Package: DepecheR 1.29.0

Jakob Theorell

DepecheR: Determination of essential phenotypic elements of clusters in high-dimensional entities

The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.

Authors:Jakob Theorell [aut, cre], Axel Theorell [aut]

DepecheR_1.29.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
DepecheR/json (API)

# Install 'DepecheR' in R:
install.packages('DepecheR', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • testData - A 14 color flow cytometry dataset for example execution and playing around
  • testDataDepeche - A depeche clustering of the testData set
  • testDataSNE - SNE of the testData set

On BioConductor:DepecheR-1.29.0(bioc 3.24)DepecheR-1.28.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

softwarecellbasedassaystranscriptiondifferentialexpressiondatarepresentationimmunooncologytranscriptomicsclassificationclusteringdimensionreductionfeatureextractionflowcytometryrnaseqsinglecellvisualizationcpp

4.96 score 23 scripts 2 mentions 13 exports 96 dependencies

Last updated from:0dce259f63. Checks:11 NOTE, 2 OK, 1 ERROR. Indexed: yes.

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linux-devel-x86_64NOTE1097
source / vignettesOK310
linux-release-arm64NOTE281
linux-release-x86_64NOTE327
macos-release-arm64NOTE229
macos-release-x86_64ERROR485
macos-oldrel-arm64NOTE197
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windows-develNOTE237
windows-releaseNOTE289
windows-oldrelNOTE240
wasm-releaseOK222

Exports:dColorPlotdColorVectordContoursdDensityPlotdepechedResidualPlotdScaledSplsdadViolinsdWilcoxgroupProbPlotneighSmoothnUniqueNeighDons

Dependencies:base64encbeanplotBHBiocParallelbitopsbslibcachemcaToolscliClusterRcodetoolscollapsecorpcorcpp11DEoptimRdigestdoSNOWdplyrellipseevaluatefarverfastmapFNNfontawesomeforeachformatRfsfutile.loggerfutile.optionsgdatagenericsggplot2ggrepelgluegmodelsgmpgplotsgridExtragtablegtoolshighrhtmltoolshtmlwidgetsigraphisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglambda.rlatticelifecyclemagrittrMASSMatrixmatrixStatsmemoisemimemixOmicsmomentspillarpkgconfigplyrpurrrR6rappdirsrARPACKRColorBrewerRcppRcppArmadilloRcppEigenreshape2rglrlangrmarkdownrobustbaseRSpectraS7sassscalessnowstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisviridisLitewithrxfunyaml

Example of a cytometry data analysis with DepecheR
Introduction | Installation | Example data description | depeche clustering | depeche function output graphs | Adjusted Rand Index as a function of penalty values | Cluster centers | tSNE/umap generation | Visualization of depeche clusters on 2D representation | Visualization of markers on tSNE | Density distribution of groups | Separating groups from each other | Visualization of defining markers for clusters. | Summary | Session information
Rendered fromDepecheR_test.Rmdusingknitr::rmarkdown

Last update: 2020-06-05
Started: 2019-01-09

Probability plot usage
Introduction | Installation | Preparations of example data | Group probability plotting | Session information
Rendered fromGroupProbPlot_usage.Rmdusingknitr::rmarkdown

Last update: 2019-12-05
Started: 2019-12-05