Multiple Imputation for Proteomics
Check if the design is valid
Check if the design is valid
MI-aware Modifed eBayes Function
Format a Result from Limma
MI-aware Modifed eBayes Function
Computes a hierarchical differential analysis
Builds the contrast matrix
Builds the design matrix for designs of level 1
Builds the design matrix for designs of level 2
Builds the design matrix for designs of level 3
Builds the design matrix
Multiple Imputation Estimate
Differential analysis after multiple imputation
mi4p: Multiple Imputation for Proteomics
Multiple imputation of quantitative proteomics datasets
Amputation of a dataset
Variance-Covariance Matrix Projection
Data simulation function
First Rubin rule (all peptides)
First Rubin rule (a given peptide)
Computes the 2nd Rubin's rule (all peptides)
2nd Rubin's rule Between-Imputation component (all peptides)
2nd Rubin's rule Between-Imputation Component (a given peptide)
2nd Rubin's rule Within-Variance Component (all peptides)
2nd Rubin's rule Within-Variance Component (a given peptide)
Check if xxxxxx
Multiple Imputation Within Variance Component
A framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <doi:10.1371/journal.pcbi.1010420>. It is dedicated to dealing with multiple imputation for proteomics.
Useful links