Multiple Imputation for Proteomics


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Documentation for package ‘mi4p’ version 1.1

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mi4p-package mi4p: Multiple imputation for proteomics
check.conditions Check if the design is valid
check.design Check if the design is valid
datasim A single simulated dataset
eBayes.mod MI-aware Modifed eBayes Function
formatLimmaResult Format a Result from Limma
hid.ebayes MI-aware Modifed eBayes Function
limmaCompleteTest.mod Computes a hierarchical differential analysis
make.contrast Builds the contrast matrix
make.design Builds the design matrix
make.design.1 Builds the design matrix for designs of level 1
make.design.2 Builds the design matrix for designs of level 2
make.design.3 Builds the design matrix for designs of level 3
meanImp_emmeans Multiple Imputation Estimate
mi4limma Differential analysis after multiple imputation
mi4p mi4p: Multiple imputation for proteomics
mm_peptides mm_peptides - peptide-level intensities for mouse
multi.impute Multiple imputation of quantitative proteomics datasets
MVgen Amputation of a dataset
norm.200.m100.sd1.vs.m200.sd1.list A list of simulated datasets.
proj_matrix Variance-Covariance Matrix Projection
protdatasim Data simulation function
qData Extract of the abundances of Exp1_R25_pept dataset
rubin1.all First Rubin rule (all peptides)
rubin1.one First Rubin rule (a given peptide)
rubin2.all Computes the 2nd Rubin's rule (all peptides)
rubin2bt.all 2nd Rubin's rule Between-Imputation component (all peptides)
rubin2bt.one 2nd Rubin's rule Between-Imputation Component (a given peptide)
rubin2wt.all 2nd Rubin's rule Within-Variance Component (all peptides)
rubin2wt.one 2nd Rubin's rule Within-Variance Component (a given peptide)
sTab Experimental design for the Exp1_R25_pept dataset
test.design Check if xxxxxx
within_variance_comp_emmeans Multiple Imputation Within Variance Component