cellpypes (version 0.3.0)

plot_last: Plot the last modified rule or class

Description

Plot the last modified rule or class

Usage

plot_last(
  obj,
  show_feat = TRUE,
  what = "rule",
  fast = NULL,
  legend_rel_width = 0.3,
  overdispersion = 0.01
)

Value

Returns a ggplot2 object with the plot.

Arguments

obj

A cellpypes object, see section cellpypes Objects below.

show_feat

If TRUE (default), a second panel shows the feature plot of the relevant gene.

what

Either "rule" or "class".

fast

Set this to TRUE if you want fast plotting in spite of many cells (using the scattermore package). If NULL (default), cellpypes decides automatically and fast plotting is done for more than 10k cells, if FALSE it always uses geom_point.

legend_rel_width

Relative width compared to the other two plots (only relevant if show_feat=TRUE).

overdispersion

Defaults to 0.01, only change if you know what you are doing. See further classify.

cellpypes Objects

A cellpypes object is a list with four slots:

raw

(sparse) matrix with genes in rows, cells in columns

totalUMI

the colSums of obj$raw

embed

two-dimensional embedding of the cells, provided as data.frame or tibble with two columns and one row per cell.

neighbors

index matrix with one row per cell and k columns, where k is the number of nearest neighbors (we recommend 15<k<100, e.g. k=50). Here are two ways to get the neighbors index matrix:

• Use find_knn(featureMatrix)$idx, where featureMatrix could be principal components, latent variables or normalized genes (features in rows, cells in columns).

• use as(seurat@graphs[["RNA_nn"]], "dgCMatrix")> .1 to extract the kNN graph computed on RNA. The > .1 ensures this also works with RNA_snn, wknn/wsnn or any other available graph – check with names(seurat@graphs).

Examples

plot_last(rule(simulated_umis, "T", "CD3E",">", 1))