Dimensionality reduction
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 -12.0 15.3
#> 2 -3.37 7.18
#> 3 3.33 35.5
#> 4 3.41 16.8
#> 5 -10.7 18.3
#> 6 0.690 22.7By 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 17.3 -1.09
#> 2 8.47 -10.1
#> 3 27.7 -22.7
#> 4 16.4 -14.4
#> 5 19.9 -5.83
#> 6 11.1 -21.4
#> 7 8.32 -11.5
#> 8 18.0 -24.4
#> 9 12.2 -17.5
#> 10 8.46 -3.67
#> # ℹ 2,990 more rowsChanging 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 9.95 -2.37
#> 2 8.75 -2.24
#> 3 3.79 0.695
#> 4 3.66 2.09
#> 5 9.91 -2.15
#> 6 1.33 2.84
#> 7 9.74 -1.85
#> 8 3.17 1.26
#> 9 5.48 -1.08
#> 10 9.54 -4.72
#> # 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 rowsMethod 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 rowssee ?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 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 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-r0.3.32.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=en_US.UTF-8
#> [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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] tidyr_1.3.2 stringr_1.6.0
#> [3] HDCytoData_1.33.1 flowCore_2.25.1
#> [5] SummarizedExperiment_1.43.0 Biobase_2.73.2
#> [7] GenomicRanges_1.65.4 Seqinfo_1.3.2
#> [9] IRanges_2.47.5 S4Vectors_0.51.10
#> [11] MatrixGenerics_1.25.0 matrixStats_1.5.0
#> [13] ExperimentHub_3.3.2 AnnotationHub_4.3.2
#> [15] BiocFileCache_3.3.0 dbplyr_2.6.0
#> [17] BiocGenerics_0.59.12 generics_0.1.4
#> [19] forcats_1.0.1 ggplot2_4.0.3
#> [21] dplyr_1.2.1 tidytof_1.7.0
#> [23] rmarkdown_2.32
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 sys_3.4.3 jsonlite_2.0.0
#> [4] shape_1.4.6.1 magrittr_2.0.5 farver_2.1.2
#> [7] vctrs_0.7.3 memoise_2.0.1 sparsevctrs_0.3.6
#> [10] BiocBaseUtils_1.15.1 htmltools_0.5.9 S4Arrays_1.13.2
#> [13] curl_8.0.0 SparseArray_1.13.4 sass_0.4.10
#> [16] parallelly_1.48.0 bslib_0.12.0 httr2_1.3.0
#> [19] lubridate_1.9.5 cachem_1.1.0 buildtools_1.0.0
#> [22] igraph_2.3.4 lifecycle_1.0.5 iterators_1.0.14
#> [25] pkgconfig_2.0.3 Matrix_1.7-6 R6_2.6.1
#> [28] fastmap_1.2.0 future_1.76.0 digest_0.6.39
#> [31] AnnotationDbi_1.75.2 RSpectra_0.16-2 RSQLite_3.53.3
#> [34] labeling_0.4.3 filelock_1.0.3 cytolib_2.25.0
#> [37] yardstick_1.4.0 timechange_0.4.0 httr_1.4.9
#> [40] polyclip_1.10-7 abind_1.4-8 compiler_4.6.1
#> [43] bit64_4.8.6 withr_3.0.3 doParallel_1.0.17
#> [46] S7_0.2.2 viridis_0.6.5 DBI_1.3.0
#> [49] ggforce_0.5.0 MASS_7.3-66 lava_1.9.3
#> [52] embed_1.2.2 rappdirs_0.3.4 DelayedArray_0.39.8
#> [55] tools_4.6.1 otel_0.2.0 future.apply_1.20.2
#> [58] nnet_7.3-21 glue_1.8.1 grid_4.6.1
#> [61] Rtsne_0.17 recipes_1.4.0 gtable_0.3.6
#> [64] tzdb_0.5.0 class_7.3-24 data.table_1.18.6.1
#> [67] hms_1.1.4 utf8_1.2.6 tidygraph_1.3.1
#> [70] XVector_0.53.0 RcppAnnoy_0.0.23 ggrepel_0.9.8
#> [73] BiocVersion_3.24.0 foreach_1.5.2 pillar_1.11.1
#> [76] RcppHNSW_0.7.0 splines_4.6.1 tweenr_2.0.3
#> [79] lattice_0.23-1 survival_3.8-12 bit_4.6.0
#> [82] RProtoBufLib_2.25.0 tidyselect_1.2.1 maketools_1.3.2
#> [85] Biostrings_2.81.9 knitr_1.52 gridExtra_2.3.1
#> [88] xfun_0.61 graphlayouts_1.2.5 hardhat_1.4.3
#> [91] timeDate_4052.112 stringi_1.8.9 yaml_2.3.12
#> [94] evaluate_1.0.5 codetools_0.2-20 ggraph_2.2.2
#> [97] tibble_3.3.1 BiocManager_1.30.27 cli_3.6.6
#> [100] uwot_0.2.5 rpart_4.1.27 jquerylib_0.1.4
#> [103] Rcpp_1.1.2 globals_0.19.1 png_0.1-9
#> [106] parallel_4.6.1 gower_1.0.2 readr_2.2.0
#> [109] blob_1.3.0 listenv_1.1.0 glmnet_5.1
#> [112] viridisLite_0.4.3 ipred_0.9-16 ggridges_0.5.7
#> [115] scales_1.4.0 prodlim_2026.03.11 crayon_1.5.3
#> [118] purrr_1.2.2 rlang_1.3.0 KEGGREST_1.53.6