Plot expression of marker genes of the clusters identified by SC3 as a heatmap

Description

To find marker genes, for each gene a binary classifier is constructed based on the mean cluster expression values. The classifier prediction is then calculated using the gene expression ranks. The area under the receiver operating characteristic (ROC) curve is used to quantify the accuracy of the prediction. A p-value is assigned to each gene by using the Wilcoxon signed rank test. By default the genes with the area under the ROC curve (AUROC) > 0.85 and with the p-value < 0.01 are selected and the top 10 marker genes of each cluster are visualized in this heatmap.

Usage

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sc3_plot_markers.SCESet(object, k, auroc = 0.85, p.val = 0.01,
  show_pdata = NULL)

## S4 method for signature 'SCESet'
sc3_plot_markers(object, k, auroc = 0.85, p.val = 0.01,
  show_pdata = NULL)

Arguments

object

an object of 'SCESet' class

k

number of clusters

auroc

area under the ROC curve

p.val

significance threshold used for the DE genes

show_pdata

a vector of colnames of the pData(object) table. Default is NULL. If not NULL will add pData annotations to the columns of the output matrix

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