indexCell: Create an index for a dataset to enable fast approximate...

In hemberg-lab/scmap: A tool for unsupervised projection of single cell RNA-seq data

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)
# remove features with duplicated names
sce <- sce[!duplicated(rownames(sce)), ]
sce <- selectFeatures(sce)
sce <- indexCell(sce)

hemberg-lab/scmap documentation built on Nov. 29, 2020, 1:06 p.m.