Clustering and metaclustering

Timothy Keyes

2025-06-04

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().

Clustering with 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

Session info

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      
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#>  [97] Rcpp_1.0.14          globals_0.18.0       png_0.1-8           
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