multiplot_template.Rmd

{{plot_title}}

{{ env_id }} = readRDS("envs/{{ env_id }}.rds")
df <- data.frame({{ env_id }}$x, {{ env_id }}$y, {{ env_id }}$color_by)
df.melted <- reshape::melt(df, id=c(names({{ env_id }}$y), names({{ env_id }}$color_by)))

plotly::plotlyOutput("plot_{{ env_id }}")
output$plot_{{ env_id }} <- plotly::renderPlotly({

  if (!{{ env_id }}$y_selection){
    index_y <- match(names({{ env_id }}$y), names(df.melted))
  } else {
    index_y <- match(input$select_y_{{ env_id }}, names(df.melted))
  }

  if (!{{ env_id }}$color_selection){
    index_color <- match(names({{ env_id }}$color_by), names(df.melted))
  } else {
    index_color <- match(input$select_color_{{ env_id }}, names(df.melted))
  }
  
  g1 <- ggplot2::ggplot(df.melted, mapping = ggplot2::aes(y = value, x = df.melted[,index_y], color = df.melted[,index_color])) +
    ggplot2::geom_jitter() +
    ggplot2::geom_violin(mapping = ggplot2::aes(y = value), scale = "count") +
    ggplot2::facet_grid(. ~ variable, scales = "free_x") +
    ggplot2::theme_bw() +
    ggplot2::theme(panel.border = ggplot2::element_blank()) +
    ggplot2::scale_colour_viridis_c() +
    ggplot2::coord_flip() +
    ggplot2::labs( y="Type", x="", color=names(df.melted)[index_color])

  plotly::ggplotly(g1)
})

---

# selection field for y
if ({{ env_id }}$y_selection){
  selectInput("select_y_{{ env_id }}", label = "Select data for y axis:",
            choices = names({{ env_id }}$y))
}

# selection field for color_by
if ({{ env_id }}$color_selection){
  selectInput("select_color_{{ env_id }}", label = "Select experimental factor for coloring:",
            choices = names({{ env_id }}$color_by))
}

Plot description:

Sequencing is called saturated when generating more sequencing output from a cDNA library does not substantially increase the number of detected features in a sample. Since the number of detected features can act as a technical confounder, and thereby drive substructure in the data, it is advisable to aim for a saturated sequencing by either adding more sequencing output or decreasing the number of samples until saturation is achieved. [@zhang_one_2018] gives advise on how to choose the optimal cell number given a fixed sequencing budget