Package: GMSimpute 0.0.1.0

GMSimpute: Generalized Mass Spectrum Missing Peaks Abundance Imputation

GMSimpute implements the Two-Step Lasso (TS-Lasso) and compound minimum to recover the abundance of missing peaks in mass spectrum analysis. TS-Lasso is a label-free imputation method that handles various types of missing peaks simultaneously. This package provides the procedure to generate missing peaks (or data) for simulation study, as well as a tool to estimate and visualize the proportion of missing at random.

Authors:Qian Li [aut, cre]

GMSimpute_0.0.1.0.tar.gz
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GMSimpute_0.0.1.0.tgz(r-4.6-any)GMSimpute_0.0.1.0.tgz(r-4.5-any)
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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
GMSimpute/json (API)

# Install 'GMSimpute' in R:

install.packages('GMSimpute', repos = c('https://qianli10000.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/qianli10000/gmsimpute/issues

Datasets:

replicates - Raw mass spectrum proteomics log abundance for 4 pairs of technical replicates.
tcga.bc - Raw mass spectrum metabolomics data for TCGA breast cancer study.
tcga.bc.full - A subset of mass spectrum metabolomics data for TCGA breast cancer study without missing peaks.

On CRAN:

3.30 score 40 scripts 249 downloads 4 exports 32 dependencies

Last updated from:c0f55aa72c. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	153
source / vignettes	OK	173
linux-release-x86_64	ERROR	143
macos-release-arm64	ERROR	87
macos-oldrel-arm64	ERROR	103
windows-devel	ERROR	114
windows-release	ERROR	95
windows-oldrel	ERROR	100
wasm-release	OK	100

Exports:GMS.Lasso MAR.est missing.sim TS.Lasso

Dependencies:cli codetools cpp11 farver foreach ggplot2 glmnet glue gtable isoband iterators labeling lattice lifecycle magrittr Matrix plyr R6 RColorBrewer Rcpp RcppEigen reshape2 rlang S7 scales shape stringi stringr survival vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generalized Mass Spectrum missing peaks imputation with Two-Step Lasso as default algorithm	GMS.Lasso
Generalized Two-Step Lasso for missing peaks imputation	GTS.Lasso
Missing At Random (MAR) proportion estimation based on technical replicates.	MAR.est
Missing peaks generating procedure for simulation study	missing.sim
Raw mass spectrum proteomics log abundance for 4 pairs of technical replicates.	replicates
Raw mass spectrum metabolomics data for TCGA breast cancer study.	tcga.bc
A subset of mass spectrum metabolomics data for TCGA breast cancer study without missing peaks.	tcga.bc.full
Two-Step Lasso for missing peaks imputation	TS.Lasso