Package: DepecheR 1.23.0

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]

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DepecheR.pdf |DepecheR.html✨
DepecheR/json (API)
NEWS

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

Peer review:

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-1.21.4(bioc 3.20)DepecheR-1.20.0(bioc 3.19)

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

softwarecellbasedassaystranscriptiondifferentialexpressiondatarepresentationimmunooncologytranscriptomicsclassificationclusteringdimensionreductionfeatureextractionflowcytometryrnaseqsinglecellvisualization

5.13 score 15 scripts 382 downloads 13 exports 102 dependencies

Last updated 25 days agofrom:68cbb01279. Checks:OK: 1 NOTE: 7 ERROR: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-win-x86_64NOTEOct 30 2024
R-4.5-linux-x86_64NOTEOct 30 2024
R-4.4-win-x86_64NOTEOct 30 2024
R-4.4-mac-x86_64NOTEOct 30 2024
R-4.4-mac-aarch64NOTEOct 30 2024
R-4.3-win-x86_64ERROROct 30 2024
R-4.3-mac-x86_64NOTEOct 30 2024
R-4.3-mac-aarch64NOTEOct 30 2024

Exports:dColorPlotdColorVectordContoursdDensityPlotdepechedResidualPlotdScaledSplsdadViolinsdWilcoxgroupProbPlotneighSmoothnUniqueNeighDons

Dependencies:base64encbeanplotBHBiocParallelbitopsbslibcachemcaToolscliClusterRcodetoolscollapsecolorspacecorpcorcpp11DEoptimRdigestdoSNOWdplyrellipseevaluatefansifarverfastmapFNNfontawesomeforeachformatRfsfutile.loggerfutile.optionsgdatagenericsggplot2ggrepelgluegmodelsgmpgplotsgridExtragsignalgtablegtoolshighrhtmltoolshtmlwidgetsigraphisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglambda.rlatticelifecyclemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemixOmicsmomentsmunsellnlmepillarpkgconfigplyrpracmapurrrR6rappdirsrARPACKRColorBrewerRcppRcppArmadilloRcppEigenreshape2rglrlangrmarkdownrobustbaseRSpectrasassscalessnowstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisviridisLitewithrxfunyaml

Example of a cytometry data analysis with DepecheR

Jakob Theorell

Rendered fromDepecheR_test.Rmdusingknitr::rmarkdownon Oct 30 2024.

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

Probability plot usage

Jakob Theorell

Rendered fromGroupProbPlot_usage.Rmdusingknitr::rmarkdownon Oct 30 2024.

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