escoEstGroupMean: Estimate esco mean parameters for discrete cell groups
In JINJINT/splattermodify: Simple Simulation of Single-cell RNA Sequencing Data

Description Usage Arguments Details Value

Estimate rate and shape parameters for the gamma distribution used to simulate gene expression means for each cell group.

1 2	escoEstGroupMean(nonDE.normcounts, DE.normcounts, degenes, cellinfo, params)

`nonDE.normcounts`	library size normalised counts matrix for nonDE genes.
`DE.normcounts`	library size normalised counts matrix for DE genes.
`degenes`	a dataframe of two columns: "genes" and "clust", where "genes" contains the position of a degenes, and "clust" contains the corresponding cell group the degenes marks.
`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.

Parameter for the gamma distribution are estimated by fitting the mean normalised counts using fitdist. Particularly, the entries corresponds to DE genes in the cell group it marks are not considered in the fitting. The 'maximum goodness-of-fit estimation' method is used to minimise the Cramer-von Mises distance. This can fail in some situations, in which case the 'method of moments estimation' method is used instead. Prior to fitting the means are winsorized by setting the top and bottom 10 percent of values to the 10th and 90th percentiles.

escoParams object with estimated values.

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