BASiCS_PriorParam {BASiCS} | R Documentation

## Prior parameters for BASiCS_MCMC

### Description

This is a convenience function to allow partial specification of prior parameters, and to ensure default parameters are consistent across usage within the package.

### Usage

    BASiCS_PriorParam(
      Data,
      k = 12,
      mu.mu = NULL,
      s2.mu = 0.5,
      s2.delta = 0.5,
      a.delta = 1,
      b.delta = 1,
      p.phi = rep(1, times = ncol(Data)),
      a.s = 1,
      b.s = 1,
      a.theta = 1,
      b.theta = 1,
      RBFMinMax = TRUE,
      FixLocations = !is.null(RBFLocations) | !is.na(MinGenesPerRBF),
      RBFLocations = NULL,
      MinGenesPerRBF = NA,
      variance = 1.2,
      m = numeric(k),
      V = diag(k),
      a.sigma2 = 2,
      b.sigma2 = 2,
      eta = 5,
      PriorMu = c("default", "EmpiricalBayes"),
      PriorDelta = c("log-normal", "gamma"),
      StochasticRef = TRUE,
      ConstrainProp = 0.2,
      GeneExponent = 1,
      CellExponent = 1
    )

### Arguments

`Data` | SingleCellExperiment object (required).
`k` | Number of regression terms, including k - 2 Gaussian radial basis functions (GRBFs).
`mu.mu, s2.mu` | Mean and variance parameters for lognormal prior on mu.
`s2.delta` | Variance parameter for lognormal prior on delta when `PriorDelta="lognormal"`.
`a.delta, b.delta` | Parameters for gamma prior on delta when `PriorDelta="gamma"`.
`p.phi` | Parameter for dirichlet prior on phi.
`a.s, b.s` | Parameters for gamma prior on s.
`a.theta, b.theta` | Parameters for gamma prior on theta.
`RBFMinMax` | Should GRBFs be placed at the minimum and maximum of `log(mu)`?
`FixLocations` | Should RBFLocations be fixed throughout MCMC, or adaptive during burn-in? By default this is `FALSE`, but it is set to `TRUE` if `RBFLocations` or `MinGenesPerRBF` are specified.
`RBFLocations` | Numeric vector specifying locations of GRBFs in units of `log(mu)`.
`MinGenesPerRBF` | Numeric scalar specifying the minimum number of genes for GRBFs to be retained. If fewer than `MinGenesPerRBF` genes have values of `log(mu)` within the range of an RBF, it is removed. The range covered by each RBF is defined as centre of the RBF plus or minus half the distance between RBFs.
`variance` | Variance of the GRBFs.
`m, V` | Mean and (co)variance priors for regression coefficients.
`a.sigma2, b.sigma2` | Priors for inverse gamma prior on regression scale.
`eta` | Degrees of freedom for t distribution of regresion errors.
`PriorMu` | Indicates if the original prior (`PriorMu = 'default'`) or an empirical Bayes approach (`PriorMu = 'EmpiricalBayes'`) will be assigned to gene-specific mean expression parameters.
`PriorDelta` | Scalar character specifying the prior type to use for delta overdispersion parameter. Options are "log-normal" (recommended) and "gamma" (used in Vallejos et al. (2015)).
`StochasticRef` | Logical scalar specifying whether the reference gene for the no-spikes version should be chosen randomly at MCMC iterations.
`ConstrainProp` | Proportion of genes to be considered as reference genes if `StochasticRef=TRUE`.
`GeneExponent, CellExponent` | Exponents for gene and cell-specific parameters. These should not be outside of divide and conquer MCMC applications.

### Value

A list containing the prior hyper-parameters that are required to run the algoritm implemented in `BASiCS_MCMC`.

### Examples

    BASiCS_PriorParam(makeExampleBASiCS_Data(), k = 12)

[Package BASiCS version 2.2.4 Index]