escoEstimate: Estimate esco simulation parameters

In JINJINT/splattermodify: Simple Simulation of Single-cell RNA Sequencing Data

Description

Estimate simulation parameters for the esco simulation from a real dataset. See the individual estimation functions for more details on how this is done.

Usage

escoEstimate(counts, dirname, group = FALSE, cellinfo = NULL,
  params = newescoParams())

## S3 method for class 'SingleCellExperiment'
escoEstimate(counts, dirname,
  group = FALSE, cellinfo = NULL, params = newescoParams())

## S3 method for class 'matrix'
escoEstimate(counts, dirname, group = FALSE,
  cellinfo = NULL, params = newescoParams())

Arguments

counts	either a counts matrix or a SingleCellExperiment object containing count data to estimate parameters from.
dirname	a tring of directory name to indicate where to save the results
group	whether the data is believed to be of discrete cell groups or not, if yes, the corresponding cellinfo indicating the cell group labels need to be input as well
cellinfo	a vector of length n, where n is the number of cells. Each entries is the group identity of a cell.
params	escoParams object to store estimated values in.

Value

escoParams object containing the estimated parameters.

See Also

escoEstMean, escoEstLib, escoEstOutlier, escoEstBCV, escoEstDropout, escoEstDE, escoEstGroupMean, escoEstGroupLib, escoEstGroupOutlier

Examples

# Load example data
data = matrix(rpois(2000*40,2),2000,40)
params <- escoEstimate(data)
params

JINJINT/splattermodify documentation built on May 19, 2021, 4:05 p.m.