BASiCS_DenoisedCounts {BASiCS} | R Documentation

## 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}/(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.

### Author(s)

Catalina A. Vallejos cnvallej@uc.cl

Nils Eling eling@ebi.ac.uk

### 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)

[Package BASiCS version 2.2.4 Index]