sccoda.model.other_models.SimpleModel

class sccoda.model.other_models.SimpleModel(reference_cell_type, *args, **kwargs)

Simple Dirichlet-Multinomial model with normal priors. Structure equivalent to scCODA’s other models.

Methods

get_chains_after_burnin(samples, …[, is_nuts])	Application of burn-in after MCMC sampling.
get_y_hat(states_burnin, num_results, num_burnin)	Calculate posterior mode of cell counts (for analysis purposes) and add intermediate parameters that are no priors to MCMC results.
make_result(states_burnin, sample_stats, …)	Result object generating function.
sample_hmc([num_results, num_burnin, …])	Hamiltonian Monte Carlo (HMC) sampling in tensorflow 2.
sample_hmc_da([num_results, num_burnin, …])	HMC sampling with dual-averaging step size adaptation (Nesterov, 2009)
sample_nuts([num_results, num_burnin, …])	HMC with No-U-turn (NUTS) sampling.
sampling(num_results, num_burnin, kernel, …)	MCMC sampling process (tensorflow 2)