dsb

Documentation

[{"label":"dsb.html","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/refman/dsb.html"},{"label":"dsb.pdf","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/dsb.pdf"},{"label":"Additional Topics - qualtile.clipping - scale.factor - Python and Bioc - multiplexing - multi batch - FAQ","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/additional_topics.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/additional_topics.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/additional_topics.R"},{"label":"End-to-end CITE-seq analysis workflow using dsb for ADT normalization and Seurat for multimodal clustering","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/end_to_end_workflow.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/end_to_end_workflow.rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/end_to_end_workflow.R"},{"label":"Fast normalization for large datasets with or without empty drops","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/fastkm.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/fastkm.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/fastkm.R"},{"label":"Normalizing ADTs for datasets without empty droplets with the dsb function ModelNegativeADTnorm","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/no_empty_drops.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/no_empty_drops.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/no_empty_drops.R"},{"label":"Understanding how the dsb method works","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/understanding_dsb.html"},{"label":"source","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/understanding_dsb.Rmd"},{"label":"R code","section":"","type":"","url":"https://cran.r-project.org/web/packages/dsb/vignettes/understanding_dsb.R"}]

Reference manual: dsb.html , dsb.pdf Vignettes: Additional Topics - qualtile.clipping - scale.factor - Python and Bioc - multiplexing - multi batch - FAQ ( source , R code ) End-to-end CITE-seq analysis workflow using dsb for ADT normalization and Seurat for multimodal clustering ( source , R code ) Fast normalization for large datasets with or without empty drops ( source , R code ) Normalizing ADTs for datasets without empty droplets with the dsb function ModelNegativeADTnorm ( source , R code ) Understanding how the dsb method works ( source , R code )

Downloads

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Package source: dsb_2.0.1.tar.gz Windows binaries: r-devel: dsb_2.0.1.zip , r-release: dsb_2.0.1.zip , r-oldrel: dsb_2.0.1.zip macOS binaries: r-release (arm64): dsb_2.0.1.tgz , r-oldrel (arm64): dsb_2.0.1.tgz , r-release (x86_64): dsb_2.0.1.tgz , r-oldrel (x86_64): dsb_2.0.1.tgz Old sources: dsb archive

Linking

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