enhanceFeatures {BayesSpace} | R Documentation

## Predict feature vectors from enhanced PCs.

### Description

Predict feature vectors from enhanced PCs.

### Usage

    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)
    )

### Arguments

`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.

### Details

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.

### Value

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.

### Examples

    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"))

[Package BayesSpace version 1.0.0 Index]