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
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