Introduction to snifter
26 October 2021
Package
snifter 1.4.0
1 Introduction
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.
2 Setting up the data
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)3 Running t-SNE
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$level1class) +
geom_point(pch = 19) +
scale_colour_discrete(name = "Cell type") +
labs(x = "t-SNE 1", y = "t-SNE 2")
4 Projecting new data into an existing embedding
The openTNSE package, and by extension snifter, also allows the embedding of new data into an existing t-SNE embedding. Here, we will split the data into “training” and “test” sets. Following this, we generate a t-SNE embedding using the training data, and project the test data into this embedding.
test_ind <- sample(nrow(mat), nrow(mat) / 2)
train_ind <- setdiff(seq_len(nrow(mat)), test_ind)
train_mat <- mat[train_ind, ]
test_mat <- mat[test_ind, ]
train_label <- data$level1class[train_ind]
test_label <- data$level1class[test_ind]
embedding <- fitsne(train_mat, random_state = 42L)Once we have generated the embedding, we can now project the unseen test
data into this t-SNE embedding.
new_coords <- project(embedding, new = test_mat, old = train_mat)
ggplot() +
geom_point(
aes(embedding[, 1], embedding[, 2],
colour = train_label,
shape = "Train"
)
) +
geom_point(
aes(new_coords[, 1], new_coords[, 2],
colour = test_label,
shape = "Test"
)
) +
scale_colour_discrete(name = "Cell type") +
scale_shape_discrete(name = NULL) +
labs(x = "t-SNE 1", y = "t-SNE 2")
Session information
sessionInfo()
#> R version 4.1.1 (2021-08-10)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] scater_1.22.0 ggplot2_3.3.5
#> [3] scran_1.22.0 scuttle_1.4.0
#> [5] scRNAseq_2.7.2 SingleCellExperiment_1.16.0
#> [7] SummarizedExperiment_1.24.0 Biobase_2.54.0
#> [9] GenomicRanges_1.46.0 GenomeInfoDb_1.30.0
#> [11] IRanges_2.28.0 S4Vectors_0.32.0
#> [13] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
#> [15] matrixStats_0.61.0 snifter_1.4.0
#> [17] BiocStyle_2.22.0
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#> [11] fansi_0.5.0 magrittr_2.0.1
#> [13] memoise_2.0.0 ScaledMatrix_1.2.0
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#> [23] xfun_0.27 dplyr_1.0.7
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#> [29] gtable_0.3.0 zlibbioc_1.40.0
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