Package: omicsGMF 1.3.0

Alexandre Segers

omicsGMF: Dimensionality reduction of (single-cell) omics data in R using omicsGMF

omicsGMF is a Bioconductor package that uses the sgdGMF-framework of the \code{sgdGMF} package for highly performant and fast matrix factorization that can be used for dimensionality reduction, visualization and imputation of omics data. It considers data from the general exponential family as input, and therefore suits the use of both RNA-seq (Poisson or Negative Binomial data) and proteomics data (Gaussian data). It does not require prior transformation of counts to the log-scale, because it rather optimizes the deviances from the data family specified. Also, it allows to correct for known sample-level and feature-level covariates, therefore enabling visualization and dimensionality reduction upon batch correction. Last but not least, it deals with missing values, and allows to impute these after matrix factorization, useful for proteomics data. This Bioconductor package allows input of SummarizedExperiment, SingleCellExperiment, and QFeature classes.

Authors:Alexandre Segers [aut, cre, fnd], Cristian Castiglione [ctb], Christophe Vanderaa [ctb], Davide Risso [ctb, fnd], Lieven Clement [ctb, fnd]

omicsGMF_1.3.0.tar.gz
omicsGMF_1.3.0.zip(r-4.7)omicsGMF_1.3.0.zip(r-4.6)omicsGMF_1.3.0.zip(r-4.5)
omicsGMF_1.3.0.tgz(r-4.6-any)omicsGMF_1.3.0.tgz(r-4.5-any)
omicsGMF_1.3.0.tar.gz(r-4.7-any)omicsGMF_1.3.0.tar.gz(r-4.6-any)
omicsGMF_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
omicsGMF/json (API)
NEWS

# Install 'omicsGMF' in R:
install.packages('omicsGMF', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/statomics/omicsgmf/issues

On BioConductor:omicsGMF-1.3.0(bioc 3.24)omicsGMF-1.2.0(bioc 3.23)

singlecellrnaseqproteomicsqualitycontrolpreprocessingnormalizationvisualizationdimensionreductiontranscriptomicsgeneexpressionsequencingsoftwaredatarepresentationmassspectrometry

5.00 score 2 stars 5 scripts 204 downloads 12 exports 178 dependencies

Last updated from:6e1ebb5abe. Checks:1 NOTE, 7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE230
linux-devel-x86_64WARNING576
source / vignettesOK327
linux-release-x86_64WARNING557
macos-release-arm64WARNING390
macos-oldrel-arm64WARNING313
windows-develWARNING636
windows-releaseWARNING600
windows-oldrelWARNING739
wasm-releaseOK189

Exports:calculateCVGMFcalculateGMFcalculateRankGMFimputeGMFplot_cvplotCVplotGMFplotRankrunCVGMFrunGMFrunRankGMFscreeplot_rank

Dependencies:abindAnnotationFilteraskpassassortheadbackportsbase64encbeachmatbeeswarmBHBiobaseBiocBaseUtilsBiocGenericsBiocNeighborsBiocParallelBiocSingularbootbroombslibcachemCairocarcarDatacliclueclustercodetoolscolorspacecorrplotcowplotcpp11crosstalkcurldata.tableDelayedArrayDerivdigestdoBydoParalleldplyrdqrngevaluatefarverfastmapFNNfontawesomeforeachforecastformatRFormulafracdifffsfutile.loggerfutile.optionsgenericsGenomicRangesggbeeswarmggplot2ggpubrggrastrggrepelggsciggsignifgluegridExtragtablehighrhtmltoolshtmlwidgetshttrigraphIRangesirlbaisobanditeratorsjquerylibjsonliteknitrlabelinglambda.rlaterlatticelazyevallifecyclelme4lmtestmagrittrMASSMatrixMatrixGenericsMatrixModelsmatrixStatsmemoisemgcvmicrobenchmarkmimeminqamodelrMsCoreUtilsMultiAssayExperimentnlmenloptrnnetnumDerivopensslotelpbkrtestpheatmappillarpkgconfigplotlyplyrpngpolynompromisesProtGenericspurrrQFeaturesquantregR6raggrappdirsrbibutilsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppMLRcppProgressRdpackreformulasreshape2rlangrmarkdownRSpectrarstatixrsvdRtsneS4ArraysS4VectorsS7sassScaledMatrixscalesscaterscuttleSeqinfosgdGMFSingleCellExperimentsitmosnowSparseArraySparseMstringistringrSummarizedExperimentSuppDistssurvivalsyssystemfontstextshapingtibbletidyrtidyselecttimeDatetinytexurcautf8uwotvctrsviporviridisviridisLitewithrxfunXVectoryamlzoo

Proteomics vignette: dimensionality reduction and imputation with omicsGMF

Rendered fromProteomics-vignette.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-10-16
Started: 2025-02-28

RNA-seq vignette: dimensionality reduction with omicsGMF

Rendered fromRNASeq-vignette.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2025-10-16
Started: 2025-02-28

Readme and manuals

Help Manual

Help pageTopics
Perform a stochastic gradient descent generalized matrix factorization (sgdGMF) on cells, based on the expression or mass spectrometry data in a SingleCellExperiment, SummarizedExperiment or QFeatures object.calculateCVGMF calculateCVGMF,ANY-method calculateCVGMF,QFeatures-method calculateCVGMF,SingleCellExperiment-method calculateCVGMF,SummarizedExperiment-method runCVGMF runCVGMF,QFeatures-method runCVGMF,SingleCellExperiment-method runCVGMF,SummarizedExperiment-method
Perform a stochastic gradient descent generalized matrix factorization (sgdGMF) on cells or bulk samples, based on the expression or mass spectrometry data in a SingleCellExperiment, SummarizedExperiment or QFeatures object.calculateGMF calculateGMF,ANY-method calculateGMF,QFeatures-method calculateGMF,SingleCellExperiment-method calculateGMF,SummarizedExperiment-method runGMF runGMF,QFeatures-method runGMF,SingleCellExperiment-method runGMF,SummarizedExperiment-method
Perform an eigendecomposition for model selection based on a screeplot.calculateRankGMF calculateRankGMF,ANY-method calculateRankGMF,QFeatures-method calculateRankGMF,SingleCellExperiment-method calculateRankGMF,SummarizedExperiment-method runRankGMF runRankGMF,QFeatures-method runRankGMF,SingleCellExperiment-method runRankGMF,SummarizedExperiment-method
Impute missing values based on the results of runGMF.imputeGMF imputeGMF,ANY-method imputeGMF,QFeatures-method imputeGMF,SingleCellExperiment-method imputeGMF,SummarizedExperiment-method
Functions to create a scree plot for model selection.plotCV plot_cv plot_cv,ANY-method
Wrapper functions to create plots for specific types of reduced dimension results in a SingleCellExperiment object, similar as the 'scater' package.plotGMF
Functions to create a scree plot for model selection.plotRank screeplot_rank screeplot_rank,ANY-method