M3D_getmarkers: Identify marker genes
In M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Description Usage Arguments Details Value Examples

Calculates area under the ROC curve for each gene to predict the best group of cells from all other cells.

1	M3DropGetMarkers(expr_mat, labels)

`expr_mat`	a numeric matrix of normalized expression values, columns = samples, rows = genes.
`labels`	a vector of group ids for each cell/sample.

Uses the ROCR package to calculate the AUC for each gene for the group with the highest average rank. Significant is calculated using a Wilcox rank-sum test.

A dataframe with a row for each gene and columns: AUC, Group (which label this gene had the highest average rank for), and pval (uncorrected p-value of prediction).

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  library(M3DExampleData)
  norm <- M3DropConvertData(Mmus_example_list$data, is.counts=TRUE)
  marker_gene_table <- M3DropGetMarkers(norm, Mmus_example_list$labels)