plotDimReduceFeature: Plotting feature expression on a dimensionality reduction...
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 expression of the specified feature.

plotDimReduceFeature(dim1, dim2, counts, features, normalize = TRUE,
  exact.match = TRUE, trim = c(-2, 2), 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.
`features`	Character vector. Uses these genes for plotting.
`normalize`	Logical. Whether to normalize the columns of 'counts'. Default TRUE.
`exact.match`	Logical. Whether an exact match or a partial match using 'grep()' is used to look up the feature in the rownames of the counts matrix. Default TRUE.
`trim`	Numeric vector. Vector of length two that specifies the lower and upper bounds for the data. This threshold is applied after row scaling. Set to NULL to disable. Default c(-2,2).
`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$counts,
                        celda.mod = celda.CG.mod)
plotDimReduceFeature(dim1 = celda.tsne[,1], dim2 = celda.tsne[,2],
                     counts = celda.CG.sim$counts,
                     features = c("Gene_99"), exact.match = TRUE)