01-overview.Rmd

# Overview

This is an overview of the transcriptomic data.

<!-- ## Experimental Design -->
```{r, results='asis'}
if(global_state$using_counts) {
  global_state$design %>%
    knitr::knit_child(
      text = c('',
               '```{r}',
               'make_table(.)',
               '```',
               ''),
      envir = environment(),
      quiet = TRUE
    ) %>%
    c("\n\n## Experimental Design\n\n", .) %>%
    cat(sep = "\n")
}
```

<!-- ## Sample PCA -->
```{r, results='asis'}
#Input: global state
pca_plot <- function(X) {
  plot_data = X$counts %>%
    column_to_rownames("Symbol") %>%
    t() %>%
    prcomp(center = T, scale. = T)
  
  plot = plot_data %>%
    fviz_pca_ind(
      repel = TRUE,
      habillage = X$design %>% dplyr::pull(Group),
      addEllipses = T) + ggtitle(NULL)
  
  plot
}

if (global_state$using_counts) {
  global_state %>%
    knitr::knit_child(
      text = c('',
               '```{r}',
               'pca_plot(.)',
               '```',
               ''),
      envir = environment(),
      quiet = TRUE) %>%
    c("\n\n## Sample PCA\n\n", .) %>%
    cat(sep = "\n")
}
```

<!-- ## Gene Expression Heatmap -->
```{r, results='asis'}
#Input: global_state
variable_genes_heatmap <- function(X, n = 500) {
  mat = X$counts %>%
    column_to_rownames(var = "Symbol") %>%
    as.matrix()
  
  indices = apply(mat, 1, var) %>%
    order(decreasing = T)
  
  mat = mat[indices[1:500], ]
  mat = t(scale(t(mat)))
  
  row_order = seriate(dist(mat), method = "TSP") %>% get_order()
  
  min = min(mat, na.rm = T)
  max = max(mat, na.rm = T)
  
  lgd = ComplexHeatmap::Legend(
    title = NULL,
    col_fun = circlize::colorRamp2(c(min, 0, max), c("blue", "white", "red")),
    at = c(min, 0, max),
    labels = c(round(min, 2), 0, round(max, 2)),
    direction = "vertical",
    labels_gp = grid::gpar(fontsize = 8)
  )
  
  group_data = X$design$Group %>% as.factor()
  
  group_cols = distinctColorPalette(k = nlevels(group_data)) %>% set_names(levels(group_data))
  
  annotation = ComplexHeatmap::HeatmapAnnotation(
    Group = group_data,
    show_annotation_name = F,
    col = list(Group = group_cols),
    annotation_legend_param = list(
      Group = list(
        color_bar = "discrete",
        title = NULL,
        direction = "horizontal",
        title_position = "topcenter",
        title_gp = grid::gpar(fontsize = 8),
        labels_gp = grid::gpar(fontsize = 8),
        nrow = 1
      )
    )
  )
  
  ht = ComplexHeatmap::Heatmap(
    mat,
    row_order = row_order,
    top_annotation = annotation,
    column_names_rot = 45,
    column_names_gp = grid::gpar(fontsize = 8),
    show_row_names = FALSE,
    show_row_dend = FALSE,
    show_heatmap_legend = FALSE
  )
  
  ComplexHeatmap::draw(
    ht,
    padding = unit(c(2, 15, 2, 2), "mm"),
    heatmap_legend_list = lgd,
    heatmap_legend_side = "right",
    annotation_legend_side = "top"
  )
  
}

if (global_state$using_counts) {
  global_state %>%
    knitr::knit_child(
      text = c('',
               '```{r}',
               'variable_genes_heatmap(.)',
               '```',
               ''),
      envir = environment(),
      quiet = TRUE
    ) %>%
    c("\n\n## Gene Expression Heatmap\n\n", .) %>%
    cat(sep = "\n")
}
```

## Comparisons

These are experimental comparisons analyzed in the report.

```{r}
make_table(tibble(Comparison = names(global_state$data)))
```