Package: BASiCS 2.17.0

Catalina Vallejos

BASiCS: Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Authors:Catalina Vallejos [aut, cre], Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb]

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BASiCS.pdf |BASiCS.html✨
BASiCS/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/catavallejos/basics/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • ChainRNA - Extract from the chain obtained for the Grun et al (2014) data: pool-and-split samples
  • ChainRNAReg - Extract from the chain obtained for the Grun et al
  • ChainSC - Extract from the chain obtained for the Grun et al (2014) data: single-cell samples
  • ChainSCReg - Extract from the chain obtained for the Grun et al

On BioConductor:BASiCS-2.17.0(bioc 3.20)BASiCS-2.16.0(bioc 3.19)

bioconductor-package

46 exports 1.64 score 134 dependencies 1 dependents 21 mentions

Last updated 2 months agofrom:61172a40b4

Exports:.reorder_paramsas.data.frameBASiCS_CalculateERCCBASiCS_CorrectOffsetBASiCS_DenoisedCountsBASiCS_DenoisedRatesBASiCS_DetectHVGBASiCS_DetectLVGBASiCS_DetectVGBASiCS_diagHistBASiCS_DiagHistBASiCS_diagPlotBASiCS_DiagPlotBASiCS_DivideAndConquerBASiCS_DrawBASiCS_effectiveSizeBASiCS_EffectiveSizeBASiCS_FilterBASiCS_LoadChainBASiCS_MCMCBASiCS_MockSCEBASiCS_PlotDEBASiCS_PlotOffsetBASiCS_PlotVGBASiCS_PriorParamBASiCS_showFitBASiCS_ShowFitBASiCS_SimBASiCS_TestDEBASiCS_VarianceDecompBASiCS_VarThresholdSearchHVGBASiCS_VarThresholdSearchLVGBASiCS_VarThresholdSearchVGdisplayChainBASiCSdisplaySummaryBASiCSformatmakeExampleBASiCS_DatanewBASiCS_ChainnewBASiCS_DataplotrowDatarowData<-showsubsetSummaryupdateObject

Dependencies:abindaskpassassertthatbackportsbase64encbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularblusterbslibcachemcheckmatecliclustercodacodetoolscolorspacecolourpickercommonmarkcowplotcpp11crayoncurlDelayedArrayDelayedMatrixStatsdigestdistributionaldqrngedgeRevaluatefansifarverfastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggExtraggplot2gluegridExtragtablehexbinhighrhtmltoolshtmlwidgetshttpuvhttrigraphIRangesirlbaisobandjquerylibjsonliteknitrlabelinglambda.rlaterlatticelifecyclelimmalocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemetapodmgcvmimeminiUImunsellnlmenumDerivopensslpillarpkgconfigplyrposteriorpromisesR6rappdirsRColorBrewerRcppRcppArmadilloRcppHNSWreshape2rlangrmarkdownrsvdS4ArraysS4VectorssassScaledMatrixscalesscranscuttleshinyshinyjsSingleCellExperimentsitmosnowsourcetoolsSparseArraysparseMatrixStatsstatmodstringistringrSummarizedExperimentsystensorAtibbletinytexUCSC.utilsutf8vctrsviridisviridisLitewithrxfunxtableXVectoryamlzlibbioc

Introduction to BASiCS

Catalina Vallejos, Nils Eling

Rendered fromBASiCS.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2021-08-11
Started: 2015-04-13

