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()
.
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")
#> Loading required namespace: Rtsne
# select only the tsne embedding columns
phenograph_tsne |>
select(contains("tsne")) |>
head()
#> # A tibble: 6 × 2
#> .tsne1 .tsne2
#> <dbl> <dbl>
#> 1 12.8 -9.45
#> 2 13.1 2.60
#> 3 33.7 14.7
#> 4 21.5 1.66
#> 5 19.5 -7.37
#> 6 22.9 10.1
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)
#> # A tibble: 3,000 × 2
#> .tsne1 .tsne2
#> <dbl> <dbl>
#> 1 17.0 -1.42
#> 2 9.66 -9.34
#> 3 14.4 -31.5
#> 4 16.3 -14.3
#> 5 18.9 -4.72
#> 6 12.5 -20.8
#> 7 8.36 -11.4
#> 8 11.4 -25.2
#> 9 13.0 -16.7
#> 10 8.00 -3.66
#> # ℹ 2,990 more rows
Changing the method
argument results in different
low-dimensional embeddings:
phenograph_data |>
tof_reduce_dimensions(method = "umap", augment = FALSE)
#> # A tibble: 3,000 × 2
#> .umap1 .umap2
#> <dbl> <dbl>
#> 1 -9.80 3.35
#> 2 -8.70 3.08
#> 3 -2.70 -0.213
#> 4 -2.56 -1.69
#> 5 -9.85 3.09
#> 6 -0.350 -2.54
#> 7 -9.78 2.48
#> 8 -2.16 -0.722
#> 9 -6.47 0.716
#> 10 -9.10 5.28
#> # ℹ 2,990 more rows
phenograph_data |>
tof_reduce_dimensions(method = "pca", augment = FALSE)
#> # A tibble: 3,000 × 5
#> .pc1 .pc2 .pc3 .pc4 .pc5
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -2.77 1.23 -0.868 0.978 3.49
#> 2 -0.969 -1.02 -0.787 1.22 0.329
#> 3 -2.36 2.54 -1.95 -0.882 -1.30
#> 4 -3.68 -0.00565 0.962 0.410 0.788
#> 5 -4.03 2.07 -0.829 1.59 5.39
#> 6 -2.59 -0.108 1.32 -1.41 -1.24
#> 7 -1.55 -0.651 -0.233 1.08 0.129
#> 8 -1.18 -0.446 0.134 -0.771 -0.932
#> 9 -2.00 -0.485 0.593 -0.0416 -0.658
#> 10 -0.0356 -0.924 -0.692 1.45 0.270
#> # ℹ 2,990 more rows
tof_reduce_*()
functionstof_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)
#> # A tibble: 3,000 × 2
#> .pc1 .pc2
#> <dbl> <dbl>
#> 1 -2.77 1.23
#> 2 -0.969 -1.02
#> 3 -2.36 2.54
#> 4 -3.68 -0.00565
#> 5 -4.03 2.07
#> 6 -2.59 -0.108
#> 7 -1.55 -0.651
#> 8 -1.18 -0.446
#> 9 -2.00 -0.485
#> 10 -0.0356 -0.924
#> # ℹ 2,990 more rows
# 3 principal components
phenograph_data |>
tof_reduce_pca(num_comp = 3)
#> # A tibble: 3,000 × 3
#> .pc1 .pc2 .pc3
#> <dbl> <dbl> <dbl>
#> 1 -2.77 1.23 -0.868
#> 2 -0.969 -1.02 -0.787
#> 3 -2.36 2.54 -1.95
#> 4 -3.68 -0.00565 0.962
#> 5 -4.03 2.07 -0.829
#> 6 -2.59 -0.108 1.32
#> 7 -1.55 -0.651 -0.233
#> 8 -1.18 -0.446 0.134
#> 9 -2.00 -0.485 0.593
#> 10 -0.0356 -0.924 -0.692
#> # ℹ 2,990 more rows
see ?tof_reduce_pca
, ?tof_reduce_umap
, and
?tof_reduce_tsne
for additional details.
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.
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