Preprocess a spatial dataset for BayesSpace

Adds metadata required for downstream analyses, and (optionally) performs PCA on log-normalized expression of top HVGs.

spatialPreprocess(
  sce,
  platform = c("Visium", "ST"),
  n.PCs = 15,
  n.HVGs = 2000,
  skip.PCA = FALSE,
  log.normalize = TRUE,
  assay.type = "logcounts",
  BSPARAM = ExactParam()
)

Arguments

sce	SingleCellExperiment to preprocess
platform	Spatial sequencing platform. Used to determine spot layout and neighborhood structure (Visium = hex, ST = square).
n.PCs	Number of principal components to compute. We suggest using the top 15 PCs in most cases.
n.HVGs	Number of highly variable genes to run PCA upon.
skip.PCA	Skip PCA (if dimensionality reduction was previously computed.)
log.normalize	Whether to log-normalize the input data with scater. May be omitted if log-normalization previously computed.
assay.type	Name of assay in sce containing normalized counts. Leave as "logcounts" unless you explicitly pre-computed a different normalization and added it to sce under another assay. Note that we do not recommend running BayesSpace on PCs computed from raw counts.
BSPARAM	A BiocSingularParam object specifying which algorithm should be used to perform the PCA. By default, an exact PCA is performed, as current spatial datasets are generally small (<10,000 spots). To perform a faster approximate PCA, please specify FastAutoParam() and set a random seed to ensure reproducibility.

Value

SingleCellExperiment with PCA and BayesSpace metadata

Examples

sce <- exampleSCE()
sce <- spatialPreprocess(sce)