spatialCluster {BayesSpace} | R Documentation

## Spatial clustering

### Description

Cluster a spatial expression dataset.

### Usage

    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.

### 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`.

### Value

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

### 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)

[Package BayesSpace version 1.4.1 Index]