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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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-1.7.0(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

5.41 score 258 scripts 390 downloads 13 exports 165 dependencies

Last updated 4 months agofrom:204269ec38. Checks:1 OK, 6 NOTE, 1 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 29 2025
R-4.5-winNOTEJan 29 2025
R-4.5-macWARNINGJan 29 2025
R-4.5-linuxNOTEJan 29 2025
R-4.4-winNOTEJan 29 2025
R-4.4-macNOTEJan 29 2025
R-4.3-winNOTEJan 29 2025
R-4.3-macNOTEJan 29 2025

Exports:convertDataToListestimateBetaParamestimateBetaParamsFromCountsgetTransformedPropsggplotColorsnormCountsplotCellTypeMeanVarplotCellTypePropsplotCellTypePropsMeanVarpropellerpropeller.anovapropeller.ttestspeckle_example_data

Dependencies:abindaskpassbase64encBHBiobaseBiocGenericsbitopsbslibcachemcaToolscliclustercodetoolscolorspacecommonmarkcowplotcpp11crayoncrosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngedgeRevaluatefansifarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleidenbaselifecyclelimmalistenvlmtestlocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeminiUImunsellnlmeopensslparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS4ArraysS4VectorssassscalesscattermoresctransformSeuratSeuratObjectshinySingleCellExperimentsitmosourcetoolsspspamSparseArrayspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstatmodstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexUCSC.utilsutf8uwotvctrsviridisLitewithrxfunxtableXVectoryamlzoo

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

Rendered fromspeckle.Rmdusingknitr::rmarkdownon Jan 29 2025.

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