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 -11.5 -11.1
#> 2 -11.9 0.101
#> 3 -34.7 -3.90
#> 4 -20.7 -1.90
#> 5 -16.6 -10.3
#> 6 -22.1 6.91
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 18.2 1.49
#> 2 15.5 -8.09
#> 3 33.6 -6.60
#> 4 9.37 -12.3
#> 5 19.0 -1.94
#> 6 20.6 -14.6
#> 7 16.7 -9.18
#> 8 26.6 -6.96
#> 9 19.6 -12.6
#> 10 11.6 2.33
#> # 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 -9.39 2.33
#> 2 -8.26 2.16
#> 3 -5.20 -0.489
#> 4 -4.57 -2.27
#> 5 -9.35 1.93
#> 6 -1.28 -3.50
#> 7 -9.22 1.44
#> 8 -4.56 -1.27
#> 9 -4.49 1.66
#> 10 -9.14 4.35
#> # 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 Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] tidyr_1.3.1 stringr_1.5.1
#> [3] HDCytoData_1.25.0 flowCore_2.19.0
#> [5] SummarizedExperiment_1.37.0 Biobase_2.67.0
#> [7] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
#> [9] IRanges_2.41.0 S4Vectors_0.45.0
#> [11] MatrixGenerics_1.19.0 matrixStats_1.4.1
#> [13] ExperimentHub_2.15.0 AnnotationHub_3.15.0
#> [15] BiocFileCache_2.15.0 dbplyr_2.5.0
#> [17] BiocGenerics_0.53.0 forcats_1.0.0
#> [19] ggplot2_3.5.1 dplyr_1.1.4
#> [21] tidytof_1.1.0
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_1.8.9 shape_1.4.6.1 magrittr_2.0.3
#> [4] farver_2.1.2 rmarkdown_2.28 zlibbioc_1.53.0
#> [7] vctrs_0.6.5 memoise_2.0.1 htmltools_0.5.8.1
#> [10] S4Arrays_1.7.0 curl_5.2.3 SparseArray_1.7.0
#> [13] sass_0.4.9 parallelly_1.38.0 bslib_0.8.0
#> [16] lubridate_1.9.3 cachem_1.1.0 commonmark_1.9.2
#> [19] igraph_2.1.1 mime_0.12 lifecycle_1.0.4
#> [22] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.7-1
#> [25] R6_2.5.1 fastmap_1.2.0 GenomeInfoDbData_1.2.13
#> [28] future_1.34.0 digest_0.6.37 colorspace_2.1-1
#> [31] AnnotationDbi_1.69.0 irlba_2.3.5.1 RSQLite_2.3.7
#> [34] labeling_0.4.3 filelock_1.0.3 cytolib_2.19.0
#> [37] fansi_1.0.6 yardstick_1.3.1 timechange_0.3.0
#> [40] httr_1.4.7 polyclip_1.10-7 abind_1.4-8
#> [43] compiler_4.5.0 bit64_4.5.2 withr_3.0.2
#> [46] doParallel_1.0.17 viridis_0.6.5 DBI_1.2.3
#> [49] highr_0.11 ggforce_0.4.2 MASS_7.3-61
#> [52] lava_1.8.0 embed_1.1.4 rappdirs_0.3.3
#> [55] DelayedArray_0.33.0 tools_4.5.0 future.apply_1.11.3
#> [58] nnet_7.3-19 glue_1.8.0 grid_4.5.0
#> [61] Rtsne_0.17 generics_0.1.3 recipes_1.1.0
#> [64] gtable_0.3.6 tzdb_0.4.0 class_7.3-22
#> [67] data.table_1.16.2 hms_1.1.3 tidygraph_1.3.1
#> [70] utf8_1.2.4 XVector_0.47.0 RcppAnnoy_0.0.22
#> [73] markdown_1.13 ggrepel_0.9.6 BiocVersion_3.21.1
#> [76] foreach_1.5.2 pillar_1.9.0 RcppHNSW_0.6.0
#> [79] splines_4.5.0 tweenr_2.0.3 lattice_0.22-6
#> [82] survival_3.7-0 bit_4.5.0 RProtoBufLib_2.19.0
#> [85] tidyselect_1.2.1 Biostrings_2.75.0 knitr_1.48
#> [88] gridExtra_2.3 xfun_0.48 graphlayouts_1.2.0
#> [91] hardhat_1.4.0 timeDate_4041.110 stringi_1.8.4
#> [94] UCSC.utils_1.3.0 yaml_2.3.10 evaluate_1.0.1
#> [97] codetools_0.2-20 ggraph_2.2.1 tibble_3.2.1
#> [100] BiocManager_1.30.25 cli_3.6.3 uwot_0.2.2
#> [103] rpart_4.1.23 munsell_0.5.1 jquerylib_0.1.4
#> [106] Rcpp_1.0.13 globals_0.16.3 png_0.1-8
#> [109] parallel_4.5.0 gower_1.0.1 readr_2.1.5
#> [112] blob_1.2.4 listenv_0.9.1 glmnet_4.1-8
#> [115] viridisLite_0.4.2 ipred_0.9-15 ggridges_0.5.6
#> [118] scales_1.3.0 prodlim_2024.06.25 purrr_1.0.2
#> [121] crayon_1.5.3 rlang_1.1.4 KEGGREST_1.47.0