Methods for label-free mass spectrometry proteomics imputation
Install from Github:
install.packages("devtools") # devtools is required to download and install the package devtools::install_github("DavisLaboratory/msImpute")
Install from Bioconductor:
if(!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("msImpute")
library(reticulate) library(msImpute) # xna is a numeric matrix with NAs (for MAR/MNAR diagnosis only) # "group" defines experimental condition (e.g. control, treatment etc). # select peptides missing in at least one experimental group selectFeatures(xna, method="ebm", group=group) # select peptides that can be informative for # exploring missing value patterns at high abundance selectFeatures(xna, method="hvp", n_features=500) # impute MAR data by low-rank models (v2 is enhanced version of v1 implementation) xcomplete <- msImpute(xna, method="v2") # impute MNAR data by low-rank models (adaptation of low-rank models for MNAR data) xcomplete <- msImpute(xna, method="v2-mnar", group=group) # Requires python. See Manual for more information. top.hvp <- findVariableFeatures(xna) computeStructuralMetrics(xcomplete, group, xna[rownames(top.hvp)[1:50],], k = 2)
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