library(tidytof)
library(dplyr)
Often, clustering single-cell data to identify communities of cells with shared characteristics is a major goal of high-dimensional cytometry data analysis.
To do this, {tidytof}
provides the tof_cluster()
verb. Several clustering methods are implemented in {tidytof}
, including the following:
Each of these methods are wrapped by tof_cluster()
.
tof_cluster()
To demonstrate, we can apply the PhenoGraph clustering algorithm to {tidytof}
’s built-in phenograph_data
. Note that phenograph_data
contains 3000 total cells (1000 each from 3 clusters identified in the original PhenoGraph publication). For demonstration purposes, we also metacluster our PhenoGraph clusters using k-means clustering.
data(phenograph_data)
set.seed(203L)
phenograph_clusters <-
phenograph_data |>
tof_preprocess() |>
tof_cluster(
cluster_cols = starts_with("cd"),
num_neighbors = 50L,
distance_function = "cosine",
method = "phenograph"
) |>
tof_metacluster(
cluster_col = .phenograph_cluster,
metacluster_cols = starts_with("cd"),
num_metaclusters = 3L,
method = "kmeans"
)
phenograph_clusters |>
dplyr::select(sample_name, .phenograph_cluster, .kmeans_metacluster) |>
head()
#> # A tibble: 6 × 3
#> sample_name .phenograph_cluster .kmeans_metacluster
#> <chr> <chr> <chr>
#> 1 H1_PhenoGraph_cluster1 5 2
#> 2 H1_PhenoGraph_cluster1 1 2
#> 3 H1_PhenoGraph_cluster1 5 2
#> 4 H1_PhenoGraph_cluster1 1 2
#> 5 H1_PhenoGraph_cluster1 1 2
#> 6 H1_PhenoGraph_cluster1 5 2
The outputs of both tof_cluster()
and tof_metacluster()
are a tof_tbl
identical to the input tibble, but now with the addition of an additional column (in this case, “.phenograph_cluster” and “.kmeans_metacluster”) that encodes the cluster id for each cell in the input tof_tbl
. Note that all output columns added to a tibble or tof_tbl
by {tidytof}
begin with a full-stop (”.”) to reduce the likelihood of collisions with existing column names.
Because the output of tof_cluster
is a tof_tbl
, we can use dplyr
’s count
method to assess the accuracy of our clustering procedure compared to the original clustering from the PhenoGraph paper.
phenograph_clusters |>
dplyr::count(phenograph_cluster, .kmeans_metacluster, sort = TRUE)
#> # A tibble: 4 × 3
#> phenograph_cluster .kmeans_metacluster n
#> <chr> <chr> <int>
#> 1 cluster2 1 1000
#> 2 cluster3 3 1000
#> 3 cluster1 2 995
#> 4 cluster1 3 5
Here, we can see that our clustering procedure groups most cells from the same PhenoGraph cluster with one another (with a small number of mistakes).
To change which clustering algorithm tof_cluster
uses, alter the method
flag.
# use the kmeans algorithm
phenograph_data |>
tof_preprocess() |>
tof_cluster(
cluster_cols = contains("cd"),
method = "kmeans"
)
# use the flowsom algorithm
phenograph_data |>
tof_preprocess() |>
tof_cluster(
cluster_cols = contains("cd"),
method = "flowsom"
)
To change the columns used to compute the clusters, change the cluster_cols
flag. And finally, if you want to return a one-column tibble
that only includes the cluster labels (as opposed to the cluster labels added as a new column to the input tof_tbl
), set augment
to FALSE
.
