enhanceFeatures: Predict feature vectors from enhanced PCs.
In BayesSpace: Clustering and Resolution Enhancement of Spatial Transcriptomes

Description Usage Arguments Details Value Examples

Predict feature vectors from enhanced PCs.

enhanceFeatures(
  sce.enhanced,
  sce.ref,
  feature_names = NULL,
  model = c("xgboost", "dirichlet", "lm"),
  use.dimred = "PCA",
  assay.type = "logcounts",
  altExp.type = NULL,
  feature.matrix = NULL,
  nrounds = 0,
  train.n = round(ncol(sce.ref) * 2/3)
)

`sce.enhanced`	SingleCellExperiment object with enhanced PCs.
`sce.ref`	SingleCellExperiment object with original PCs and expression.
`feature_names`	List of genes/features to predict expression/values for.
`model`	Model used to predict enhanced values.
`use.dimred`	Name of dimension reduction to use.
`assay.type`	Expression matrix in `assays(sce.ref)` to predict.
`altExp.type`	Expression matrix in `altExps(sce.ref)` to predict. Overrides `assay.type` if specified.
`feature.matrix`	Expression/feature matrix to predict, if not directly attached to `sce.ref`. Must have columns corresponding to the spots in `sce.ref`. Overrides `assay.type` and `altExp.type` if specified.
`nrounds`	Nonnegative integer to set the `nrounds` parameter (max number of boosting iterations) for xgboost. `nrounds = 100` works reasonably well in most cases. If `nrounds` is set to 0, the parameter will be tuned using a train-test split. We recommend tuning `nrounds` for improved feature prediction, but note this will increase runtime.
`train.n`	Number of spots to use in the training dataset for tuning nrounds. By default, 2/3 the total number of spots are used.

Enhanced features are computed by fitting a predictive model to a low-dimensional representation of the original expression vectors. By default, a linear model is fit for each gene using the top 15 principal components from each spot, i.e. lm(gene ~ PCs), and the fitted model is used to predict the enhanced expression for each gene from the subspots' principal components.

Diagnostic measures, such as RMSE for xgboost or R.squared for linear regression, are added to the 'rowData' of the enhanced experiment if the features are an assay of the original experiment. Otherwise they are stored as an attribute of the returned matrix/altExp.

Note that feature matrices will be returned and are expected to be input as p \times n matrices of p-dimensional feature vectors over the n spots.

If assay.type or altExp.type are specified, the enhanced features are stored in the corresponding slot of sce.enhanced and the modified SingleCellExperiment object is returned.

If feature.matrix is specified, or if a subset of features are requested, the enhanced features are returned directly as a matrix.

set.seed(149)
sce <- exampleSCE()
sce <- spatialCluster(sce, 7, nrep=100, burn.in=10)
enhanced <- spatialEnhance(sce, 7, init=sce$spatial.cluster, nrep=100, burn.in=10)
enhanced <- enhanceFeatures(enhanced, sce, feature_names=c("gene_1", "gene_2"))