bayesynergy

The function bayesynergy is the main function of the bayesynergy package. It will fit a Bayesian semi-parametric model for in-vitro drug combination experiments to estimate synergistic and antagonistic effects.

Usage

bayesynergy(
  y,
  x,
  type = 3,
  drug_names = NULL,
  experiment_ID = NULL,
  units = NULL,
  lower_asymptotes = T,
  heteroscedastic = T,
  bayes_factor = F,
  robust = F,
  rho = 0.9,
  pcprior = F,
  pcprior_hypers = c(1, 0.1, 1, 0.2),
  lambda = 0.005,
  nu = 1.5,
  method = "sampling",
  control = list(),
  ...
)

Arguments

y

vector or matrix of viability measures. Replicates can be given in long or wide format.

x

two-column matrix of drug concentrations.

type

integer; the type of model used. Must be one of the following: 1 (Splines), 2 (GP with squared exponential kernel), 3 (GP with Matérn kernel) or 4 (GP with rational quadratic kernel).

drug_names

vector of size 2; names of the drugs utilized for the experiment.

experiment_ID

character; identifier of experiment, typically name of cell Line.

units

vector of size 2; concentration units for the drugs, e.g. c("\(\mu\)M","\(\mu\)M")

lower_asymptotes

logical; if TRUE the model will estimate the lower asymptotes of monotherapy curves.

heteroscedastic

logical; if TRUE, the model will assume heteroscedastic measurement error.

bayes_factor

logical; if TRUE, the Bayes factor is computed between the full model, and a model containing only the non-interaction surface.

robust

logical: if TRUE, the model assumes a log-Pareto-tailed Normal (LPTN) distribution

rho

numeric: the rho hyperparameter for the robust likelihood distribution

pcprior

logical: if TRUE, the model uses a penalized complexity (PC) prior on the kernel hyperparameters of the Matérn kernel (type=3)

pcprior_hypers

vector of size 4 giving hyperparameters for the PC prior on Matérn hyperparameters

lambda

numeric; the parameter controls the residual noise observed in the heteroscedastic model when f = 0.

nu

numeric; the nu parameter for the Matérn kernel. Must be one of (0.5, 1.5, 2.5)

method

The method of estimation. Must be one of `sampling`,`vb` corresponding to full sampling, or variational Bayes.

control

list; passed on to the stan sampler, e.g. for setting adapt_delta.

...

Arguments passed to rstan::sampling or rstan::vb (e.g. iter, chains).

Value

An object of S3 class "bayesynergy", which is a list with the following entries

stanfit	An object of class stanmodel, returned from the sampler.
posterior_mean	A list containing the posterior means of model parameters.
data	A list containing the original data used to fit the model.
model	A list containing model specification for the model fit.
returnCode	numeric; non-zero values indicate model was fit with errors or warnings
LPML	numeric; The log pseudo-marginal likelihood of the fitted model.
divergent	numeric; The number of divergent transitions from the Hamiltonian sampler.
messages	character; any warnings are collected here for inspection.
bayesfactor	numeric; The Bayes factor comparing the full model to a simpler model containing only the non-interaction assumption, p0. A large number indicating that an interaction effect is present. See the package vignette for more information.

Examples

if (FALSE) {
library(bayesynergy)
data("mathews_DLBCL")
y_mat <- mathews_DLBCL$`ispinesib + ibrutinib`[[1]]
x_mat <- mathews_DLBCL$`ispinesib + ibrutinib`[[2]]
fit <- bayesynergy(y_mat,x_mat)
}