We will use a synthetic dataset to illustrate the functionalities of the condiments package. We start directly with a dataset where the following steps are assumed to have been run:
# For analysis library(condiments) library(slingshot) # For data manipulation library(dplyr) library(tidyr) # For visualization library(ggplot2) library(RColorBrewer) library(viridis) set.seed(2071) theme_set(theme_classic())
data("toy_dataset", package = "condiments") df <- toy_dataset$sd
As such, we start with a matrix
df of metadata for the cells: coordinates in a reduced dimension space
(Dim1, Dim2), a vector of conditions assignments
conditions (A or B) and a lineage assignment.
We can first plot the cells on the reduced dimensions
p <- ggplot(df, aes(x = Dim1, y = Dim2, col = conditions)) + geom_point() + scale_color_brewer(type = "qual") p
We can also visualize the underlying skeleton structure of the two conditions.
p <- ggplot(df, aes(x = Dim1, y = Dim2, col = conditions)) + geom_point(alpha = .5) + geom_point(data = toy_dataset$mst, size = 2) + geom_path(data = toy_dataset$mst, aes(group = lineages), size = 1.5) + scale_color_brewer(type = "qual") + facet_wrap(~conditions) + guides(col = FALSE) p