fit_msn_PG_smooth: Multivariate skew normal mixture model clustering - PG...

In carter-allen/spruce: Spatial Random Effects Clustering of Single Cell Data

Multivariate skew normal mixture model clustering - PG multinom regression Spatial smoothing

Description

Implement Gibbs sampling for MSN model with spatial smoothing prior. Includes fixed effects multinomial regression on cluster indicators using Polya-Gamma data augmentation.

Usage

fit_msn_PG_smooth(
  Y,
  W,
  coords_df,
  K,
  r = 3,
  nsim = 2000,
  burn = 1000,
  z_init = NULL,
  verbose = FALSE
)

Arguments

Y	An n x g matrix of gene expression values. n is the number of cell spots and g is the number of features.
W	An n x v matrix of covariates to predict cluster membership. Should include an intercept (i.e., first column is 1)
coords_df	An n x 2 data frame or matrix of 2d spot coordinates.
K	The number of mixture components to fit.
r	Empirical spatial smoothing
nsim	Number of total MCMC iterations to run.
burn	Number of MCMC iterations to discard as burn in. The number of saved samples is nsim - burn.
z_init	Optional initialized allocation vector. Initialized with hierarchical clustering if NULL.
verbose	Logical for printing cluster allocations at each iteration.

Value

a list of posterior samples

carter-allen/spruce documentation built on April 6, 2024, 8:34 p.m.