## 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.Â 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)
```

```
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
```

`p`