marker.test: ROC-based marker discovery

In paodan/studySeu: 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

marker.test(object, cells.1, cells.2, genes.use = NULL, thresh.use = log(2),
  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.
thresh.use	Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Increasing thresh.use speeds up the function, but can miss weaker signals.
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.

paodan/studySeu documentation built on May 23, 2019, 3:06 p.m.