indexCluster {scmap} | R Documentation

## Create a precomputed Reference

### Description

Calculates centroids of each cell type and merge them into a single table.

### Usage

    indexCluster(object = NULL, cluster_col = "cell_type1")

    indexCluster.SingleCellExperiment(object, cluster_col)

    ## S4 method for signature 'SingleCellExperiment'
    indexCluster(object = NULL,
      cluster_col = "cell_type1")

### Arguments

`object` | SingleCellExperiment object
`cluster_col` | column name in the 'colData' slot of the SingleCellExperiment object containing the cell classification information

### Value

a 'data.frame' containing calculated centroids of the cell types of the Reference dataset

### 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)$feature_symbol <- rownames(sce)
    isSpike(sce, 'ERCC') <- grepl('^ERCC-', rownames(sce))
    # remove features with duplicated names
    sce <- sce[!duplicated(rownames(sce)), ]
    sce <- selectFeatures(sce)
    sce <- indexCluster(sce[rowData(sce)$scmap_features, ])

[Package scmap version 1.2.0 Index]