spatialPreprocess {BayesSpace} | R Documentation

## Preprocess a spatial dataset for BayesSpace

### Description

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

### Usage

    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)

[Package BayesSpace version 1.2.1 Index]