# Clustering and metaclustering

## Timothy Keyes

### 2024-10-29

    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:

- FlowSOM
- k-means
- PhenoGraph
- Supervised distance-based clustering
- X-shift

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 4.4.1 (2024-06-14)
    #> Platform: x86_64-pc-linux-gnu
    #> Running under: Ubuntu 24.04.1 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.3.12.0
    #>
    #> 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)
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    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
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