---
title: "PBMCs profiled with the Chromium Single Cell Multiome ATAC + Gene Expression from 10x"
date: "`r BiocStyle::doc_date()`"
vignette: |
  %\VignetteIndexEntry{scMultiome 10x PBMC}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
output:
    BiocStyle::html_document:
      toc_float: true
package: SingleCellMultiModal
---

# Installation

```{r,eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("SingleCellMultiModal")
```

## Load

```{r,include=TRUE, results="hide", message=FALSE, warning=FALSE}
library(SingleCellMultiModal)
library(MultiAssayExperiment)
library(scran)
library(scater)
```

# Description

This data set consists of about 10K Peripheral Blood Mononuclear Cells (PBMCs)
derived from a single healthy donor. It is available
[from the 10x Genomics website](https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets).

Provided are the RNA expression counts quantified at the gene level and the
chromatin accessibility levels quantified at the peak level. Here we provide
the default peaks called by the CellRanger software. If you want to explore
other peak definitions or chromatin accessibility quantifications (at the
promoter level, etc.), you have download the `fragments.tsv.gz` file from the
10x Genomics website.

# Downloading datasets

The user can see the available dataset by using the default options

```{r}
mae <- scMultiome("pbmc_10x", modes = "*", dry.run = FALSE, format = "MTX")
```

```{r, echo=FALSE}
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}
colors <- gg_color_hue(length(unique(mae$celltype)))
names(colors) <- unique(mae$celltype)
```

# Exploring the data structure

There are two assays: `rna` and `atac`, stored as
[SingleCellExperiment](http://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html)
objects

```{r}
mae
```

where the cells are the same in both assays:

```{r}
upsetSamples(mae)
```

## Cell metadata

Columns:

- **nCount_RNA**: number of read counts
- **nFeature_RNA**: number of genes with at least one read count
- **nCount_ATAC**: number of ATAC read counts
- **nFeature_ATAC**: number of ATAC peaks with at least one read count
- **celltype**: The cell types have been annotated by the 10x Genomics R&D team using gene markers. They provide a rough characterisation of the cell type diversity, but keep in mind that they are not ground truth labels.
- **broad_celltype**: `Lymphoid` or `Myeloid` origin

The cells have not been QC-ed, choosing a minimum number of genes/peaks per
cell depends is left to you! In addition, there are further quality control
criteria that you may want to apply, including mitochondrial coverage, fraction
of reads overlapping ENCODE Blacklisted regions, Transcription start site
enrichment, etc. See suggestions below for software that can perform a
semi-automated quality control pipeline

```{r}
head(colData(mae))
```

## RNA expression

The RNA expression consists of 36,549 genes and 10,032 cells, stored using
the `dgCMatrix` sparse matrix format

```{r}
dim(experiments(mae)[["rna"]])
```

```{r}
names(experiments(mae))
```

Let's do some standard dimensionality reduction plot:

```{r}
sce.rna <- experiments(mae)[["rna"]]

# Normalisation
sce.rna <- logNormCounts(sce.rna)

# Feature selection
decomp <- modelGeneVar(sce.rna)
hvgs <- rownames(decomp)[decomp$mean>0.01 & decomp$p.value <= 0.05]
sce.rna <- sce.rna[hvgs,]

# PCA
sce.rna <- runPCA(sce.rna, ncomponents = 25)

# UMAP
set.seed(42)
sce.rna <- runUMAP(sce.rna, dimred="PCA", n_neighbors = 25, min_dist = 0.3)
plotUMAP(sce.rna, colour_by="celltype", point_size=0.5, point_alpha=1)
```

## Chromatin Accessibility

The ATAC expression consists of 108,344 peaks and 10,032 cells:

```{r}
dim(experiments(mae)[["atac"]])
```

Let's do some standard dimensionality reduction plot. Note that scATAC-seq data is sparser than scRNA-seq, almost binary. The log normalisation + PCA approach that `scater` implements for scRNA-seq is not a good strategy for scATAC-seq data. Topic modelling or TFIDF+SVD are a better strategy. Please see the package recommendations below.

```{r}
sce.atac <- experiments(mae)[["atac"]]

# Normalisation
sce.atac <- logNormCounts(sce.atac)

# Feature selection
decomp <- modelGeneVar(sce.atac)
hvgs <- rownames(decomp)[decomp$mean>0.25]
sce.atac <- sce.atac[hvgs,]

# PCA
sce.atac <- runPCA(sce.atac, ncomponents = 25)

# UMAP
set.seed(42)
sce.atac <- runUMAP(sce.atac, dimred="PCA", n_neighbors = 25, min_dist = 0.3)
plotUMAP(sce.atac, colour_by="celltype", point_size=0.5, point_alpha=1)
```

# Suggested software for the downstream analysis

These are my personal recommendations of R-based analysis software:

-   **RNA expression**: [scater](http://bioconductor.org/packages/release/bioc/html/scater.html), [scran](https://bioconductor.org/packages/release/bioc/html/scran.html)
-   **ATAC accessibility**: [archR](https://www.archrproject.com/), [snapATAC](https://github.com/r3fang/SnapATAC), [cisTopic](https://github.com/aertslab/cisTopic), [Signac](https://satijalab.org/signac), [chromVar](https://bioconductor.org/packages/release/bioc/html/chromVAR.html), [Cicero](https://www.bioconductor.org/packages/release/bioc/html/cicero.html)
-   **Integrative analysis**: [MOFA+](https://biofam.github.io/MOFA2), [Seurat](https://satijalab.org/seurat). Note that both methods have released vignettes in their website where they analysed this same data set.

# sessionInfo

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