escoEstGroupOutlier: Estimate esco Group expression outlier parameters for...
In JINJINT/splattermodify: Simple Simulation of Single-cell RNA Sequencing Data

Description Usage Arguments Details Value

Parameters are estimated by comparing means of individual genes to the median mean expression level.

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

`nonDE.normcounts`	library size normalised counts matrix for non DE 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.

Expression outlier genes are detected using the Median Absolute Deviation (MAD) from median method. If the log2 mean expression of a gene is greater than two MADs above the median log2 mean expression it is designated as an outlier. Specifically, the meadian log2 mean expression for a degene is computed with the cell samples that does not belongs to the cell group it marked. The proportion of outlier genes is used to estimate the outlier probability. Factors for each outlier gene are calculated by dividing mean expression by the median mean expression. A log-normal distribution is then fitted to these factors in order to estimate the outlier factor location and scale parameters using fitdist.

escoParams object with estimated values.

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