plotDimReduceCluster: Plotting the cell labels on a dimensionality reduction plot

In compbiomed/celda: CEllular Latent Dirichlet Allocation

Description

Create a scatterplot for each row of a normalized gene expression matrix where x and y axis are from a data dimensionality reduction tool. The cells are colored by its given 'cluster' label.

Usage

plotDimReduceCluster(dim1, dim2, cluster, size = 1, xlab = "Dimension_1",
  ylab = "Dimension_2", specific_clusters = NULL)

Arguments

dim1	Numeric vector. First dimension from data dimensionality reduction output.
dim2	Numeric vector. Second dimension from data dimensionality reduction output.
cluster	Integer vector. Contains cluster labels for each cell.
size	Numeric. Sets size of point on plot. Default 1.
xlab	Character vector. Label for the x-axis. Default "Dimension_1".
ylab	Character vector. Label for the y-axis. Default "Dimension_2".
specific_clusters	Numeric vector. Only color cells in the specified clusters. All other cells will be grey. If NULL, all clusters will be colored. Default NULL.

Value

The plot as a ggplot object

Examples

celda.tsne <- celdaTsne(counts = celda.CG.sim$counts, celda.mod = celda.CG.mod)
plotDimReduceCluster(dim1 = celda.tsne[,1], dim2 = celda.tsne[,2],
                     cluster = as.factor(celda.CG.mod$z),
                     specific_clusters = c(1,2,3))

compbiomed/celda documentation built on May 25, 2019, 3:58 a.m.