02-quality-control-B-pca.Rmd

```{r parameters-and-defaults, include = FALSE}
module <- "scRNAseq"
section <- "quality_control"
```

```{r parameter-merge, include = FALSE}
local_params <- module %>%
  options() %>%
  magrittr::extract2(module) %>%
  magrittr::extract2(section) %>%
  ReporteR.base::validate_params(parameters_and_defaults)
```

```{r scRNAseq-quality-control-B-pca-checks}
assertive.sets::assert_is_subset(local_params$features, colnames(SummarizedExperiment::colData(object)))
```

```{r scRNAseq-quality-control-B-pca-processing, include = FALSE}
object %<>%
  scater::runPCA(method = local_params$qc_pca, selected_variables = local_params$features, use_coldata = T, ncomponents = 2)
```

### PCA-based quality map
Here we use **P**rincipal **C**omponent **A**nanalysis (PCA)[@ringner_2008] to visualize single cells in a *sample quality subspace*. 

``Principal component analysis (PCA) is a mathematical algorithm that reduces
the dimensionality of the data while retaining most of the variation in the
data set [...]. It accomplishes this reduction by identifying directions,
called principal components, along which the variation in the data is maximal.
By using a few components, each sample can be represented by relatively few
numbers instead of by values for thousands of variables. Samples can then be
plotted, making it possible to visually assess similarities and differences
between samples and determine whether samples can be grouped.'' [@ringner_2008]

In case of the *sample quality subspace*, samples are embedded in a high dimensional space described by the features `r ReporteR.base::itemize(local_params$features)`, and then reduced down to two principal components by PCA. Typically, PCA arranges samples that are associated with low quality features in close proximity and enables exclusion of those samples from downstream analysis (Figure \@ref(fig:scRNAseq-quality-control-B-pca-figure)).

```{r scRNAseq-quality-control-B-pca-figure, warning = FALSE, message = FALSE, echo = FALSE, fig.cap = "Biplot of components 1 (\'Dimension 1\') vs. 2 (\'Dimension 2\') from *principal component analysis* [@ringner_2008] of the sample quality subspace. Percentages in the axis labels indicate the fractional variance explained by the corresponding component."}
object %>%
  scater::plotReducedDim(use_dimred = "PCA_coldata", colour_by = local_params$features[1]) + 
  ggplot2::guides(colour = FALSE) +
  theme_qc_pca
```