In cgtc/RebelAnalysis: Luminex Analysis App

```{css, echo=FALSE}

.toggle { height: 2em; overflow-y: hidden; } .toggle.open { height: auto; }

```r

# Store passed-along data
standards <- params$all_data$standards
data <- params$all_data$data

# different sized plots in the same chunk
subchunkify <- function(g, fig_height=6, fig_width=8) {
  g_deparsed <- paste0(deparse(
    function() {g}
  ), collapse = '')

  sub_chunk <- paste0("`","``{r sub_chunk_", floor(runif(1) * 10000), ", fig.height=",
   fig_height, ", fig.width=", fig_width, ", echo=FALSE}",
  "\n(", 
    g_deparsed
    , ")()",
  "\n`","``
  ")

  cat(trimws(knitr::knit(text = knitr::knit_expand(text = sub_chunk), quiet = TRUE)))
}

Standards

The following plots provide a quick inspection on the proportional response of the standards across all cytokines examined. The summary boxplot is presented on a log y axis in order to observe this response across serial dilutions.

standards |>
  render_standards_graph() +
  ggplot2::scale_y_log10(labels = function(x) format(x, scientific = FALSE))

standards |>
  process_std() |>
  render_standards_cv_graph()

standards |>
  process_std() |>
  render_std_table(height = "300px")

shiny::incProgress()

Data

Samples Analysed

The following tables list the amount of replicates detected in the datafile.

fulldatapoints <- length(unique(data$Cytokine))

data |>
  dplyr::group_by(Name, Day, Dilution) |>
  dplyr::summarise(
    n = dplyr::n() / fulldatapoints
  ) |>
  dplyr::arrange(Name, Day, Dilution) |>
  dplyr::mutate(n = ifelse(n == round(n, 0), n, kableExtra::cell_spec(n, color = "black", background = "yellow", bold = TRUE))) |>
  magrittr::set_names(c("Sample Name", "Day Sampled", "Dilution Ratio", "Average Replicate Points")) |>
  kableExtra::kbl(digits = 2, format = "html", align = "c", caption = "Data Table", table.attr = "style=\'float: left\'") |>
  kableExtra::kable_styling(position = "center", bootstrap_options = c("striped", "hover", "condensed"), full_width = TRUE) |>
  kableExtra::scroll_box(height = "250px")

data |>
  dplyr::group_by(Cytokine) |>
  dplyr::summarise(
    n = dplyr::n()
  ) |>
  magrittr::set_names(c("Cytokine", "Datapoints")) |>
  kableExtra::kbl(digits = 2, format = "html", align = "c", caption = "Data Table", table.attr = "style=\'float: left\'") |>
  kableExtra::kable_styling(position = "center", bootstrap_options = c("striped", "hover", "condensed"), full_width = TRUE) |>
  kableExtra::scroll_box(height = "250px")

shiny::incProgress()

QC / Variations

These plots provide quick inspection into the variations of the technical replicates being analysed.

data |>
  render_var_graph() +
  ggplot2::ggtitle("Variations across samples/cytokines")

data |>
  calculate_var_table_data() |>
  render_var_table(height = "300px")

shiny::incProgress()

Associations / Correlations

Correlation Matrix

This plot investigates associations between different cytokine levels across all samples. This might be meaningless depending on how different the samples analysed are. Use the web app interface for better exploration of individual samples or timepoints.

data |>
  render_cor_graph()

Principal Component Analysis

Warning: These plots should be very carefully interpreted. Seek data sciency help if in doubt.

pca_data <- data |>
  calculate_pca_data()

shiny::incProgress()

pca_data |>
  render_pca_summary_graph()

pca_data |>
  render_pca_biplot_graph()

pca_data |>
  render_pca_scree_graph()

shiny::incProgress()

Individual Samples

The following groups of plots investigate further details between timepoints and within-sample cytokine comparisons.

sample_names <- unique(data$Name)

if (length(sample_names) >= 1) {

  for (i in sample_names) {
    cat(paste0("#### Analysis of Sample: ", i))

    f_data <- data |>
      dplyr::filter(Name == i)

    day_names <- as.numeric(as.character(unique(f_data$Day)))
    message("Total Days: ", length(day_names), " for Sample: ", i)

    p <- f_data |>
      render_samples_graph() +
      ggplot2::ggtitle(paste("Timeline overview for sample:", i)) +
      ggplot2::scale_y_log10(labels = function(x) format(x, scientific = FALSE))

    print(p)

    if (length(day_names) > 1) {

      cat("\n\n")

      q <- f_data |>
        ggplot2::ggplot(
          ggplot2::aes(x = Day, y = Value, color = Dilution)
        ) +
        ggplot2::geom_point(position = ggplot2::position_jitterdodge(.1, 0, .25), alpha = 1/3) +
        ggplot2::geom_boxplot(position = ggplot2::position_dodge2(.25)) +
        ggplot2::facet_wrap(. ~ Cytokine, ncol = 6) +
        ggplot2::theme_classic() +
        ggplot2::scale_color_viridis_d(end = .85, name = "Dilution:") +
        ggplot2::xlab("Day") +
        ggplot2::ylab("Value") +
        ggplot2::scale_y_log10(limits = c(1, NA), labels = function(x) format(x, scientific = FALSE)) +
        ggplot2::ggtitle(paste("Timepoint analysis for sample", i)) +
        ggplot2::theme(
          axis.text.x = ggplot2::element_text(face = "bold"),
          legend.position = "bottom"
        )

      subchunkify(q, 12, 8)

      cat("\n\n")

    } else {

      cat("\n\n ###### Report Warning [001]: Sample ", i, " does not have more than one timepoint. Did you label the data properly?")

    }

    shiny::incProgress(.3/length(sample_names))

  }
} else {
  cat("\n##### Report Error [001]: Names not parsed properly")
}

Cytokine Level Comparisons

This plot provides a quick insight into each cytokine's level for each sample side by side.

data |>
  ggplot2::ggplot(
    ggplot2::aes(x = Sample_day, y = Value, color = Dilution)
  ) +
    ggplot2::geom_point(position = ggplot2::position_jitterdodge(.1, 0, .25), alpha = 1/3) +
    ggplot2::geom_boxplot(position = ggplot2::position_dodge2(.25)) +
    ggplot2::facet_wrap(. ~ Cytokine, ncol = 6) +
    ggplot2::theme_classic() +
    ggplot2::scale_color_viridis_d(end = .85, name = "Dilution:") +
    ggplot2::ylab("Value") +
    ggplot2::scale_y_log10(limits = c(1, NA), labels = function(x) format(x, scientific = FALSE)) +
    ggplot2::ggtitle(paste("Cytokine level comparison across samples")) +
    ggplot2::theme(
      legend.position = "bottom",
      axis.title.x = ggplot2::element_blank(),
      axis.text.x = ggplot2::element_text(angle = 45, face = "bold", hjust = 1, vjust = 1)
    )

shiny::incProgress()