library(tidytof)
library(dplyr)
library(ggplot2)
A useful tool for visualizing the phenotypic relationships between single cells and clusters of cells is dimensionality reduction, a form of unsupervised machine learning used to represent high-dimensional datasets in a smaller number of dimensions.
{tidytof} includes several dimensionality reduction algorithms commonly used by biologists: Principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP). To apply these to a dataset, use tof_reduce_dimensions().
Dimensionality reduction with tof_reduce_dimensions().
Here is an example call to tof_reduce_dimensions() in which we use tSNE to visualize data in {tidytof}’s built-in phenograph_data dataset.
data(phenograph_data)
# perform the dimensionality reduction
phenograph_tsne <-
phenograph_data |>
tof_preprocess() |>
tof_reduce_dimensions(method = "tsne")
#> Loading required namespace: Rtsne
# select only the tsne embedding columns
phenograph_tsne |>
select(contains("tsne")) |>
head()
#> # A tibble: 6 × 2
#> .tsne1 .tsne2
#> <dbl> <dbl>
#> 1 3.44 7.34
#> 2 5.78 9.95
#> 3 26.3 8.05
#> 4 12.8 15.9
#> 5 9.85 8.35
#> 6 16.4 20.6
By default, tof_reduce_dimensions will add reduced-dimension feature embeddings to the input tof_tbl and return the augmented tof_tbl (that is, a tof_tbl with new columns for each embedding dimension) as its result. To return only the features embeddings themselves, set augment to FALSE (as in tof_cluster).
phenograph_data |>
tof_preprocess() |>
tof_reduce_dimensions(method = "tsne", augment = FALSE)
#> # A tibble: 3,000 × 2
#> .tsne1 .tsne2
#> <dbl> <dbl>
#> 1 2.97 16.3
#> 2 9.05 9.26
#> 3 32.3 5.47
#> 4 16.7 14.4
#> 5 6.64 17.9
#> 6 22.3 9.96
#> 7 12.0 6.54
#> 8 25.0 6.73
#> 9 18.6 9.82
#> 10 4.04 7.99
#> # ℹ 2,990 more rows
Changing the method argument results in different low-dimensional embeddings:
phenograph_data |>
tof_reduce_dimensions(method = "umap", augment = FALSE)
#> # A tibble: 3,000 × 2
#> .umap1 .umap2
#> <dbl> <dbl>
#> 1 -10.2 3.16
#> 2 -9.15 2.47
#> 3 -7.48 0.424
#> 4 -6.38 -1.20
#> 5 -10.3 2.86
#> 6 -0.240 -4.18
#> 7 -10.3 2.33
#> 8 -7.54 -1.31
#> 9 -5.59 0.669
#> 10 -9.28 4.74
#> # ℹ 2,990 more rows
phenograph_data |>
tof_reduce_dimensions(method = "pca", augment = FALSE)
#> # A tibble: 3,000 × 5
#> .pc1 .pc2 .pc3 .pc4 .pc5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -2.77 1.23 -0.868 0.978 3.49
#> 2 -0.969 -1.02 -0.787 1.22 0.329
#> 3 -2.36 2.54 -1.95 -0.882 -1.30
#> 4 -3.68 -0.00565 0.962 0.410 0.788
#> 5 -4.03 2.07 -0.829 1.59 5.39
#> 6 -2.59 -0.108 1.32 -1.41 -1.24
#> 7 -1.55 -0.651 -0.233 1.08 0.129
#> 8 -1.18 -0.446 0.134 -0.771 -0.932
#> 9 -2.00 -0.485 0.593 -0.0416 -0.658
#> 10 -0.0356 -0.924 -0.692 1.45 0.270
#> # ℹ 2,990 more rows
Method specifications for tof_reduce_*() functions
tof_reduce_dimensions() provides a high-level API for three lower-level functions: tof_reduce_pca(), tof_reduce_umap(), and tof_reduce_tsne(). The help files for each of these functions provide details about the algorithm-specific method specifications associated with each of these dimensionality reduction approaches. For example, tof_reduce_pca takes the num_comp argument to determine how many principal components should be returned:
# 2 principal components
phenograph_data |>
tof_reduce_pca(num_comp = 2)
#> # A tibble: 3,000 × 2
#> .pc1 .pc2
#> <dbl> <dbl>
#> 1 -2.77 1.23
#> 2 -0.969 -1.02
#> 3 -2.36 2.54
#> 4 -3.68 -0.00565
#> 5 -4.03 2.07
#> 6 -2.59 -0.108
#> 7 -1.55 -0.651
#> 8 -1.18 -0.446
#> 9 -2.00 -0.485
#> 10 -0.0356 -0.924
#> # ℹ 2,990 more rows
# 3 principal components
phenograph_data |>
tof_reduce_pca(num_comp = 3)
#> # A tibble: 3,000 × 3
#> .pc1 .pc2 .pc3
#> <dbl> <dbl> <dbl>
#> 1 -2.77 1.23 -0.868
#> 2 -0.969 -1.02 -0.787
#> 3 -2.36 2.54 -1.95
#> 4 -3.68 -0.00565 0.962
#> 5 -4.03 2.07 -0.829
#> 6 -2.59 -0.108 1.32
#> 7 -1.55 -0.651 -0.233
#> 8 -1.18 -0.446 0.134
#> 9 -2.00 -0.485 0.593
#> 10 -0.0356 -0.924 -0.692
#> # ℹ 2,990 more rows
see ?tof_reduce_pca, ?tof_reduce_umap, and ?tof_reduce_tsne for additional details.
Visualization using tof_plot_cells_embedding()
Regardless of the method used, reduced-dimension feature embeddings can be visualized using {ggplot2} (or any graphics package). {tidytof} also provides some helper functions for easily generating dimensionality reduction plots from a tof_tbl or tibble with columns representing embedding dimensions:
# plot the tsne embeddings using color to distinguish between clusters
phenograph_tsne |>
tof_plot_cells_embedding(
embedding_cols = contains(".tsne"),
color_col = phenograph_cluster
)
# plot the tsne embeddings using color to represent CD11b expression
phenograph_tsne |>
tof_plot_cells_embedding(
embedding_cols = contains(".tsne"),
color_col = cd11b
) +
ggplot2::scale_fill_viridis_c()
Such visualizations can be helpful in qualitatively describing the phenotypic differences between the clusters in a dataset. For example, in the example above, we can see that one of the clusters has high CD11b expression, whereas the others have lower CD11b expression.
Session info
sessionInfo()
#> R version 4.5.0 beta (2025-04-02 r88102)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 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)
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#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
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