DSAVEGetSingleCellDivergence: DSAVEGetSingleCellDivergence

In SysBioChalmers/DSAVE-R: DSAVE: Tools to Investigate Variation and Purity of Subpopulations in Single-Cell RNA-Seq Data

Description

Calculates the DSAVE cell-wise variation metric.

Usage

DSAVEGetSingleCellDivergence(
  data,
  minUMIsPerCell = 200,
  numGenesGW = 5,
  tpmLowerBound = 0,
  iterations = 15,
  silent = FALSE
)

Arguments

data	numeric matrix (can be sparse), the input dataset (cell population)
minUMIsPerCell	All cells are downsampled to this number of counts, or. higher if possible without losing cells. The result is NA for cells with lower counts.
numGenesGW	The number of gene specific divergence values to store per cell. Will store the most divergent ones
tpmLowerBound	(optional) TPM lower bound, genes below this will not be included. Defaults to 0 (meaning all genes are included).
iterations	(optional) The number of times to iterate. Defaults to 15.
silent	(optional) If true, no progress bar is shown. Defaults to FALSE

Details

The divergence is the negative log-likelihood for getting the observed counts' distribution for each cell when sampling counts from the mean dataset gene expression. The values are positive, and the higher the value, the more divergent the cell is.

Value

a list (divs = vector of divergence, one value per cell, geneDivGenes = The top divergent genes per cell, a matrix, geneDivVals = The corresponding divergence values for the divergent genes per cell)

Author(s)

Johan Gustafsson, <[e-mail]>

Examples

## Not run: a = DSAVEGetSingleCellDivergence(data)

SysBioChalmers/DSAVE-R documentation built on Oct. 19, 2021, 11:37 p.m.