dimensionality-reduction.Rmd

---
title: "Dimensionality reduction"
author: "Timothy Keyes"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
description: > 
  Read this vignette to learn how visualize single-cell phenotypes in 
  low-dimensional space using dimensionality reduction algorithms (PCA, UMAP, tSNE)
vignette: >
  %\VignetteIndexEntry{Dimensionality reduction}
  %\VignetteEngine{knitr::knitr}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
```

```{r setup, message = FALSE}
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.

```{r}
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`).

```{r}
phenograph_data |>
    tof_preprocess() |>
    tof_reduce_dimensions(method = "tsne", augment = FALSE)
```

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

```{r}
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: 

```{r}
# 2 principal components
phenograph_data |>
    tof_reduce_pca(num_comp = 2)
```

```{r}
# 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:

```{r}
# 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

```{r}
sessionInfo()
```