---
title: "Plotting single cell data with schex"
author: "Saskia Freytag"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{using_schex}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)
library(ggplot2)
theme_set(theme_classic())
```

Reduced dimension plotting is one of the essential tools for the analysis of 
single cell data. However, as the number of cells/nuclei in these these plots 
increases, the usefulness of these plots decreases. Many cells are plotted
on top of each other obscuring information, even when taking advantage of
transparency settings. This package provides binning strategies of cells/nuclei
into hexagon cells. Plotting summarized information of all cells/nuclei in their 
respective hexagon cells presents information without obstructions. The
package seemlessly works with the two most common object classes for the storage
of single cell data; `SingleCellExperiment` from the 
[SingleCellExperiment](https://bioconductor.org/packages/3.9/bioc/html/SingleCellExperiment.html) 
package and `Seurat` from the [Seurat](https://satijalab.org/seurat/) package.

## Load libraries

```{r setup, message=FALSE}
library(igraph)
library(schex)
library(TENxPBMCData)
library(scater)
library(scran)
library(ggrepel)
```

## Setup single cell data

In order to demonstrate the capabilities of the schex package, I will use the
a dataset of Peripheral Blood Mononuclear Cells (PBMC) freely available from 
10x Genomics. There are 2,700 single cells that were sequenced on the 
Illumina NextSeq 500. This data is handly availabe in the [`TENxPBMCData` package](http://bioconductor.org/packages/release/data/experiment/html/TENxPBMCData.html).

```{r load}
tenx_pbmc3k <- TENxPBMCData(dataset = "pbmc3k")

rownames(tenx_pbmc3k) <- uniquifyFeatureNames(rowData(tenx_pbmc3k)$ENSEMBL_ID, 
    rowData(tenx_pbmc3k)$Symbol_TENx)
```

In the  next few sections, I will perform some simple quality control steps
including filtering and normalization. I will then calculate various dimension 
reductions and cluster the data. These steps do by no means constitute
comprehensive handling of the data. For a more detailed guide the reader is 
referred to the following guides:

* [Luecken MD and Theis FJ; Current best practices in single‐cell RNA‐seq analysis:
  a tutorial; Molecular Systems Biology, 
  2019](https://www.embopress.org/doi/10.15252/msb.20188746)
* [Lun ATL, McCarthy DJ and Marioni JC; A step-by-step workflow for low-level 
  analysis of single-cell RNA-seq data with Bioconductor; F1000 Research, 2016](https://f1000research.com/articles/5-2122)

### Filtering

I filter cells with high mitochondrial content as well as cells with low
library size or feature count.

```{r filter-cells}
rowData(tenx_pbmc3k)$Mito <- grepl("^MT-", rownames(tenx_pbmc3k))
colData(tenx_pbmc3k) <- cbind(colData(tenx_pbmc3k), 
    perCellQCMetrics(tenx_pbmc3k, 
        subsets=list(Mt=rowData(tenx_pbmc3k)$Mito)))
rowData(tenx_pbmc3k) <- cbind(rowData(tenx_pbmc3k), 
    perFeatureQCMetrics(tenx_pbmc3k))

tenx_pbmc3k <- tenx_pbmc3k[, !colData(tenx_pbmc3k)$subsets_Mt_percent > 50]

libsize_drop <- isOutlier(tenx_pbmc3k$total,
      nmads = 3,type = "lower", log = TRUE)
feature_drop <- isOutlier(tenx_pbmc3k$detected,
    nmads = 3, type = "lower", log = TRUE)

tenx_pbmc3k <- tenx_pbmc3k[, !(libsize_drop | feature_drop)]
```

I filter any genes that have 0 count for all cells.

```{r filter-genes}
rm_ind <- calculateAverage(tenx_pbmc3k)<0
tenx_pbmc3k <- tenx_pbmc3k[!rm_ind,]
```

### Normalization

I normalize the data by using a simple library size normalization.

```{r norm, message=FALSE, warning=FALSE}
tenx_pbmc3k <- scater::logNormCounts(tenx_pbmc3k)
```

### Dimension reduction

I use both Principal Components Analysis (PCA) and Uniform Manifold 
Approximation and Projection (UMAP) in order to obtain reduced dimension 
representations of the data. Since there is a random component in the UMAP, I
will also set a seed.

```{r dim-red, message=FALSE, warning=FALSE}
tenx_pbmc3k <- runPCA(tenx_pbmc3k)
set.seed(10)
tenx_pbmc3k <- runUMAP(tenx_pbmc3k, dimred = "PCA", spread = 1, 
    min_dist = 0.4)
```

### Clustering

I will cluster the data on the PCA representation using Louvain clustering.

