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

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

library(tidytof)
library(dplyr)
library(ggplot2)

A useful tool for visualizing the phenotypic relationships between single cells and clusters of cells is dimensionality reduction, a form of unsupervised machine learning used to represent high-dimensional datasets in a smaller number of dimensions.

{tidytof} includes several dimensionality reduction algorithms commonly used by biologists: Principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP). To apply these to a dataset, use tof_reduce_dimensions().

Dimensionality reduction with tof_reduce_dimensions().

Here is an example call to tof_reduce_dimensions() in which we use tSNE to visualize data in {tidytof}'s built-in phenograph_data dataset.

data(phenograph_data)

# perform the dimensionality reduction
phenograph_tsne <-
    phenograph_data |>
    tof_preprocess() |>
    tof_reduce_dimensions(method = "tsne")

# select only the tsne embedding columns
phenograph_tsne |>
    select(contains("tsne")) |>
    head()

By default, tof_reduce_dimensions will add reduced-dimension feature embeddings to the input tof_tbl and return the augmented tof_tbl (that is, a tof_tbl with new columns for each embedding dimension) as its result. To return only the features embeddings themselves, set augment to FALSE (as in tof_cluster).

phenograph_data |>
    tof_preprocess() |>
    tof_reduce_dimensions(method = "tsne", augment = FALSE)

Changing the method argument results in different low-dimensional embeddings:

phenograph_data |>
    tof_reduce_dimensions(method = "umap", augment = FALSE)

phenograph_data |>
    tof_reduce_dimensions(method = "pca", augment = FALSE)

Method specifications for tof_reduce_*() functions

tof_reduce_dimensions() provides a high-level API for three lower-level functions: tof_reduce_pca(), tof_reduce_umap(), and tof_reduce_tsne(). The help files for each of these functions provide details about the algorithm-specific method specifications associated with each of these dimensionality reduction approaches. For example, tof_reduce_pca takes the num_comp argument to determine how many principal components should be returned:

# 2 principal components
phenograph_data |>
    tof_reduce_pca(num_comp = 2)

# 3 principal components
phenograph_data |>
    tof_reduce_pca(num_comp = 3)

see ?tof_reduce_pca, ?tof_reduce_umap, and ?tof_reduce_tsne for additional details.

Visualization using tof_plot_cells_embedding()

Regardless of the method used, reduced-dimension feature embeddings can be visualized using {ggplot2} (or any graphics package). {tidytof} also provides some helper functions for easily generating dimensionality reduction plots from a tof_tbl or tibble with columns representing embedding dimensions:

# plot the tsne embeddings using color to distinguish between clusters
phenograph_tsne |>
    tof_plot_cells_embedding(
        embedding_cols = contains(".tsne"),
        color_col = phenograph_cluster
    )

# plot the tsne embeddings using color to represent CD11b expression
phenograph_tsne |>
    tof_plot_cells_embedding(
        embedding_cols = contains(".tsne"),
        color_col = cd11b
    ) +
    ggplot2::scale_fill_viridis_c()

Such visualizations can be helpful in qualitatively describing the phenotypic differences between the clusters in a dataset. For example, in the example above, we can see that one of the clusters has high CD11b expression, whereas the others have lower CD11b expression.

Session info

sessionInfo()

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