Introduction to snifter

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.

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)

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)
#> Installing pyenv ...
#> Done! pyenv has been installed to '/github/home/.local/share/r-reticulate/pyenv/bin/pyenv'.
#> Using Python: /github/home/.pyenv/versions/3.12.10/bin/python3.12
#> Creating virtual environment '/github/home/.cache/R/basilisk/1.23.0/snifter/1.21.0/fitsne' ...
#> Done!
#> Installing packages: pip, wheel, setuptools
#> Installing packages: 'opentsne==1.0.2', 'scikit-learn==1.7.0', 'scipy==1.16.0', 'numpy==2.3.1'
#> Virtual environment '/github/home/.cache/R/basilisk/1.23.0/snifter/1.21.0/fitsne' successfully created.
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")

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 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        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: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggplot2_4.0.1    snifter_1.21.0   BiocStyle_2.39.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.7-4        gtable_0.3.6        jsonlite_2.0.0     
#>  [4] compiler_4.5.2      BiocManager_1.30.27 filelock_1.0.3     
#>  [7] Rcpp_1.1.1          parallel_4.5.2      assertthat_0.2.1   
#> [10] jquerylib_0.1.4     scales_1.4.0        png_0.1-8          
#> [13] yaml_2.3.12         fastmap_1.2.0       reticulate_1.44.1  
#> [16] lattice_0.22-7      R6_2.6.1            labeling_0.4.3     
#> [19] knitr_1.51          maketools_1.3.2     bslib_0.9.0        
#> [22] RColorBrewer_1.1-3  rlang_1.1.7         cachem_1.1.0       
#> [25] dir.expiry_1.19.0   xfun_0.56           S7_0.2.1           
#> [28] sass_0.4.10         sys_3.4.3           cli_3.6.5          
#> [31] withr_3.0.2         digest_0.6.39       grid_4.5.2         
#> [34] rappdirs_0.3.4      basilisk_1.23.0     lifecycle_1.0.5    
#> [37] vctrs_0.7.0         evaluate_1.0.5      glue_1.8.0         
#> [40] farver_2.1.2        buildtools_1.0.0    rmarkdown_2.30     
#> [43] tools_4.5.2         htmltools_0.5.9