snifter 1.4.0

snifter provides an R wrapper for the openTSNE
implementation of fast interpolated t-SNE (FI-tSNE).
It is based on *basilisk* and *reticulate*.
This vignette aims to provide a brief overview of typical use
when applied to scRNAseq data, but it does not provide a comprehensive
guide to the available options in the package.

It is highly advisable to review the documentation in snifter and the openTSNE documentation to gain a full understanding of the available options.

We will illustrate the use of snifter
using data from *scRNAseq* and
single cell utility functions provided by *scuttle*,
*scater* and
*scran* - first we load these libraries
and set a random seed to ensure the t-SNE visualisation is reproducible
(note: it is good practice to ensure that a t-SNE embedding is robust
by running the algorithm multiple times).

```
library("snifter")
library("scRNAseq")
library("scran")
library("scuttle")
library("scater")
library("ggplot2")
theme_set(theme_bw())
set.seed(42)
```

Before running t-SNE, we first load data generated
by Zeisel et al.
from *scRNAseq*. We filter this data to remove genes
expressed only in a small number of cells,
estimate normalisation factors using *scran*
and generate 20 principal components. We will use these principal
components to generate the t-SNE embedding later.

```
data <- ZeiselBrainData()
data <- data[rowMeans(counts(data) != 0) > 0.05, ]
data <- computeSumFactors(data, cluster = quickCluster(data))
data <- logNormCounts(data)
data <- runPCA(data, ncomponents = 20)
## Convert this to a factor to use as colouring variable later
data$level1class <- factor(data$level1class)
```

The main functionality of the package lies in the `fitsne`

function. This function returns a matrix of t-SNE co-ordinates. In this case,
we pass in the 20 principal components computed based on the
log-normalised counts. We colour points based on the discrete
cell types identified by the authors.

```
mat <- reducedDim(data)
fit <- fitsne(mat, random_state = 42L)
ggplot() +
aes(fit[, 1], fit[, 2], colour = data$level1class) +
geom_point(pch = 19) +
scale_colour_discrete(name = "Cell type") +
labs(x = "t-SNE 1", y = "t-SNE 2")
```