indexCell {scmap} | R Documentation

## Create an index for a dataset to enable fast approximate nearest neighbour search

### Description

The method is based on product quantization for the cosine distance. Split the training data into M identically sized chunks by genes. Use k-means to find k subcentroids for each group. Assign cluster numbers to each member of the dataset.

### Usage

    indexCell(object = NULL, M = NULL, k = NULL)

    indexCell.SingleCellExperiment(object, M, k)

    ## S4 method for signature 'SingleCellExperiment'
    indexCell(object = NULL, M = NULL,
      k = NULL)

### Arguments

`object` | an object of `SingleCellExperiment` class
`M` | number of chunks into which the expr matrix is split
`k` | number of clusters per group for k-means clustering

### Value

a list of four objects: 1) a list of matrices containing the subcentroids of each group 2) a matrix containing the subclusters for each cell for each group 3) the value of M 4) the value of k

### 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)

[Package scmap version 1.6.0 Index]