plot_confusion_HM() generates a heatmap showing the contribution of each group_var to a cell population after normalizing all levels in group_var to the same cell numbers.
Usage
plot_confusion_HM(
fcd,
cluster_slot,
cluster_var,
group_var,
numeric = FALSE,
size = 15,
title = "Confusion matrix",
cluster_cols = FALSE,
cluster_rows = FALSE
)Arguments
- fcd
flow cytometry data set, that has been subjected to clustering or cell type label prediction with cyCONDOR
- cluster_slot
string specifying which clustering slot to use to find variable specified in cluster_var
- cluster_var
string specifying variable name in cluster_slot that identifies cell population labels to be used to calculate the confusion.
- group_var
string indicating variable name in cell_anno that should be used to calculate the relative contribution to the variable specified in cluster_var.
- numeric
logical, indicating if levels in cluster_var should be ordered in ascending numerical order.
- size
size of the individual squares and font
- title
character string, title of the plot.
- cluster_cols
logical indicating if columns should be clustered (default: FALSE)
- cluster_rows
logical indicating if rows should be clustered (default: FALSE)
Value
plot_confusion_HM() first calculates cell counts for each combination of group_var and cell population and normalizes the counts to a total of 1000 cells per group_var.
Afterwards the percentage of cells coming from each level in group_var is calculated per cell population. The normalization of counts corrects the visualization for differences in total cells (events) measured per group_var.