BASiCS_PriorParam {BASiCS} | R Documentation |
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
|
a.delta, b.delta |
Parameters for gamma prior on delta when
|
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
|
FixLocations |
Should RBFLocations be fixed throughout MCMC, or adaptive
during burn-in? By default this is |
RBFLocations |
Numeric vector specifying locations of GRBFs in units
of |
MinGenesPerRBF |
Numeric scalar specifying the minimum number of genes
for GRBFs to be retained. If fewer than |
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 ( |
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 |
GeneExponent, CellExponent |
Exponents for gene and cell-specific parameters. These should not be outside of divide and conquer MCMC applications. |
A list containing the prior hyper-parameters that are required to
run the algoritm implemented in BASiCS_MCMC
.
BASiCS_PriorParam(makeExampleBASiCS_Data(), k = 12)