Package: speckle 1.13.0

Belinda Phipson

speckle: Statistical methods for analysing single cell RNA-seq data

The speckle package contains functions for the analysis of single cell RNA-seq data. The speckle package currently contains functions to analyse differences in cell type proportions. There are also functions to estimate the parameters of the Beta distribution based on a given counts matrix, and a function to normalise a counts matrix to the median library size. There are plotting functions to visualise cell type proportions and the mean-variance relationship in cell type proportions and counts. As our research into specialised analyses of single cell data continues we anticipate that the package will be updated with new functions.

Authors:Belinda Phipson [aut, cre]

speckle_1.13.0.tar.gz
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speckle_1.13.0.tgz(r-4.6-any)speckle_1.13.0.tgz(r-4.5-any)
speckle_1.13.0.tar.gz(r-4.7-any)speckle_1.13.0.tar.gz(r-4.6-any)
speckle_1.13.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
speckle/json (API)
NEWS

# Install 'speckle' in R:
install.packages('speckle', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • pbmc_props - Cell type proportions from single cell PBMC data

On BioConductor:speckle-1.13.0(bioc 3.24)speckle-1.12.0(bioc 3.23)

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

singlecellrnaseqregressiongeneexpression

5.87 score 496 scripts 626 downloads 13 exports 159 dependencies

Last updated from:784ac53456. Checks:8 NOTE, 2 OK. Indexed: yes.

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bioc-checksNOTE221
linux-devel-x86_64NOTE412
source / vignettesOK345
linux-release-x86_64NOTE408
macos-release-arm64NOTE450
macos-oldrel-arm64NOTE198
windows-develNOTE295
windows-releaseNOTE364
windows-oldrelNOTE293
wasm-releaseOK193

Exports:convertDataToListestimateBetaParamestimateBetaParamsFromCountsgetTransformedPropsggplotColorsnormCountsplotCellTypeMeanVarplotCellTypePropsplotCellTypePropsMeanVarpropellerpropeller.anovapropeller.ttestspeckle_example_data

Dependencies:abindaskpassbase64encBHBiobaseBiocGenericsbitopsbslibcachemcaToolscliclustercodetoolscommonmarkcowplotcpp11crosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngedgeRevaluatefarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsGenomicRangesggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallifecyclelimmalistenvlmtestlocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemimeminiUInlmeopensslotelparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS4ArraysS4VectorsS7sassscalesscattermoresctransformSeqinfoSeuratSeuratObjectshinySingleCellExperimentsitmosourcetoolsspspamSparseArrayspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstatmodstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableXVectoryamlzoo

speckle: statistical methods for analysing single cell RNA-seq data

Belinda Phipson

Rendered fromspeckle.Rmdusingknitr::rmarkdownon May 30 2026.

Last update: 2022-12-06
Started: 2022-08-19