Package: speckle 1.7.0

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]

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speckle.pdf |speckle.html
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NEWS

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

Peer review:

Datasets:
  • pbmc_props - Cell type proportions from single cell PBMC data

On BioConductor:speckle-1.7.0(bioc 3.21)speckle-1.6.0(bioc 3.20)

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

singlecellrnaseqregressiongeneexpression

5.38 score 240 scripts 344 downloads 13 exports 166 dependencies

Last updated 1 months agofrom:204269ec38. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 30 2024
R-4.5-winNOTENov 30 2024
R-4.5-linuxNOTENov 30 2024
R-4.4-winNOTENov 30 2024
R-4.4-macNOTENov 30 2024
R-4.3-winNOTENov 30 2024
R-4.3-macNOTENov 30 2024

Exports:convertDataToListestimateBetaParamestimateBetaParamsFromCountsgetTransformedPropsggplotColorsnormCountsplotCellTypeMeanVarplotCellTypePropsplotCellTypePropsMeanVarpropellerpropeller.anovapropeller.ttestspeckle_example_data

Dependencies:abindaskpassbase64encBHBiobaseBiocGenericsbitopsbslibcachemcaToolscliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngedgeRevaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleidenlifecyclelimmalistenvlmtestlocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS4ArraysS4VectorssassscalesscattermoresctransformSeuratSeuratObjectshinySingleCellExperimentsitmosourcetoolsspspamSparseArrayspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstatmodstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexUCSC.utilsutf8uwotvctrsviridisLitewithrxfunxtableXVectoryamlzlibbioczoo

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

Rendered fromspeckle.Rmdusingknitr::rmarkdownon Nov 30 2024.

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