In keyes-timothy/tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)

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()

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)

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()

Session info

sessionInfo()

keyes-timothy/tidytof documentation built on May 7, 2024, 12:33 p.m.