diffgenes: Compute Expression Differences between Clusters

In RaceID: Identification of Cell Types, Inference of Lineage Trees, and Prediction of Noise Dynamics from Single-Cell RNA-Seq Data

Compute Expression Differences between Clusters

Description

This functions computes expression differences between clusters and ranks genes by z-score differences.

Usage

diffgenes(object, cl1, cl2, mincount = 1)

Arguments

object	SCseq class object.
cl1	A vector of valid cluster numbers (contained in the cpart slot of the SCseq object). Represents the first group of the comparison.
cl2	A vector of valid cluster numbers (contained in the cpart slot of the SCseq object). Represents the second group of the comparison.
mincount	Minimal normalized expression level of a gene to be included into the analysis. A gene needs to be expressed at this level in at least a single cell.

Value

A list with four components:

z	a vector of z-scores in decreasing order with genes up-regulated in cl1 appearing at the top of the list.
cl1	a data.frame with expression values for cells in cl1.
cl2	a data.frame with expression values for cells in cl2.
cl1n	a vector of cluster numbers for cells in cl1.
cl2n	a vector of cluster numbers for cells in cl2.

Examples

sc <- SCseq(intestinalDataSmall)
sc <- filterdata(sc)
sc <- compdist(sc)
sc <- clustexp(sc)
sc <- findoutliers(sc)
x <- diffgenes(sc,1,2)
head(x$z)
plotdiffgenes(x,names(x$z)[1])

RaceID documentation built on Sept. 28, 2023, 5:06 p.m.