Readme and manuals

Help Manual

Help pageTopics
Generate balanced subsets for divide and conquer BASiCS.generateSubsets
Methods for subsetting BASiCS_Result and BASiCS_ResultsDE objects.[,BASiCS_Result,ANY,ANY,ANY-method [,BASiCS_ResultsDE,ANY,ANY,ANY-method [[,BASiCS_ResultsDE,ANY,ANY-method
Converting BASiCS results objects to data.framesas.data.frame,BASiCS_ResultDE-method as.data.frame,BASiCS_ResultsDE-method as.data.frame,BASiCS_ResultVG-method
Convert concentration in moles per microlitre to molecule countsBASiCS_CalculateERCC
The BASiCS_Chain classBASiCS_Chain BASiCS_Chain-class
'show' method for BASiCS_Chain objectsBASiCS_Chain-methods show,BASiCS_Chain-method updateObject,BASiCS_Chain-method
Remove global mean expression offsetBASiCS_CorrectOffset
Calculates denoised expression expression countsBASiCS_DenoisedCounts
Calculates denoised expression ratesBASiCS_DenoisedRates
Detection method for highly (HVG) and lowly (LVG) variable genesBASiCS_DetectHVG BASiCS_DetectHVGLVG BASiCS_DetectHVG_LVG BASiCS_DetectLVG BASiCS_DetectVG
Create diagnostic plots of MCMC parametersBASiCS_DiagHist BASiCS_diagHist
Create diagnostic plots of MCMC parametersBASiCS_DiagPlot BASiCS_diagPlot
Run divide and conquer MCMC with BASiCSBASiCS_DivideAndConquer
Generate a draw from the posterior of BASiCS using the generative model.BASiCS_Draw
Calculate effective sample size for BASiCS_Chain parametersBASiCS_EffectiveSize BASiCS_effectiveSize
Filter for input datasetsBASiCS_Filter
Loads pre-computed MCMC chains generated by the 'BASiCS_MCMC' functionBASiCS_LoadChain
BASiCS MCMC samplerBASiCS_MCMC
Create a mock SingleCellExperiment object.BASiCS_MockSCE
Produce plots assessing differential expression resultsBASiCS_PlotDE BASiCS_PlotDE,BASiCS_ResultDE-method BASiCS_PlotDE,BASiCS_ResultsDE-method BASiCS_PlotDE,missing-method
Visualise global offset in mean expression between two chains.BASiCS_PlotOffset
Plot variance decomposition results.BASiCS_PlotVarianceDecomp
Plots of HVG/LVG search.BASiCS_PlotVG
Prior parameters for BASiCS_MCMCBASiCS_PriorParam
The BASiCS_Result classBASiCS_Result BASiCS_Result-class
The BASiCS_ResultDE classBASiCS_ResultDE BASiCS_ResultDE-class
The BASiCS_ResultsDE classBASiCS_ResultsDE BASiCS_ResultsDE-class
The BASiCS_ResultVG classBASiCS_ResultVG BASiCS_ResultVG-class
Plotting the trend after Bayesian regressionBASiCS_ShowFit BASiCS_showFit
Generates synthetic data according to the model implemented in BASiCSBASiCS_Sim
The BASiCS_Summary classBASiCS_Summary BASiCS_Summary-class
'show' method for BASiCS_Summary objectsBASiCS_Summary-methods show,BASiCS_Summary-method
Detection of genes with changes in expressionBASiCS_TestDE
Decomposition of gene expression variability according to BASiCSBASiCS_VarianceDecomp
Detection method for highly and lowly variable genes using a grid of variance contribution thresholdsBASiCS_VarThresholdSearchHVG BASiCS_VarThresholdSearchHVG_LVG BASiCS_VarThresholdSearchLVG BASiCS_VarThresholdSearchVG
Defunct functions in package 'BASiCS'BASiCS-defunct BASiCS_D_TestDE
Extract from the chain obtained for the Grun et al (2014) data: pool-and-split samplesChainRNA
Extract from the chain obtained for the Grun et al (2014) data: pool-and-split samples (regression model)ChainRNAReg
Extract from the chain obtained for the Grun et al (2014) data: single-cell samplesChainSC
Extract from the chain obtained for the Grun et al (2014) data: single-cell samples (regression model)ChainSCReg
'dim' method for BASiCS_Chain objectsdim dim,BASiCS_Chain-method
'dimnames' method for BASiCS_Chain objectsdimnames dimnames,BASiCS_Chain-method
Accessors for the slots of a BASiCS_Chain objectdisplayChainBASiCS displayChainBASiCS,BASiCS_Chain-method displayChainBASiCS-BASiCS_Chain-method
Accessors for the slots of a 'BASiCS_Summary' objectdisplaySummaryBASiCS displaySummaryBASiCS,BASiCS_Summary-method displaySummaryBASiCS-BASiCS_Summary-method
Methods for formatting BASiCS_Result and BASiCS_ResultsDE objects.format,BASiCS_ResultDE-method format,BASiCS_ResultsDE-method format,BASiCS_ResultVG-method
Create a synthetic SingleCellExperiment example object with the format required for BASiCSmakeExampleBASiCS_Data
Creates a BASiCS_Chain object from pre-computed MCMC chainsnewBASiCS_Chain
Creates a SingleCellExperiment object from a matrix of expression counts and experimental information about spike-in genesnewBASiCS_Data
'plot' method for BASiCS_Chain objectsplot,BASiCS_Chain,ANY-method plot,BASiCS_Chain-method plot-BASiCS_Chain-method
'plot' method for BASiCS_Summary objectsplot,BASiCS_Summary,ANY-method plot,BASiCS_Summary-method, plot-BASiCS_Summary-method
rowData getter and setter for BASiCS_ResultsDE and BASiCS_ResultVG objects.rowData,BASiCS_ResultsDE-method rowData,BASiCS_ResultVG-method rowData<-,BASiCS_ResultsDE-method rowData<-,BASiCS_ResultVG-method
Accessors for the slots of a 'BASiCS_ResultDE' objectshow,BASiCS_ResultDE-method
Accessors for the slots of a 'BASiCS_ResultsDE' objectshow,BASiCS_ResultsDE-method
Accessors for the slots of a 'BASiCS_ResultVG' objectshow,BASiCS_ResultVG-method
A 'subset' method for `BASiCS_Chain`` objectssubset subset,BASiCS_Chain-method
'Summary' method for BASiCS_Chain objectsSummary Summary,BASiCS_Chain-method