Package: msImpute 1.21.0

Soroor Hediyeh-zadeh

msImpute: Imputation of label-free mass spectrometry peptides

MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP).

Authors:Soroor Hediyeh-zadeh [aut, cre]

msImpute_1.21.0.tar.gz
msImpute_1.21.0.zip(r-4.7)msImpute_1.21.0.zip(r-4.6)msImpute_1.21.0.zip(r-4.5)
msImpute_1.21.0.tgz(r-4.6-any)msImpute_1.21.0.tgz(r-4.5-any)
msImpute_1.21.0.tar.gz(r-4.6-any)msImpute_1.21.0.tar.gz(r-4.5-any)
msImpute_1.21.0.tgz(r-4.5-emscripten)
msImpute.pdf |msImpute.html✨
msImpute/json (API)
NEWS

# Install 'msImpute' in R:

install.packages('msImpute', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/davislaboratory/msimpute/issues

Datasets:

pxd007959 - Processed peptide intensity matrix and experimental design table from PXD007959 study
pxd010943 - SWATH-MS Analysis of Gfi1-mutant bone marrow neutrophils
pxd014777 - Processed peptide intensity matrix from PXD014777 study

On BioConductor:msImpute-1.21.0(bioc 3.23)msImpute-1.20.0(bioc 3.22)

massspectrometry proteomics software imputation-algorithm label-free-proteomics low-rank-approximation

5.35 score 15 stars 15 scripts 356 downloads 11 exports 77 dependencies

Last updated from:1f62ae8432. Checks:8 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	202
linux-devel-x86_64	WARNING	346
source / vignettes	OK	306
linux-release-x86_64	WARNING	288
macos-release-arm64	WARNING	265
macos-oldrel-arm64	WARNING	152
windows-devel	WARNING	231
windows-release	WARNING	224
windows-oldrel	WARNING	250
wasm-release	OK	165

Exports:computeStructuralMetrics CPD evidenceToMatrix findVariableFeatures KNC KNN msImpute mspip plotCV2 scaleData selectFeatures

Dependencies:abind assorthead beachmat BH Biobase BiocGenerics BiocNeighbors BiocParallel BiocSingular bluster cli cluster codetools cpp11 data.table DelayedArray dplyr dqrng edgeR FNN formatR futile.logger futile.options generics GenomicRanges glue here igraph IRanges irlba jsonlite lambda.r lattice lifecycle limma locfit magrittr Matrix MatrixGenerics matrixStats metapod mvtnorm pdist pillar pkgconfig png purrr R6 rappdirs Rcpp RcppTOML reticulate rlang rprojroot rsvd S4Arrays S4Vectors ScaledMatrix scran scuttle Seqinfo SingleCellExperiment sitmo snow softImpute SparseArray statmod stringi stringr SummarizedExperiment tibble tidyr tidyselect utf8 vctrs withr XVector

msImpute: Imputation of peptide intensity by low-rank approximation

Soroor Hediyeh-zadeh, Andrew I. Webb, Melissa J. Davis

Rendered frommsImpute-vignette.Rmdusingknitr::rmarkdownon Apr 09 2026.

Last update: 2020-10-20
Started: 2020-08-23

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Metrics for the assessment of post-imputation structural preservation	computeStructuralMetrics
CPD	CPD
Creates intensity matrix from tabular data in evidence table of MaxQuant	evidenceToMatrix
Find highly variable peptides	findVariableFeatures
k-nearest class means (KNC)	KNC
k-nearest neighbour (KNN)	KNN
Imputation of peptide log-intensity in mass spectrometry label-free proteomics by low-rank approximation	msImpute
Fills missing values by Peptide Identity Propagation (PIP)	mspip
Plot mean-CV^2 trend	plotCV2
Processed peptide intensity matrix and experimental design table from PXD007959 study	pxd007959
SWATH-MS Analysis of Gfi1-mutant bone marrow neutrophils	pxd010943
Processed peptide intensity matrix from PXD014777 study	pxd014777
Standardize a matrix to have optionally row means zero and variances one, and/or column means zero and variances one.	scaleData
Select features for MAR/MNAR pattern examination	selectFeatures