setFeatures {scmap} | R Documentation

## Set the most important features (genes/transcripts) for projection

### Description

This method manually sets the features to be used for projection.

### Usage

    setFeatures(object, features = NULL)

    setFeatures.SingleCellExperiment(object, features)

    ## S4 method for signature 'SingleCellExperiment'
    setFeatures(object, features = NULL)

### Arguments

`object` | an object of `SingleCellExperiment` class
`features` | a character vector of feature names

### Details

Please note that `feature_symbol` column of `rowData(object)` must be present in the input object and should not contain any duplicated feature names. This column defines feature names used during projection. Feature symbols in the reference dataset must correpond to the feature symbols in the projection dataset, otherwise the mapping will not work!

### Value

an object of `SingleCellExperiment` class with a new column in `rowData(object)` slot which is called `scmap_features`. It can be accessed by using `as.data.frame(rowData(object))$scmap_features`.

### Examples

    library(SingleCellExperiment)
    sce <- SingleCellExperiment(assays = list(normcounts = as.matrix(yan)), colData = ann)
    # this is needed to calculate dropout rate for feature selection
    # important: normcounts have the same zeros as raw counts (fpkm)
    counts(sce) <- normcounts(sce)
    logcounts(sce) <- log2(normcounts(sce) + 1)
    # use gene names as feature symbols
    rowData(sce)$feature_symbol <- rownames(sce)
    isSpike(sce, 'ERCC') <- grepl('^ERCC-', rownames(sce))
    # remove features with duplicated names
    sce <- sce[!duplicated(rownames(sce)), ]
    sce <- setFeatures(sce, c('MMP2', 'ZHX3'))

[Package scmap version 1.2.0 Index]