fit_maple: Fit Maple multi-sample Bayesian spatial mixture model

In maple: Bayesian Analysis of Multi-Sample Spatial Transcriptomics Experiments

Fit Maple multi-sample Bayesian spatial mixture model

Description

This function allows you to detect sub-populations and explain membership with relevant covariates in multi-sample spatial transcriptomics experiments.

Usage

fit_maple(
  seurat_obj,
  K,
  emb = "PCs",
  n_dim = 8,
  covars = NULL,
  MCAR = FALSE,
  CAR = FALSE,
  smooth = TRUE,
  r = 3,
  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	The cell spot embedding to use. Either one of "PCs", "scGNN", "harmony", "HVGs", or "SVGs".
n_dim	The number of dimensions to use.
covars	Column names of Seurat meta data to use as covariates. If none specified, will fit a global intercept and sample-indicator model for cell type membership probabilities.
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 controlled by r parameter?
r	Spatial smoothing parameter for if smooth == TRUE. Should be greater than 0 with larger values enforcing stronger prior spatial association.
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.

Value

A list of MCMC samples, including the MAP estimate of cluster indicators (z)

Examples

## Not run: 
brain1 <- LoadData("stxBrain", type = "anterior1")
brain2 <- LoadData("stxBrain", type = "anterior2")
brain1 <- SCTransform(brain1, assay = "Spatial", verbose = FALSE)
brain2 <- SCTransform(brain2, assay = "Spatial", verbose = FALSE)
brain <- merge(brain1,brain2)
DefaultAssay(brain) <- "SCT"
VariableFeatures(brain) <- c(VariableFeatures(brain1),VariableFeatures(brain2))
brain <- RunPCA(brain)
brain_fit_PCs <- fit_maple(brain,K = 6,emb = "PCs")

## End(Not run)

maple documentation built on March 18, 2022, 6:02 p.m.