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11-scater.Rmd
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11-scater.Rmd
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---
output: html_document
---
## Bioconductor, `SingleCellExperiment` and `scater`
```{r, echo=FALSE}
library(knitr)
opts_chunk$set(fig.align = "center")
set.seed(1)
```
### Bioconductor
From [Wikipedia](https://en.wikipedia.org/wiki/Bioconductor):
[Bioconductor](https://www.bioconductor.org/) is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology. Bioconductor is based primarily on the statistical R programming language, but does contain contributions in other programming languages. It has two releases each year that follow the semiannual releases of R. At any one time there is a release version, which corresponds to the released version of R, and a development version, which corresponds to the development version of R. Most users will find the release version appropriate for their needs.
We strongly recommend all new comers and even experienced high-throughput data analysts to use well developed and maintained [Bioconductor methods and classes](https://www.bioconductor.org/developers/how-to/commonMethodsAndClasses/).
### `SingleCellExperiment` class
[`SingleCellExperiment`](http://bioconductor.org/packages/SingleCellExperiment) (SCE) is a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.
In practice, an object of this class can be created using its constructor:
```{r message=FALSE, warning=FALSE}
library(SingleCellExperiment)
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
rownames(counts) <- paste("gene", 1:10, sep = "")
colnames(counts) <- paste("cell", 1:10, sep = "")
sce <- SingleCellExperiment(
assays = list(counts = counts),
rowData = data.frame(gene_names = paste("gene_name", 1:10, sep = "")),
colData = data.frame(cell_names = paste("cell_name", 1:10, sep = ""))
)
sce
```
In the `SingleCellExperiment`, users can assign arbitrary names to entries of assays. To assist interoperability between packages, some suggestions for what the names should be for particular types of data are provided by the authors:
* __counts__: Raw count data, e.g., number of reads or transcripts for a particular gene.
* __normcounts__: Normalized values on the same scale as the original counts. For example, counts divided by cell-specific size factors that are centred at unity.
* __logcounts__: Log-transformed counts or count-like values. In most cases, this will be defined as log-transformed normcounts, e.g., using log base 2 and a pseudo-count of 1.
* __cpm__: Counts-per-million. This is the read count for each gene in each cell, divided by the library size of each cell in millions.
* __tpm__: Transcripts-per-million. This is the number of transcripts for each gene in each cell, divided by the total number of transcripts in that cell (in millions).
Each of these suggested names has an appropriate getter/setter method for convenient manipulation of the `SingleCellExperiment`. For example, we can take the (very specifically named) `counts` slot, normalise it and assign it to `normcounts` instead:
```{r}
normcounts(sce) <- log2(counts(sce) + 1)
sce
dim(normcounts(sce))
head(normcounts(sce))
```
### `scater` package
[`scater`](http://bioconductor.org/packages/scater/) is a R package for single-cell RNA-seq analysis [@McCarthy2017-kb]. The package contains several useful methods for quality control, visualisation and pre-processing of data prior to further downstream analysis.
`scater` features the following functionality:
* Automated computation of QC metrics
* Transcript quantification from read data with pseudo-alignment
* Data format standardisation
* Rich visualizations for exploratory analysis
* Seamless integration into the Bioconductor universe
* Simple normalisation methods
We highly recommend to use `scater` for all single-cell RNA-seq analyses and `scater` is the basis of the first part of the course.
As illustrated in the figure below, `scater` will help you with quality control, filtering and normalization of your expression matrix following mapping and alignment. <span style="color:red">Keep in mind that this figure represents the original version of `scater` where an `SCESet` class was used. In the newest version this figure is still correct, except that `SCESet` can be substituted with the `SingleCellExperiment` class.</span>
