View source: R/spatialPreprocess.R
spatialPreprocess | R Documentation |
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()
)
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 |
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
|
SingleCellExperiment with PCA and BayesSpace metadata
sce <- exampleSCE()
sce <- spatialPreprocess(sce)
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