plot.COMPASSResult {COMPASS} | R Documentation |
This function can be used to visualize the mean probability of response; that is, the probability that there is a difference in response between samples subjected to the 'treatment' condition, and samples subjected to the 'control' condition.
## S3 method for class 'COMPASSResult' plot( x, y, subset = NULL, threshold = 0.01, minimum_dof = 1, maximum_dof = Inf, must_express = NULL, row_annotation, palette = colorRampPalette(brewer.pal(10, "Purples"))(20), show_rownames = FALSE, show_colnames = FALSE, measure = NULL, order_by = FunctionalityScore, order_by_max_functionality = TRUE, markers = NULL, ... )
x |
An object of class |
y |
This argument gets passed to |
subset |
An R expression, evaluated within the metadata, used to determine which individuals should be kept. |
threshold |
A numeric threshold for filtering under-expressed
categories. Any categories with mean score < |
minimum_dof |
The minimum degree of functionality for the categories to be plotted. |
maximum_dof |
The maximum degree of functionality for the categories to be plotted. |
must_express |
A character vector of markers that should be included
in each subset plotted. For example, |
row_annotation |
A vector of names, pulled from the metadata, to be used for row annotation. |
palette |
The colour palette to be used. |
show_rownames |
Boolean; if |
show_colnames |
Boolean; if |
measure |
Optional. By default, we produce a heatmap of the mean
gammas produced in a model fit. We can override this by supplying a
matrix of suitable dimension as well; these can be generated with
the |
order_by |
Order rows within a group. This should be a function;
e.g. |
order_by_max_functionality |
Order columns by functionality within each degree subset.
to |
markers |
specifies a subset of markers to plot. default is NULL, which means all markers. |
... |
Optional arguments passed to |
The plot as a grid
object (grob
). It can be redrawn
with e.g. grid::grid.draw()
.
## visualize the mean probability of reponse plot(CR) ## visualize the proportion of cells belonging to a category plot(CR, measure=PosteriorPs(CR))