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 2The 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 5Here, 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.
sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 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=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] HDCytoData_1.32.0 flowCore_2.24.0
#> [3] SummarizedExperiment_1.42.0 Biobase_2.72.0
#> [5] GenomicRanges_1.64.0 Seqinfo_1.2.0
#> [7] IRanges_2.46.0 S4Vectors_0.50.1
#> [9] MatrixGenerics_1.24.0 matrixStats_1.5.0
#> [11] ExperimentHub_3.2.0 AnnotationHub_4.2.0
#> [13] BiocFileCache_3.2.0 dbplyr_2.5.2
#> [15] BiocGenerics_0.58.1 generics_0.1.4
#> [17] forcats_1.0.1 ggplot2_4.0.3
#> [19] dplyr_1.2.1 tidytof_1.6.0
#> [21] rmarkdown_2.31
#>
#> 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] htmltools_0.5.9 S4Arrays_1.12.0 curl_7.1.0
#> [13] SparseArray_1.12.2 sass_0.4.10 parallelly_1.47.0
#> [16] bslib_0.11.0 httr2_1.2.2 lubridate_1.9.5
#> [19] cachem_1.1.0 buildtools_1.0.0 igraph_2.3.1
#> [22] lifecycle_1.0.5 iterators_1.0.14 pkgconfig_2.0.3
#> [25] Matrix_1.7-5 R6_2.6.1 fastmap_1.2.0
#> [28] future_1.70.0 digest_0.6.39 AnnotationDbi_1.74.0
#> [31] RSQLite_3.53.1 labeling_0.4.3 filelock_1.0.3
#> [34] cytolib_2.24.0 yardstick_1.4.0 timechange_0.4.0
#> [37] httr_1.4.8 polyclip_1.10-7 abind_1.4-8
#> [40] compiler_4.6.0 bit64_4.8.2 withr_3.0.2
#> [43] doParallel_1.0.17 S7_0.2.2 viridis_0.6.5
#> [46] DBI_1.3.0 ggforce_0.5.0 MASS_7.3-65
#> [49] lava_1.9.1 rappdirs_0.3.4 DelayedArray_0.38.2
#> [52] tools_4.6.0 otel_0.2.0 future.apply_1.20.2
#> [55] nnet_7.3-20 glue_1.8.1 grid_4.6.0
#> [58] recipes_1.3.2 gtable_0.3.6 tzdb_0.5.0
#> [61] class_7.3-23 tidyr_1.3.2 data.table_1.18.4
#> [64] hms_1.1.4 utf8_1.2.6 tidygraph_1.3.1
#> [67] XVector_0.52.0 ggrepel_0.9.8 BiocVersion_3.23.1
#> [70] foreach_1.5.2 pillar_1.11.1 stringr_1.6.0
#> [73] RcppHNSW_0.7.0 splines_4.6.0 tweenr_2.0.3
#> [76] lattice_0.22-9 survival_3.8-6 bit_4.6.0
#> [79] RProtoBufLib_2.24.0 tidyselect_1.2.1 maketools_1.3.2
#> [82] Biostrings_2.80.1 knitr_1.51 gridExtra_2.3
#> [85] xfun_0.57 graphlayouts_1.2.3 hardhat_1.4.3
#> [88] timeDate_4052.112 stringi_1.8.7 yaml_2.3.12
#> [91] evaluate_1.0.5 codetools_0.2-20 ggraph_2.2.2
#> [94] tibble_3.3.1 BiocManager_1.30.27 cli_3.6.6
#> [97] rpart_4.1.27 jquerylib_0.1.4 Rcpp_1.1.1-1.1
#> [100] globals_0.19.1 png_0.1-9 parallel_4.6.0
#> [103] gower_1.0.2 readr_2.2.0 blob_1.3.0
#> [106] listenv_0.10.1 glmnet_5.0 viridisLite_0.4.3
#> [109] ipred_0.9-15 ggridges_0.5.7 scales_1.4.0
#> [112] prodlim_2026.03.11 crayon_1.5.3 purrr_1.2.2
#> [115] rlang_1.2.0 KEGGREST_1.52.0