# will result in a tibble with only 1 column (the cluster labels)
phenograph_data |>
tof_preprocess() |>
tof_cluster(
cluster_cols = contains("cd"),
method = "kmeans",
augment = FALSE
) |>
head()
#> # A tibble: 6 × 1
#> .kmeans_cluster
#> <chr>
#> 1 9
#> 2 9
#> 3 2
#> 4 19
#> 5 12
#> 6 19
sessionInfo()
#> R version 4.5.0 (2025-04-11 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows Server 2022 x64 (build 20348)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=C
#> [2] LC_CTYPE=English_United States.utf8
#> [3] LC_MONETARY=English_United States.utf8
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.utf8
#>
#> time zone: America/New_York
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] HDCytoData_1.29.0 flowCore_2.21.0
#> [3] SummarizedExperiment_1.39.0 Biobase_2.69.0
#> [5] GenomicRanges_1.61.0 GenomeInfoDb_1.45.4
#> [7] IRanges_2.43.0 S4Vectors_0.47.0
#> [9] MatrixGenerics_1.21.0 matrixStats_1.5.0
#> [11] ExperimentHub_2.99.5 AnnotationHub_3.99.5
#> [13] BiocFileCache_2.99.5 dbplyr_2.5.0
#> [15] BiocGenerics_0.55.0 generics_0.1.4
#> [17] forcats_1.0.0 ggplot2_3.5.2
#> [19] dplyr_1.1.4 tidytof_1.3.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_2.0.0 shape_1.4.6.1
#> [4] magrittr_2.0.3 farver_2.1.2 rmarkdown_2.29
#> [7] vctrs_0.6.5 memoise_2.0.1 sparsevctrs_0.3.4
#> [10] htmltools_0.5.8.1 S4Arrays_1.9.1 curl_6.2.3
#> [13] SparseArray_1.9.0 sass_0.4.10 parallelly_1.45.0
#> [16] bslib_0.9.0 httr2_1.1.2 lubridate_1.9.4
#> [19] cachem_1.1.0 igraph_2.1.4 lifecycle_1.0.4
#> [22] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.7-3
#> [25] R6_2.6.1 fastmap_1.2.0 future_1.49.0
#> [28] digest_0.6.37 AnnotationDbi_1.71.0 RSQLite_2.4.0
#> [31] labeling_0.4.3 filelock_1.0.3 cytolib_2.21.0
#> [34] yardstick_1.3.2 timechange_0.3.0 httr_1.4.7
#> [37] polyclip_1.10-7 abind_1.4-8 compiler_4.5.0
#> [40] bit64_4.6.0-1 withr_3.0.2 doParallel_1.0.17
#> [43] viridis_0.6.5 DBI_1.2.3 ggforce_0.4.2
#> [46] MASS_7.3-65 lava_1.8.1 rappdirs_0.3.3
#> [49] DelayedArray_0.35.1 tools_4.5.0 future.apply_1.11.3
#> [52] nnet_7.3-20 glue_1.8.0 grid_4.5.0
#> [55] recipes_1.3.1 gtable_0.3.6 tzdb_0.5.0
#> [58] class_7.3-23 tidyr_1.3.1 data.table_1.17.4
#> [61] hms_1.1.3 utf8_1.2.5 tidygraph_1.3.1
#> [64] XVector_0.49.0 ggrepel_0.9.6 BiocVersion_3.22.0
#> [67] foreach_1.5.2 pillar_1.10.2 stringr_1.5.1
#> [70] RcppHNSW_0.6.0 splines_4.5.0 tweenr_2.0.3
#> [73] lattice_0.22-7 survival_3.8-3 bit_4.6.0
#> [76] RProtoBufLib_2.21.0 tidyselect_1.2.1 Biostrings_2.77.1
#> [79] knitr_1.50 gridExtra_2.3 xfun_0.52
#> [82] graphlayouts_1.2.2 hardhat_1.4.1 timeDate_4041.110
#> [85] stringi_1.8.7 UCSC.utils_1.5.0 yaml_2.3.10
#> [88] evaluate_1.0.3 codetools_0.2-20 ggraph_2.2.1
#> [91] tibble_3.2.1 BiocManager_1.30.25 cli_3.6.5
#> [94] rpart_4.1.24 jquerylib_0.1.4 dichromat_2.0-0.1
#> [97] Rcpp_1.0.14 globals_0.18.0 png_0.1-8
#> [100] parallel_4.5.0 gower_1.0.2 readr_2.1.5
#> [103] blob_1.2.4 listenv_0.9.1 glmnet_4.1-9
#> [106] viridisLite_0.4.2 ipred_0.9-15 ggridges_0.5.6
#> [109] scales_1.4.0 prodlim_2025.04.28 purrr_1.0.4
#> [112] crayon_1.5.3 rlang_1.1.6 KEGGREST_1.49.0