Package: Dino 1.11.0

Jared Brown

Dino: Normalization of Single-Cell mRNA Sequencing Data

Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge.

Authors:Jared Brown [aut, cre], Christina Kendziorski [ctb]

Dino_1.11.0.tar.gz
Dino_1.11.0.zip(r-4.5)Dino_1.11.0.zip(r-4.4)Dino_1.11.0.zip(r-4.3)
Dino_1.11.0.tgz(r-4.4-any)Dino_1.11.0.tgz(r-4.3-any)
Dino_1.11.0.tar.gz(r-4.5-noble)Dino_1.11.0.tar.gz(r-4.4-noble)
Dino_1.11.0.tgz(r-4.4-emscripten)
Dino.pdf |Dino.html✨
Dino/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/jbrownbiostat/dino/issues

Datasets:
  • multimodalDat - Plot data from simulated expression
  • pbmcSmall - Subset of 500 peripheral blood mononuclear cells (PBMCs) from a healthy donor
  • unimodalDat - Plot data from simulated expression

On BioConductor:Dino-1.11.0(bioc 3.20)Dino-1.10.0(bioc 3.19)

bioconductor-package

3 exports 0.49 score 182 dependencies 9 mentions

Last updated 2 months agofrom:c9c0971bd4

Exports:DinoDino_SCESeuratFromDino

Dependencies:abindaskpassbase64encbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularbitopsblusterbslibcachemcaToolscliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDelayedArrayDelayedMatrixStatsdeldirdigestdotCall64dplyrdqrngedgeRevaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomeformatRfsfutile.loggerfutile.optionsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglambda.rlaterlatticelazyevalleidenlifecyclelimmalistenvlmtestlocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemetapodmgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectrarsvdRtsneS4ArraysS4VectorssassScaledMatrixscalesscattermorescransctransformscuttleSeuratSeuratObjectshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArraysparseMatrixStatsspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.utilsstatmodstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexUCSC.utilsutf8uwotvctrsviridisLitewithrxfunxtableXVectoryamlzlibbioczoo

Normalization by distributional resampling of high throughput single-cell RNA-sequencing data

Jared Brown and Christina Kendziorski

Rendered fromDino.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2022-05-31
Started: 2020-10-20