Package: APL 1.17.0

Clemens Kohl

APL: Association Plots

APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest.

Authors:Clemens Kohl [cre, aut], Elzbieta Gralinska [aut], Martin Vingron [aut]

APL_1.17.0.tar.gz
APL_1.17.0.zip(r-4.7)APL_1.17.0.zip(r-4.6)APL_1.17.0.zip(r-4.5)
APL_1.17.0.tgz(r-4.6-any)APL_1.17.0.tgz(r-4.5-any)
APL_1.17.0.tar.gz(r-4.7-any)APL_1.17.0.tar.gz(r-4.6-any)
APL_1.17.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
APL/json (API)
NEWS

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

Bug tracker:https://github.com/vingronlab/apl/issues

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

On BioConductor:APL-1.17.0(bioc 3.24)APL-1.16.0(bioc 3.23)

statisticalmethoddimensionreductionsinglecellsequencingrnaseqgeneexpression

6.26 score 17 stars 18 scripts 274 downloads 23 exports 111 dependencies

Last updated from:e6c22daf7b. Checks:1 NOTE, 9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE308
linux-devel-x86_64OK560
source / vignettesOK473
linux-release-x86_64OK491
macos-release-arm64OK267
macos-oldrel-arm64OK238
windows-develOK1601
windows-releaseOK1715
windows-oldrelOK1490
wasm-releaseOK285

Exports:%>%aplapl_coordsapl_scoreapl_topGOas.cacompas.listca_3Dplotca_biplotca_coordscacompcacomp_namescacomp_slotcheck_cacompnew_cacomppick_dimsplot_enrichmentrun_APLrunAPLshowshow.cacompsubset_dimsvar_rows

Dependencies:abindAnnotationDbiaskpassbase64encBiobaseBiocGenericsBiostringsbitbit64blobbslibcachemclicodetoolscpp11crayoncrosstalkcurldata.tableDBIDelayedArraydigestdotCall64dplyrevaluatefarverfastmapfontawesomefsfuturefuture.applygenericsGenomicRangesggplot2ggrepelglobalsglueGO.dbgraphgtablehighrhtmltoolshtmlwidgetshttrIRangesisobandjquerylibjsonliteKEGGRESTknitrlabelinglaterlatticelazyevallifecyclelistenvmagrittrMatrixMatrixGenericsmatrixStatsmemoisemimeopensslorg.Hs.eg.dborg.Mm.eg.dbotelparallellypillarpkgconfigplotlypngprogressrpromisespurrrR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownRSpectraRSQLiteS4ArraysS4VectorsS7sassscalesSeqinfoSeuratObjectSingleCellExperimentspspamSparseArraySparseMstringistringrSummarizedExperimentsystibbletidyrtidyselecttinytextopGOutf8vctrsviridisLitewithrxfunXVectoryaml

Analyzing data with APL

Elzbieta Gralinska, Clemens Kohl, Martin Vingron

Rendered fromAPL.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2024-11-02
Started: 2021-12-01

Readme and manuals

Help Manual

Help pageTopics
Association Plotapl
Calculate Association Plot coordinatesapl_coords
Plot Association Plot with ggplotapl_ggplot
Plot Association Plot with plotlyapl_plotly
Find rows most highly associated with a conditionapl_score
Run Gene overrepresentation analysis with topGOapl_topGO
Create cacomp object from Seurat/SingleCellExperiment containeras.cacomp as.cacomp,cacomp-method as.cacomp,list-method as.cacomp,Seurat-method as.cacomp,SingleCellExperiment-method
Convert cacomp object to list.as.list,cacomp-method
Plot of the first 3D CA projection of the data.ca_3Dplot ca_3Dplot,cacomp-method ca_3Dplot,Seurat-method ca_3Dplot,SingleCellExperiment-method
Plot of 2D CA projection of the data.ca_biplot ca_biplot,cacomp-method ca_biplot,Seurat-method ca_biplot,SingleCellExperiment-method
Calculate correspondence analysis row and column coordinates.ca_coords
Correspondance Analysiscacomp cacomp,dgCMatrix-method cacomp,matrix-method cacomp,Seurat-method cacomp,SingleCellExperiment-method
Prints slot names of cacomp objectcacomp_names
Access slots in a cacomp objectcacomp_slot
An S4 class that contains all elements needed for CA.cacomp-class new_cacomp
Calculate residuals for Correspondence analysiscalc_residuals
Check if cacomp object was correctly created.check_cacomp
Perform clipping of residualsclip_residuals
Compute Freeman-Tukey residualscomp_ft_residuals
Compute Negative-Binomial residualscomp_NB_residuals
Compute Standardized Residualscomp_std_residuals
Runs elbow methodelbow_method
Find most variable rowsinertia_rows
Helper function to check if object is empty.is.empty
Calculates permuted association plot coordinatespermutation_cutoff
Compute statistics to help choose the number of dimensionspick_dims pick_dims,cacomp-method pick_dims,Seurat-method pick_dims,SingleCellExperiment-method
Generates plot for results from apl_topGOplot_enrichment
Random direction association plot coordinatesrandom_direction_cutoff
Recompute missing values of cacomp object.recompute
removes 0-only rows and columns in a matrix.rm_zeros
Compute and plot Association PlotrunAPL runAPL,dgCMatrix-method runAPL,matrix-method runAPL,Seurat-method runAPL,SingleCellExperiment-method run_APL
Internal function for `cacomp`run_cacomp
Scree Plotscree_plot
Prints cacomp objectshow,cacomp-method show.cacomp
Subset dimensions of a caobjsubset_dims
Find most variable rowsvar_rows