In wolski/p3003PBC: Slides and R exercises for FGCZ Bioinformatics course

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Modelling of proteomics data

linear models
mixed effect linear models
Limma
ROPECA
msqRob
makes empirical bayes available for .

Limma

Limma (developed for genomics) employs empirical bayes
Empirical Bayes methods are procedures for statistical inference in which the prior distribution is estimated from the data.
Bayesian would start with a prior derived from pilot experiment or expert knowledge and update it using the data using the bayes rule.
Advantage
Works for all types linear models.
Disadvantage
You can't perform peptide level modelling directly - peptide measurements are correlated measures.

Limma

Get posterior variance and prior degrees of freedom based on variance distribution of all proteins
update t-statistic, and degrees of freedom
recompute p.values

sv <- squeezeVar(sigma^2, df=df.residual) #<< 
moderated.t <- tstat * sigma / sqrt(sv$var.post)
df.total <- df.residual + sv$df.prior   
p.value <- 2*pt( abs(moderated.t), df=df.total, lower.tail=FALSE)