plotDimReduceCluster {celda} | R Documentation |
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.
plotDimReduceCluster(dim1, dim2, cluster, size = 1, xlab = "Dimension_1", ylab = "Dimension_2", specificClusters = NULL, labelClusters = FALSE, labelSize = 3.5)
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". |
specificClusters |
Numeric vector. Only color cells in the specified clusters. All other cells will be grey. If NULL, all clusters will be colored. Default NULL. |
labelClusters |
Logical. Whether the cluster labels are plotted. Default FALSE. |
labelSize |
Numeric. Sets size of label if labelClusters is TRUE. Default 3.5. |
The plot as a ggplot object
data(celdaCGSim, celdaCGMod) celdaTsne <- celdaTsne(counts = celdaCGSim$counts, celdaMod = celdaCGMod) plotDimReduceCluster(dim1 = celdaTsne[, 1], dim2 = celdaTsne[, 2], cluster = as.factor(clusters(celdaCGMod)$z), specificClusters = c(1, 2, 3))