fit_spruce: Fit spruce Bayesian spatial mixture model
In carter-allen/spruce: Spatial Random Effects Clustering of Single Cell Data

fit_spruce

R Documentation

Fit spruce Bayesian spatial mixture model

Description

This function allows you to detect sub-populations single-sample spatial transcriptomics experiments.

Usage

fit_spruce(
  seurat_obj,
  K,
  emb = "PCs",
  n_dim = 8,
  r = 3,
  MCAR = TRUE,
  CAR = TRUE,
  smooth = TRUE,
  nsim = 2000,
  burn = 1000,
  z_init = NULL
)

Arguments

`seurat_obj`	An integrated Seurat object
`K`	The number of sub-populations to infer. Each should be present in each sample.
`emb`	Either one of "PCs", "HVGs", or "SVGs" OR a matrix with custom embeddings. If the latter, rows should be sorted as in meta data of Seurat object.
`n_dim`	The number of dimensions to use if emb is specified as one of "PCs", "HVGs", or "SVGs". Ignored if emb is a matrix of custom embeddings.
`r`	Spatial smoothing parameter. Should be greater than 0 with larger values enforcing stronger prior spatial association.
`MCAR`	Logical. Include multivariate CAR random intercepts in gene expression model?
`CAR`	Logical. Include univariate CAR random intercepts in multinomial gene expression model?
`smooth`	Logical. Use manual spatial smoothing controled by r parameter?
`nsim`	Number of total MCMC iterations to conduct.
`burn`	Number of initial MCMC iterations to discard as burn in. The number of saved iterations is nsim-burn
`z_init`	Initialized cluster allocation vector to aid in MCMC convergence. If NULL z_init will be set using hierarchical clustering.