BASiCS_DenoisedCounts: Calculates denoised expression expression counts

In catavallejos/BASiCS: Bayesian Analysis of Single-Cell Sequencing data

Calculates denoised expression expression counts

Description

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

Usage

BASiCS_DenoisedCounts(Data, Chain, WithSpikes = TRUE)

Arguments

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.

Details

See vignette browseVignettes("BASiCS")

Value

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

Author(s)

Catalina A. Vallejos [e-mail]

Nils Eling [e-mail]

See Also

BASiCS_Chain

Examples

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)

catavallejos/BASiCS documentation built on March 27, 2024, 12:49 a.m.