plotDimReduceModule {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 the module probability(s).
plotDimReduceModule(dim1, dim2, counts, celdaMod, modules = NULL, rescale = TRUE, size = 1, xlab = "Dimension_1", ylab = "Dimension_2", colorLow = "grey", colorMid = NULL, colorHigh = "blue")
dim1 |
Numeric vector. First dimension from data dimensionality reduction output. |
dim2 |
Numeric vector. Second dimension from data dimensionality reduction output. |
counts |
Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate 'celdaMod'. |
celdaMod |
Celda object of class "celda_G" or "celda_CG". |
modules |
Character vector. Module(s) from celda model to be plotted. e.g. c("1", "2"). |
rescale |
Logical. Whether rows of the matrix should be rescaled to [0, 1]. Default TRUE. |
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". |
colorLow |
Character. A color available from 'colors()'. The color will be used to signify the lowest values on the scale. Default 'grey'. |
colorMid |
Character. A color available from 'colors()'. The color will be used to signify the midpoint on the scale. |
colorHigh |
Character. A color available from 'colors()'. The color will be used to signify the highest values on the scale. Default 'blue'. |
The plot as a ggplot object
data(celdaCGSim, celdaCGMod) celdaTsne <- celdaTsne(counts = celdaCGSim$counts, celdaMod = celdaCGMod) plotDimReduceModule( dim1 = celdaTsne[, 1], dim2 = celdaTsne[, 2], counts = celdaCGSim$counts, celdaMod = celdaCGMod, modules = c("1", "2"))