Spatial clustering

Cluster a spatial expression dataset.

spatialCluster(
  sce,
  q,
  use.dimred = "PCA",
  d = 15,
  platform = c("Visium", "ST"),
  init = NULL,
  init.method = c("mclust", "kmeans"),
  model = c("t", "normal"),
  precision = c("equal", "variable"),
  nrep = 50000,
  burn.in = 1000,
  gamma = NULL,
  mu0 = NULL,
  lambda0 = NULL,
  alpha = 1,
  beta = 0.01,
  save.chain = FALSE,
  chain.fname = NULL
)

Arguments

sce	A SingleCellExperiment object containing the spatial data.
q	The number of clusters.
use.dimred	Name of a reduced dimensionality result in reducedDims(sce). If provided, cluster on these features directly.
d	Number of top principal components to use when clustering.
platform	Spatial transcriptomic platform. Specify 'Visium' for hex lattice geometry or 'ST' for square lattice geometry. Specifying this parameter is optional when analyzing SingleCellExperiments processed using readVisium or spatialPreprocess, as this information is included in their metadata.
init	Initial cluster assignments for spots.
init.method	If init is not provided, cluster the top d PCs with this method to obtain initial cluster assignments.
model	Error model. ('normal' or 't')
precision	Covariance structure. ('equal' or 'variable' for EEE and VVV covariance models, respectively.)
nrep	The number of MCMC iterations.
burn.in	The number of MCMC iterations to exclude as burn-in period.
gamma	Smoothing parameter. Defaults to 2 for platform="ST" and 3 for platform="Visium". (Values in range of 1-3 seem to work well.)
mu0	Prior mean hyperparameter for mu. If not provided, mu0 is set to the mean of PCs over all spots.
lambda0	Prior precision hyperparam for mu. If not provided, lambda0 is set to a diagonal matrix \(0.01 I\).
alpha	Hyperparameter for Wishart distributed precision lambda.
beta	Hyperparameter for Wishart distributed precision lambda.
save.chain	If true, save the MCMC chain to an HDF5 file.
chain.fname	File path for saved chain. Tempfile used if not provided.

Value

Returns a modified sce with cluster assignments stored in colData under the name spatial.cluster.

Details

The input SCE must have row and col columns in its colData, corresponding to the array row and column coordinates of each spot. These are automatically parsed by readVisium or can be added manually when creating the SCE.

Cluster labels are stored in the spatial.cluster column of the SCE, and the cluster initialization is stored in cluster.init.

See also

spatialPreprocess for preparing the SCE for clustering, spatialEnhance for enhancing the clustering resolution, clusterPlot for visualizing the cluster assignments, featurePlot for visualizing expression levels in spatial context, and mcmcChain for examining the full MCMC chain associated with the clustering.

Examples

set.seed(149)
sce <- exampleSCE()
sce <- spatialCluster(sce, 7, nrep=100, burn.in=10)
#> Neighbors were identified for 96 out of 96 spots.
#> Fitting model...
#> Calculating labels using iterations 10 through 100.