03-normalization-C-scnorm.Rmd

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

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

```{r scRNAseq-normalization-C-scnorm-checks, include = FALSE, echo = FALSE}
num_figure_rows <- 3
assertive.sets::assert_is_subset(local_params$features, colnames(SummarizedExperiment::colData(object_filtered)))
if (assertive.properties::is_empty(local_params$features)) {
  local_params$features <- c(NULL)
  num_figure_rows <- 2
}


if (assertive.properties::is_non_empty(local_params$batch)) {
  assertive.sets::assert_is_subset(local_params$batch, colnames(SummarizedExperiment::colData(object_filtered)))
  batch = droplevels(as.factor(SummarizedExperiment::colData(object_filtered)[, local_params$batch]))
  cellcount <- table(batch)
} else {
  batch = NULL
  cellcount <- dim(object_filtered)[2]
}

assertive.numbers::assert_all_are_greater_than(min(cellcount), 15)
```

### Normalization by SCnorm

```{r scRNAseq-normalization-C-scnorm-processing, include = FALSE, echo = FALSE}

scnorm <- SCnorm::SCnorm(object_filtered, Conditions = batch, reportSF = FALSE, NCores = parallel::detectCores() - 1)

assay(object_filtered, "norm_scnorm") <- assay(scnorm, "normcounts")
```

Global scaling factors, like *sizefactors*, are based on the assumption that the count-depth relationship is common across individual genes. When this relationship is not common across genes, normalization via global scale factors leads to overcorrection for weakly and moderately expressed genes and, in some cases, undernormalization of highly expressed genes. 
Here, we apply a method [@bacher_scnorm_2017] that tries to overcome this by using quantile regression to estimate the dependence of transcript expression on sequencing depth for every gene. Genes with similar dependence are then grouped, and a second quantile regression is used to estimate scale factors within each group. Within-group adjustment for sequencing depth is then performed using the estimated scale factors to provide normalized estimates of expression. Figure \@ref(fig:scRNAseq-normalization-C-scnorm-figure) depicts effects of the normalization strategy.

```{r scRNAseq-normalization-C-scnorm-figure-params, include = FALSE, echo = FALSE}
fig_height <- ReporteR.base::estimate_figure_height(
  height_in_panels = num_figure_rows,
  panel_height_in_in = params$formatting_defaults$figures$panel_height_in,
  axis_space_in_in = params$formatting_defaults$figures$axis_space_in,
  mpf_row_space = as.numeric(grid::convertUnit(grid::unit(5, 'mm'), 'in')),
  max_height_in_in = params$formatting_defaults$figures$max_height_in)
```

```{r scRNAseq-normalization-C-scnorm-figure, echo = FALSE, message=FALSE, warning=FALSE, fig.height = fig_height$global, fig.cap = paste("Results of normalization by SCnorm [@bacher_scnorm_2017].", caption_norm_pca, ifelse(num_figure_rows == 3, caption_norm_pca_extra, ""))}
figure_normalization_scnorm <- multipanelfigure::multi_panel_figure(height = fig_height$sub, columns = 3, rows = num_figure_rows, unit = "in")

# Based on raw counts
figure_normalization_scnorm <- multipanelfigure::fill_panel(figure_normalization_scnorm,
                                                                scater::plotPCASCE(object_filtered, ntop = 10, exprs_values = "logcounts", colour_by = local_params$features[1], add_ticks = FALSE) +
                                                                  theme_norm_pca)
figure_normalization_scnorm <- multipanelfigure::fill_panel(figure_normalization_scnorm,
                                                                scater::plotExplanatoryVariables(object_filtered, exprs_values = "logcounts", variables = local_params$features) +
                                                                  theme_norm_pca,
                                                                column = 2:3)

# Based on normalized values
figure_normalization_scnorm <- multipanelfigure::fill_panel(figure_normalization_scnorm,
                                                                scater::plotPCASCE(object_filtered, ntop = 10, exprs_values = "norm_scnorm", colour_by = local_params$features[1], add_ticks = FALSE) +
                                                                  ggplot2::guides(colour = FALSE) +
                                                                  theme_norm_pca)
figure_normalization_scnorm <- multipanelfigure::fill_panel(figure_normalization_scnorm,
                                                                scater::plotExplanatoryVariables(object_filtered, exprs_values = "norm_scnorm", variables = local_params$features) +
                                                                  theme_norm_pca,
                                                                column = 2:3)

# Additional panels for first three variables
if(num_figure_rows == 3) {
  for(i in 1:min(3, length(local_params$features))) {
    figure_normalization_scnorm <- multipanelfigure::fill_panel(figure_normalization_scnorm,
                                                                    scater::plotPCASCE(object_filtered, ntop = 10, exprs_values = "norm_scnorm", colour_by = local_params$features[i], add_ticks = FALSE) + 
                                                                      ggplot2::guides(colour = FALSE) +
                                                                      theme_norm_pca)
  }
}

figure_normalization_scnorm

```