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()
.
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 -13.6 -13.9
#> 2 -10.6 -2.56
#> 3 -33.8 1.23
#> 4 -20.0 -2.56
#> 5 -19.7 -11.2
#> 6 -18.7 6.54
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 17.6 3.97
#> 2 17.1 -1.67
#> 3 33.5 -14.1
#> 4 12.0 -13.3
#> 5 18.8 0.998
#> 6 23.3 -10.2
#> 7 18.4 -3.27
#> 8 27.7 -9.08
#> 9 21.9 -8.72
#> 10 11.2 2.28
#> # 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 -9.79 3.42
#> 2 -8.73 2.86
#> 3 -7.59 0.558
#> 4 -4.82 -3.49
#> 5 -9.83 3.13
#> 6 -0.0546 -4.37
#> 7 -9.86 2.76
#> 8 -7.62 -0.0948
#> 9 -5.15 0.851
#> 10 -8.81 5.09
#> # 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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