BASiCS_Summary {BASiCS} | R Documentation

## The BASiCS_Summary class

### Description

Container of a summary of a `BASiCS_Chain` object. In each element of the `parameters` slot, first column contains posterior medians; second and third columns respectively contain the lower and upper limits of an high posterior density interval (for a given probability).

### Slots

`parameters`

  List of parameters in which each entry contains a matrix: first column contains posterior medians, second column contains the lower limits of an high posterior density interval and third column contains the upper limits of high posterior density intervals.

  mu

    Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of gene-specific mean expression parameters μ_i.

  delta

    Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of gene-specific biological over-dispersion parameters δ_i, biological genes only

  phi

    Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of cell-specific mRNA content normalisation parameters φ_j

  s

    Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of cell-specific technical normalisation parameters s[j]

  nu

    Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of cell-specific random effects ν_j

  theta

    Posterior median (1st column), lower (2nd column) and upper (3rd column) limits of technical over-dispersion parameter(s) θ (each row represents one batch)

  `beta`

    Posterior median (first column), lower (second column) and upper (third column) limits of regression coefficients β

  `sigma2`

    Posterior median (first column), lower (second column) and upper (third column) limits of residual variance σ^2

  `epsilon`

    Posterior median (first column), lower (second column) and upper (third column) limits of gene-specific residual over-dispersion parameter ε

### Examples

    # A BASiCS_Summary object created by the Summary method.
    Data <- makeExampleBASiCS_Data()
    Chain <- BASiCS_MCMC(Data, N = 100, Thin = 2, Burn = 2, Regression = FALSE)
    ChainSummary <- Summary(Chain)

[Package BASiCS version 2.6.0 Index]