plotDimReduceModule: Plotting the Celda module probability on a dimensionality...
In compbiomed/celda: CEllular Latent Dirichlet Allocation

Description Usage Arguments Value Examples

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).

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plotDimReduceModule(dim1, dim2, counts, celda.mod, modules = NULL,
  rescale = TRUE, size = 1, xlab = "Dimension_1", ylab = "Dimension_2",
  color_low = "grey", color_mid = NULL, color_high = "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 'celda.mod'.
`celda.mod`	Celda object of class "celda_G" or "celda_CG".
`modules`	Character vector. Module(s) from celda model to be plotted.
`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".
`color_low`	Character. A color available from ‘colors()'. The color will be used to signify the lowest values on the scale. Default ’grey'.
`color_mid`	Character. A color available from 'colors()'. The color will be used to signify the midpoint on the scale.
`color_high`	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

celda.tsne <- celdaTsne(counts = celda.CG.sim$counts, 
                        celda.mod = celda.CG.mod)
plotDimReduceModule(dim1 = celda.tsne[,1], dim2 = celda.tsne[,2], 
                    counts = celda.CG.sim$counts, celda.mod = celda.CG.mod,
                    modules = c("L1","L2"))