BASiCS_PriorParam: Prior parameters for BASiCS_MCMC
In BASiCS: Bayesian Analysis of Single-Cell Sequencing data

Description Usage Arguments Value Examples

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

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
)

`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.