BASiCS_DenoisedCounts: Calculates denoised expression expression counts
In BASiCS: Bayesian Analysis of Single-Cell Sequencing data

Description Usage Arguments Details Value Author(s) See Also Examples

Calculates denoised expression counts by removing cell-specific technical variation. The latter includes global-scaling normalisation and therefore no further normalisation is required.

1	BASiCS_DenoisedCounts(Data, Chain, WithSpikes = TRUE)

`Data`	An object of class `SingleCellExperiment`
`Chain`	An object of class `BASiCS_Chain`
`WithSpikes`	A logical scalar specifying whether denoised spike-in genes should be generated as part of the output value. This only applies when the `BASiCS_Chain` object was generated with the setting `WithSpikes=TRUE`.

See vignette browseVignettes("BASiCS")

A matrix of denoised expression counts. In line with global scaling normalisation strategies, these are defined as X_{ij}/(φ_j ν_j) for biological genes and X_{ij}/(ν_j) for spike-in genes. For this calculation φ_j ν_j are estimated by their corresponding posterior medians. If spike-ins are not used, φ_j is set equal to 1.

Catalina A. Vallejos cnvallej@uc.cl

Nils Eling eling@ebi.ac.uk

BASiCS_Chain

Data <- makeExampleBASiCS_Data(WithSpikes = TRUE)
## The N and Burn parameters used here are optimised for speed
## and should not be used in regular use.
## For more useful parameters,
## see the vignette (\code{browseVignettes("BASiCS")})
Chain <- BASiCS_MCMC(Data, N = 1000, Thin = 10, Burn = 500,
                     Regression = FALSE, PrintProgress = FALSE)

DC <- BASiCS_DenoisedCounts(Data, Chain)