mi4limma  R Documentation 
This function performs hierarchical differential analysis using a moderated ttest statistic, which accounts for multiple imputation variability if applicable.
mi4limma(qData, sTab, VarRubin, comp.type = "OnevsOne", robust = FALSE)
qData 
A matrix of quantitative data, without any missing values. It
should be the averaged matrix from the array resulting from

sTab 
The experimental matrix, also corresponding to the pData function of MSnbase. 
VarRubin 
A numerical vector, resulting from 
comp.type 
A string that corresponds to the type of comparison. Values are: 'anova1way', 'OnevsOne' and 'OnevsAll'; default is 'OnevsOne'. 
robust 
logical, should the estimation of df.prior and var.prior be robustified against outlier sample variances? (as in limma's eBayes) 
A list of two dataframes : logFC and P_Value. The first one contains the logFC values of all the comparisons (one column for one comparison), the second one contains the pvalue of all the comparisons (one column for one comparison). The names of the columns for those two dataframes are identical and correspond to the description of the comparison.
Adapted by Marie Chion, from limmaCompleteTest
of the
DAPAR
package by Hélène Borges, Thomas Burger,
Quentin GiaiGianetto and Samuel Wieczorek.
M. Chion, Ch. Carapito and F. Bertrand (2021). Accounting for multiple imputationinduced variability for differential analysis in mass spectrometrybased labelfree quantitative proteomics. arxiv:2108.07086. https://arxiv.org/abs/2108.07086.
set.seed(2016)
data(qData)
data(sTab)
fit.limma < mi4limma(qData, sTab, diag(1,2))
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