```{r cluster}
snn_gr <- buildSNNGraph(tenx_pbmc3k, use.dimred = "PCA", k = 50)
clusters <- cluster_louvain(snn_gr)
tenx_pbmc3k$cluster <- factor(clusters$membership)
```

## Plotting single cell data

At this stage in the workflow we usually would like to plot aspects of our data
in one of the reduced dimension representations. Instead of plotting this in an
ordinary fashion, I will demonstrate how schex can provide a better way of 
plotting this.

#### Calculate hexagon cell representation

First, I will calculate the hexagon cell representation for each cell for
a specified dimension reduction representation. I decide to use `nbins=40` which
specifies that I divide my x range into 40 bins. Note that this might be a 
parameter that you want to play around with depending on the number of cells/
nuclei in your dataset. Generally, for more cells/nuclei, `nbins` should be 
increased.

```{r calc-hexbin}
tenx_pbmc3k <- make_hexbin(tenx_pbmc3k, nbins = 40, 
    dimension_reduction = "UMAP", use_dims=c(1,2))
```

#### Plot number of cells/nuclei in each hexagon cell

First I plot how many cells are in each hexagon cell. This should be
relatively even, otherwise change the `nbins` parameter in the previous 
calculation.

```{r plot-density, fig.height=7, fig.width=7}
plot_hexbin_density(tenx_pbmc3k)
```

#### Plot meta data in hexagon cell representation

Next I colour the hexagon cells by some meta information, such as the majority 
of cells cluster membership and the median total count in each hexagon cell.

```{r plot-meta, fig.height=7, fig.width=7}
plot_hexbin_meta(tenx_pbmc3k, col="cluster", action="majority")
plot_hexbin_meta(tenx_pbmc3k, col="total", action="median")
```

While for plotting the cluster membership the outcome is not too different from
the classic plot, it is much easier to observe differences in the total count. 

```{r plot-meta-trad, fig.height=7, fig.width=7}
plotUMAP(tenx_pbmc3k, colour_by="cluster")
plotUMAP(tenx_pbmc3k, colour_by="total")
```

For convenience there is also a function that allows the calculation of label
positions for factor variables. These can be overlayed with the package 
`ggrepel`.

```{r plot-meta-label, message=FALSE, fig.height=7, fig.width=7}
label_df <- make_hexbin_label(tenx_pbmc3k, col="cluster")
pp <- plot_hexbin_meta(tenx_pbmc3k, col="cluster", action="majority") 
pp + ggrepel::geom_label_repel(data = label_df, aes(x=x, y=y, label = label), 
    colour="black",  label.size = NA, fill = NA)  
```

#### Plot gene expression in hexagon cell representation

Finally, I will visualize the gene expression of the POMGNT1 gene in the 
hexagon cell representation.

```{r plot-gene, fig.height=7, fig.width=7}
gene_id <-"POMGNT1"
plot_hexbin_feature(tenx_pbmc3k, type="logcounts", feature=gene_id, 
    action="mean", xlab="UMAP1", ylab="UMAP2", 
    title=paste0("Mean of ", gene_id))
```

Again it is much easier to observe differences in gene expression using the 
hexagon cell representation than the classic representation.

```{r plot-gene-trad, fig.height=7, fig.width=7}
plotUMAP(tenx_pbmc3k, by_exprs_values="logcounts", colour_by=gene_id)
```

We can overlay the gene expression data with the clusters for convenience.

```{r, message=FALSE, fig.height=7, fig.width=7}
plot_hexbin_feature_plus(tenx_pbmc3k,
    col="cluster", type="logcounts",
    feature="POMGNT1", action="mean")
```

### Understanding `schex` output as `ggplot` objects

The `schex` packages renders ordinary `ggplot` objects and thus these can be 
treated and manipulated using the 
[`ggplot` grammar](https://ggplot2.tidyverse.org/). For example the non-data
components of the plots can be changed using the function `theme`.

```{r}
gene_id <-"CD19"
gg <- schex::plot_hexbin_feature(tenx_pbmc3k, type="logcounts", feature=gene_id, 
    action="mean", xlab="UMAP1", ylab="UMAP2", 
    title=paste0("Mean of ", gene_id))
gg + theme_void()
```

The fact that `schex` renders `ggplot` objects can also be used to save these 
plots. Simply use `ggsave` in order to save any created plot.

```{r, eval=FALSE}
ggsave(gg, file="schex_plot.pdf")
```