Package: limpca 1.9.0

Manon Martin

limpca: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods

This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design.

Authors:Bernadette Govaerts [aut, ths], Sebastien Franceschini [ctb], Robin van Oirbeek [ctb], Michel Thiel [aut], Pascal de Tullio [dtc], Manon Martin [aut, cre], Nadia Benaiche [ctb]

limpca_1.9.0.tar.gz
limpca_1.9.0.zip(r-4.7)limpca_1.9.0.zip(r-4.6)limpca_1.9.0.zip(r-4.5)
limpca_1.9.0.tgz(r-4.6-any)limpca_1.9.0.tgz(r-4.5-any)
limpca_1.9.0.tar.gz(r-4.7-any)limpca_1.9.0.tar.gz(r-4.6-any)
limpca_1.9.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
limpca/json (API)

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

Bug tracker:https://github.com/manonmartin/limpca/issues

Pkgdown/docs site:https://manonmartin.github.io

Datasets:
  • trout - Trout: the Rainbow trouts transcriptomic dataset
  • UCH - UCH: the Urine Citrate-Hippurate metabolomic dataset

On BioConductor:limpca-1.9.0(bioc 3.24)limpca-1.8.0(bioc 3.23)

statisticalmethodprincipalcomponentregressionvisualizationexperimentaldesignmultiplecomparisongeneexpressionmetabolomics

5.43 score 2 stars 5 scripts 22 exports 125 dependencies

Last updated from:eb4fa56366. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE213
linux-devel-x86_64OK381
source / vignettesOK362
linux-release-x86_64OK355
macos-release-arm64OK306
macos-oldrel-arm64OK196
windows-develOK278
windows-releaseOK283
windows-oldrelOK276
wasm-releaseOK191

Exports:data2LmpDataListlmpBootstrapTestslmpContributionslmpEffectMatriceslmpEffectPlotlmpLoading1dPlotlmpLoading2dPlotlmpModelMatrixlmpPcaEffectslmpScorePlotlmpScoreScatterPlotMlmpScreePlotpcaBySvdpcaLoading1dPlotpcaLoading2dPlotpcaScorePlotpcaScreePlotplotDesignplotLineplotMeansplotScatterplotScatterM

Dependencies:abindaskpassbackportsbase64encBiobaseBiocGenericsbitbit64blobbroombslibcachemcallrcellrangerclicliprcodetoolsconflictedcpp11crayoncurldata.tableDBIdbplyrDelayedArraydigestdoParalleldplyrdtplyrevaluatefarverfastmapfontawesomeforcatsforeachfsgarglegenericsGenomicRangesggplot2ggrepelggscigluegoogledrivegooglesheets4gtablehavenhighrhmshtmltoolshttridsIRangesisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclelubridatemagrittrMatrixMatrixGenericsmatrixStatsmemoisemimemodelropensslotelpillarpkgconfigplyrprettyunitsprocessxprogresspspurrrR6raggrappdirsRColorBrewerRcppreadrreadxlrematchrematch2reprexreshape2rlangrmarkdownrstudioapirvestS4ArraysS4VectorsS7sassscalesselectrSeqinfoSparseArraystringistringrSummarizedExperimentsyssystemfontstextshapingtibbletidyrtidyselecttidyversetimechangetinytextzdbutf8uuidvctrsviridisLitevroomwithrxfunxml2XVectoryaml

Get started with limpca.
Introduction | About the package | Vignettes description | Installation and loading of the limpca package | Short application on the UCH dataset | Data object | Data visualisation | PCA | Model estimation and effect matrix decomposition | Effect matrix test of significance and importance measure | ASCA decomposition | sessionInfo
Rendered fromlimpca.Rmdusingknitr::rmarkdown

Last update: 2024-04-26
Started: 2022-12-06

Application of limpca on the Trout transcriptomic dataset.
Installation and loading of the limpca package | Data and model presentation | Data import and exploration | Data import and design visualization | Principal Component Analysis of row data | Log10 transformation of the data and new PCA | New PCA without the outliers | Mean agregation by aquarium and scaling | Exploration of aggregated data | Design | Example of lineplot of the responses for two observations | PCA aggregated data | Score plots | 1D Loading plots | 2D Loading plots | Scatterplot matrix of all 15 responses | GLM decomposition | Model matrix X generation | Computation of effect matrices and importances | Bootstrap test of effect significance | ASCA and APCA | ASCA | PCA decomposition of effect matrices | Contributions | Scores and loadings plots | Day effect | Treatment effect | Day:Treatment effect | Combined Day+Treatment+Day:Treatment effect | Residual matrix decomposition | Effect plots on scores | APCA | Univariate ANOVA | Parallel ANOVA modeling and FDR p-value corrections | FDR corrected p_values (q-values) | Plot ASCA loadings versus -log10(q-values) | sessionInfo | References
Rendered fromTrout.Rmdusingknitr::rmarkdown

Last update: 2024-04-26
Started: 2022-12-07

Application of limpca on the UCH metabolomics dataset.
Introduction | Installation and loading of the limpca package | Data import | Data exploration | Design | Outcomes visualization | plotLine function | plotScatter function | plotScatterM function | plotMeans function | PCA | Application of ASCA+ and APCA+ | Model estimation and effect matrix decomposition | Model formula | Model matrix generation | Model estimation and effect matrices decomposition | Effects importance | Bootstrap tests and quantification of effects importance | ASCA/APCA/ASCA-E decomposition | ASCA | Contributions | Scores and loadings Plots | Main effects | Interaction Hippurate:Time | Combination of effects Hippurate+Time+Hippurate:Time | Model residuals | Other graphs | Scores scatter plot | 2D Loadings | Effects plot | Main effects for Hippurate | APCA | Scores Plot | Loadings plot | ASCA-E | sessionInfo | References
Rendered fromUCH.Rmdusingknitr::rmarkdown

Last update: 2024-04-26
Started: 2022-09-20