03 method uses a beta-uniform mixture model to fit the histogram of p-values.
Beta values were normalized using the BMIQ (Beta Mixture Quantile dilation) method.
The probesets were quality filtered for adjusted detection p-values <0.05, batch corrected with functional normalization [ 44], and then normalized across the two probe types using beta mixture quantile normalization (BMIQ) [ 45], all implemented in the watermelon package [ 46].
Here we give the rationale of using uniform or beta mixture to simulate p-values under null and alternative hypotheses.
For inference, data were clustered using a mixture model [48] with a mixture of beta distributions [49], and the number of classes was determined by Bayesian information criterion (BIC) [50], [51].
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