selectFeatures {scmap} | R Documentation

## Find the most informative features (genes/transcripts) for projection

### Description

This is a modification of the M3Drop method. Instead of fitting a Michaelis-Menten model to the log expression-dropout relation, we fit a linear model. Namely, the linear model is build on the log(expression) versus log(dropout) distribution. After fitting a linear model important features are selected as the top N residuals of the linear model.

### Usage

    selectFeatures(object, n_features = 500, suppress_plot = TRUE)

    selectFeatures.SingleCellExperiment(object, n_features, suppress_plot)

    ## S4 method for signature 'SingleCellExperiment'
    selectFeatures(object, n_features = 500,
      suppress_plot = TRUE)

### Arguments

`object` | an object of `SingleCellExperiment` class
`n_features` | number of the features to be selected
`suppress_plot` | boolean parameter, which defines whether to plot log(expression) versus log(dropout) distribution for all genes. Selected features are highlighted with the red colour.

### 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 <- selectFeatures(sce)

[Package scmap version 1.6.0 Index]