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.
MarkerTest(object, cells.1, cells.2, genes.use = NULL, print.bar = TRUE)Seurat object
Group 1 cells
Group 2 cells
Genes to test. Default is to use all genes
Print a progress bar once expression testing begins (uses pbapply to do this)
Returns a 'predictive power' (abs(AUC-0.5)) ranked matrix of putative differentially expressed genes.
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.