Initial pre-processing

Generating a synthetic dataset

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:

  • Obtaining count matrices for each setting (i.e. each condition).
  • Integration and normalization between the conditions.
  • Reduced Dimension Estimations
  • (Clustering)
# 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.

Vizualisation

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