MarkerTest: ROC-based marker discovery

In nukappa/seurat_v2: Seurat : R toolkit for single cell genomics

Description

Identifies 'markers' of gene expression using ROC analysis. For each gene, evaluates (using AUC) a classifier built on that gene alone, to classify between two groups of cells.

Usage

MarkerTest(object, cells.1, cells.2, genes.use = NULL, print.bar = TRUE)

Arguments

object	Seurat object
cells.1	Group 1 cells
cells.2	Group 2 cells
genes.use	Genes to test. Default is to use all genes
print.bar	Print a progress bar once expression testing begins (uses pbapply to do this)

Details

An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. Each of the cells in cells.1 exhibit a higher level than each of the cells in cells.2). An AUC value of 0 also means there is perfect classification, but in the other direction. A value of 0.5 implies that the gene has no predictive power to classify the two groups.

Value

Returns a 'predictive power' (abs(AUC-0.5)) ranked matrix of putative differentially expressed genes.

nukappa/seurat_v2 documentation built on May 24, 2019, 9:57 a.m.