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 6 2
#> 2 H1_PhenoGraph_cluster1 1 2
#> 3 H1_PhenoGraph_cluster1 6 2
#> 4 H1_PhenoGraph_cluster1 6 2
#> 5 H1_PhenoGraph_cluster1 6 2
#> 6 H1_PhenoGraph_cluster1 6 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 3 1000
#> 2 cluster3 1 1000
#> 3 cluster1 2 995
#> 4 cluster1 1 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 2
#> 2 1
#> 3 19
#> 4 9
#> 5 2
#> 6 9
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> 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] HDCytoData_1.25.0 flowCore_2.17.0
#> [3] SummarizedExperiment_1.35.1 Biobase_2.65.1
#> [5] GenomicRanges_1.57.1 GenomeInfoDb_1.41.1
#> [7] IRanges_2.39.2 S4Vectors_0.43.2
#> [9] MatrixGenerics_1.17.0 matrixStats_1.3.0
#> [11] ExperimentHub_2.13.1 AnnotationHub_3.13.3
#> [13] BiocFileCache_2.13.0 dbplyr_2.5.0
#> [15] BiocGenerics_0.51.1 forcats_1.0.0
#> [17] ggplot2_3.5.1 dplyr_1.1.4
#> [19] tidytof_0.99.8
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_1.8.8 shape_1.4.6.1 magrittr_2.0.3
#> [4] farver_2.1.2 rmarkdown_2.28 zlibbioc_1.51.1
#> [7] vctrs_0.6.5 memoise_2.0.1 htmltools_0.5.8.1
#> [10] S4Arrays_1.5.7 curl_5.2.2 SparseArray_1.5.31
#> [13] sass_0.4.9 parallelly_1.38.0 bslib_0.8.0
#> [16] lubridate_1.9.3 cachem_1.1.0 igraph_2.0.3
#> [19] mime_0.12 lifecycle_1.0.4 iterators_1.0.14
#> [22] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.5.1
#> [25] fastmap_1.2.0 GenomeInfoDbData_1.2.12 future_1.34.0
#> [28] digest_0.6.37 colorspace_2.1-1 AnnotationDbi_1.67.0
#> [31] RSQLite_2.3.7 labeling_0.4.3 filelock_1.0.3
#> [34] cytolib_2.17.0 fansi_1.0.6 yardstick_1.3.1
#> [37] timechange_0.3.0 httr_1.4.7 polyclip_1.10-7
#> [40] abind_1.4-5 compiler_4.4.1 bit64_4.0.5
#> [43] withr_3.0.1 doParallel_1.0.17 viridis_0.6.5
#> [46] DBI_1.2.3 highr_0.11 ggforce_0.4.2
#> [49] MASS_7.3-61 lava_1.8.0 rappdirs_0.3.3
#> [52] DelayedArray_0.31.11 tools_4.4.1 future.apply_1.11.2
#> [55] nnet_7.3-19 glue_1.7.0 grid_4.4.1
#> [58] generics_0.1.3 recipes_1.1.0 gtable_0.3.5
#> [61] tzdb_0.4.0 class_7.3-22 tidyr_1.3.1
#> [64] data.table_1.16.0 hms_1.1.3 tidygraph_1.3.1
#> [67] utf8_1.2.4 XVector_0.45.0 ggrepel_0.9.5
#> [70] BiocVersion_3.20.0 foreach_1.5.2 pillar_1.9.0
#> [73] stringr_1.5.1 RcppHNSW_0.6.0 splines_4.4.1
#> [76] tweenr_2.0.3 lattice_0.22-6 survival_3.7-0
#> [79] bit_4.0.5 RProtoBufLib_2.17.0 tidyselect_1.2.1
#> [82] Biostrings_2.73.1 knitr_1.48 gridExtra_2.3
#> [85] xfun_0.47 graphlayouts_1.1.1 hardhat_1.4.0
#> [88] timeDate_4032.109 stringi_1.8.4 UCSC.utils_1.1.0
#> [91] yaml_2.3.10 evaluate_0.24.0 codetools_0.2-20
#> [94] ggraph_2.2.1 tibble_3.2.1 BiocManager_1.30.25
#> [97] cli_3.6.3 rpart_4.1.23 munsell_0.5.1
#> [100] jquerylib_0.1.4 Rcpp_1.0.13 globals_0.16.3
#> [103] png_0.1-8 parallel_4.4.1 gower_1.0.1
#> [106] readr_2.1.5 blob_1.2.4 listenv_0.9.1
#> [109] glmnet_4.1-8 viridisLite_0.4.2 ipred_0.9-15
#> [112] ggridges_0.5.6 scales_1.3.0 prodlim_2024.06.25
#> [115] purrr_1.0.2 crayon_1.5.3 rlang_1.1.4
#> [118] KEGGREST_1.45.1