scmapCell2Cluster {scmap} | R Documentation

## Approximate k-NN cell-type classification using scfinemap

### Description

Each cell in the query dataset is assigned a cell-type if the similarity between its nearest neighbour exceeds a threshold AND its w nearest neighbours have the same cell-type.

### Usage

    scmapCell2Cluster(scmapCell_results = NULL, cluster_list = NULL, w = 3,
      threshold = 0.5)

    scmapCell2Cluster.SingleCellExperiment(scmapCell_results, cluster_list, w,
      threshold)

    ## S4 method for signature 'list'
    scmapCell2Cluster(scmapCell_results = NULL,
      cluster_list = NULL, w = 3, threshold = 0.5)

### Arguments

`scmapCell_results` | the output of 'scmapCell()' with 'projection' as its input.
`cluster_list` | list of cell cluster labels correspondint to each index against which the 'projection' has been projected
`w` | an integer specifying the number of nearest neighbours to find
`threshold` | the threshold which the maximum similarity between the query and a reference cell must exceed for the cell-type to be assigned

### Value

The query dataset with the predicted labels attached to colData(query_dat)$cell_type1

### 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)$feature_symbol <- rownames(sce)
    isSpike(sce, 'ERCC') <- grepl('^ERCC-', rownames(sce))
    # remove features with duplicated names
    sce <- sce[!duplicated(rownames(sce)), ]
    sce <- selectFeatures(sce)
    sce <- indexCell(sce)
    scmapCell_results <- scmapCell(sce, list(metadata(sce)$scmap_cell_index))
    sce <- scmapCell2Cluster(scmapCell_results, cluster_list = list(colData(sce)$cell_type1))

[Package scmap version 1.8.0 Index]