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 by generating some toy data. First, we’ll load the needed libraries, and set a random seed to ensure the simulated data are reproducible (note: it is good practice to ensure that a t-SNE embedding is robust by running the algorithm multiple times).

library("snifter")
library("ggplot2")
theme_set(theme_bw())
set.seed(42)

n_obs <- 500
n_feats <- 200
means_1 <- rnorm(n_feats)
means_2 <- rnorm(n_feats)
counts_a <- replicate(n_obs, rnorm(n_feats, means_1))
counts_b <- replicate(n_obs, rnorm(n_feats, means_2))
counts <- t(cbind(counts_a, counts_b))
label <- rep(c("A", "B"), each = n_obs)

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.

fit <- fitsne(counts, random_state = 42L)
ggplot() +
    aes(fit[, 1], fit[, 2], colour = label) +
    geom_point(pch = 19) +
    scale_colour_discrete(name = "Cluster") +
    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(counts), nrow(counts) / 2)
train_ind <- setdiff(seq_len(nrow(counts)), test_ind)
train_mat <- counts[train_ind, ]
test_mat <- counts[test_ind, ]

train_label <- label[train_ind]
test_label <- label[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 = "Cluster") +
    scale_shape_discrete(name = NULL) +
    labs(x = "t-SNE 1", y = "t-SNE 2")

Session information

sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#> 
#> 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       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggplot2_3.5.1    snifter_1.16.0   BiocStyle_2.34.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.7-1          gtable_0.3.6          jsonlite_1.8.9       
#>  [4] highr_0.11            dplyr_1.1.4           compiler_4.4.1       
#>  [7] BiocManager_1.30.25   filelock_1.0.3        tinytex_0.53         
#> [10] tidyselect_1.2.1      Rcpp_1.0.13           magick_2.8.5         
#> [13] parallel_4.4.1        assertthat_0.2.1      jquerylib_0.1.4      
#> [16] scales_1.3.0          png_0.1-8             yaml_2.3.10          
#> [19] fastmap_1.2.0         reticulate_1.39.0     lattice_0.22-6       
#> [22] R6_2.5.1              labeling_0.4.3        generics_0.1.3       
#> [25] knitr_1.48            tibble_3.2.1          bookdown_0.41        
#> [28] munsell_0.5.1         pillar_1.9.0          bslib_0.8.0          
#> [31] rlang_1.1.4           utf8_1.2.4            cachem_1.1.0         
#> [34] dir.expiry_1.14.0     xfun_0.48             sass_0.4.9           
#> [37] cli_3.6.3             withr_3.0.2           magrittr_2.0.3       
#> [40] digest_0.6.37         grid_4.4.1            basilisk_1.18.0      
#> [43] lifecycle_1.0.4       vctrs_0.6.5           evaluate_1.0.1       
#> [46] glue_1.8.0            farver_2.1.2          fansi_1.0.6          
#> [49] colorspace_2.1-1      rmarkdown_2.28        pkgconfig_2.0.3      
#> [52] basilisk.utils_1.18.0 tools_4.4.1           htmltools_0.5.8.1