Interactive GSEA results: visualizations with reactable & plotly

As a Computational Biologist, I frequently analyze data from high throughput experiments, including transcriptomics, proteomics or metabolomics results. As a first step, I usually examine the behavior of individual analysis - genes, proteins or metabolites - and obtain a long list of effect sizes, p- or q-values.

Frequently, another layer of analysis focuses on the behavior of predefined gene sets, e.g. groups of genes whose up- or down-regulation reflects the activity of a biological process, a metabolic pathway or is indicative of a cellular state.

There are numerous methods to perform gene set enrichment (GSEA), over-representation (ORA) or pathway analysis, with more than 140 R packages on Bioconductor alone.

Note

How the gene sets are actually defined, e.g. whether they reflect the underlying biology of the system under study, is likely more critical than the exact choice of algorithm - but that’s a discussion for another day.)

Sharing analysis results

Regardless of the chosen statistical approach, GSEA or ORA analyses typically produce set-level statistics, e.g. a summary of the effect size across all members of a gene set alongside a statistic, p-value, etc.

To share results with my collaborators, I would like to enable them to

1. Browse set-level results to hone in on specific pathways / processes of interest.
2. Visualize the behavior of the analytes in the set.
3. Drill down to a subset of analytes and export e.g. gene-level results

The pioneering ReportingTools Bioconductor package creates static web pages for gene-set enrichment results, including gene- and set-level plots and statistics. But all of the plots are generated in advance, and interactivity is limited.

In this blog post, I take advantage of the reactable, plotly, crosstalk and htmlwidgets R packages to create a stand-alone interactive HTML report, allowing my collaborators to explore the results without the need for a server.

I learned a lot about these incredibly useful packages!

Note

At the time of writing, the current release of the reactable R package (v0.4.1) is not compatible with the latest release of the htmlwidgets (v1.6.0). This issue has already been fixed in reactable’s development version, which is available from github Alternatively, you can use the previous release of htmlwidgets (v1.5.4), e.g.  by installing it with remotes::install_version("htmlwidgets", version = "1.5.4").

Features

Here, I am combining several interactive elements, linked through SharedData objects via crosstalk:

• At the top, an interactive volcano plot showing the effects sizes (mean trimmed log2 fold changes) and nominal p-values for each tested gene set.
• Below, a nested reactable table displays the results for each set. When a row is expanded
  • It shows a volcano plot with gene-level results, as well as a linked table with the corresponding statistics.
  • The reader can hone in on specific genes by selecting points in the volcano plot, or by searching the table.

First, we define a set of helper functions are, which are composed into the main gene_set_report() function.

library(Biobase)
library(crosstalk)
library(dplyr)
library(htmltools)
library(plotly)
library(reactable)
library(sparrow)
library(stringr)
library(htmlwidgets)  
library(V8)  # to create static HTML

#' Retrieve gene-level statistics for a single gene set
#' 
#' @param stats named list of data.frames with gene-level statistics, one for
#' each gene set 
#' @gene_set_name Scalar character, the name of an element of `stats`.
#' in `data`.
#' @return A data.frame with results for a single gene set
.get_gene_data <- function(mg, gene_set_name, keep.cols = c(
  "symbol", "entrez_id", "logFC", "pval", "CI.L", "CI.R", "pval", "padj")) {
  sparrow::geneSet(mg, name = gene_set_name) %>%
    dplyr::select(tidyselect::any_of(keep.cols)) %>%
    dplyr::arrange(pval)
}

#' @importFrom htmltools tags
.entrez_url <- function(value) {
  if(!is.na(value) & nzchar(value)) {
    url <- sprintf("http://www.ncbi.nlm.nih.gov/gene/%s",
                   value)
    return(htmltools::tags$a(href = url, target = "_blank", 
                             as.character(value)))
  } else {
    return(value)
  }
}

#' @importFrom htmltools tags
.symbol_url <- function(value) {
  if(!is.na(value) & nzchar(value)) {
    url <- sprintf(
      "https://www.genenames.org/tools/search/#!/?query=%s",
      value)
    return(
      htmltools::tags$a(href = url, target = "_blank", as.character(value))
    )
  } else {
    return(value)
  }
}

#' @importFrom htmltools tags
.msigdb_url <- function(value) {
  if(!is.na(value) & nzchar(value)) {
    url <- sprintf(
      "https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/%s.html",
      value)
    return(
      htmltools::tags$a(href = url, target = "_blank", as.character(value))
    )
  } else {
    return(value)
  }
}

#' Create a reactable table with gene-level results
#' 
#' @param data A data.frame or a `SharedData` object.
#' @param defaultColDef A list that defines the default configuration for a 
#' column, typically the output of the [reactable::colDef] function.
#' @param columns A list of column definitions, each generated with the 
#' [reactable::colDef] function.
#' @param theme A `reactableTheme` object, typically generated with a call to
#' the [reactable::reactableTheme] function.
#' @param striped Scalar flag, display stripes?
#' @param bordered Scalar flag, display borders?
#' @param highlight Scalar flag, highlight selected rows?
#' @param searchable Scalar flag, add search box?
#' @param defaultPageSize Scalar integer, the default number of rows to display.
#' @param elementId Scalar character, an (optional) element identifier
#' @param ... Additional arguments for the [reactable::reactable] function.
#' @return A `reactable` object.
#' @export
#' @importFrom reactable colDef reactable colFormat
#' @examples
#' \dontrun{
#' df <- data.frame(
#'    symbol = c("TP53", "KRAS", "PIK3CA"),
#'    pval = runif(3, 0, 1),
#'    logFC = rnorm(3)
#' )
#' stats_table(df)
#' }
stats_table <- function(
    data, 
    defaultColDef = reactable::colDef(
      align = "center",
      minWidth = 100,
      sortNALast = TRUE
    ),
    columns = list(
      symbol = reactable::colDef(
        name = "Symbol",
        cell = .symbol_url
      ),
      entrezid = reactable::colDef(
        name = "EntrezId",
        cell = .entrez_url
      ),
      entrez_id = reactable::colDef(
        name = "EntrezId",
        cell = .entrez_url
      ),
      entrez = reactable::colDef(
        name = "EntrezId",
        cell = .entrez_url
      ),
      pval = reactable::colDef(
        name = "P-value",
        format = reactable::colFormat(digits = 4)),
      padj = reactable::colDef(
        name = "P-value",
        format = reactable::colFormat(digits = 4)),
      t = reactable::colDef(
        name = "t-statistic",
        format = reactable::colFormat(digits = 2)),
      B = reactable::colDef(
        name = "log-odds",
        format = reactable::colFormat(digits = 2)),
      AveExpr = reactable::colDef(
        name = "Mean expr",
        format = reactable::colFormat(digits = 2)),
      CI.L = reactable::colDef(
        name = "Lower 95% CI",
        format = reactable::colFormat(digits = 2)),
      CI.R = reactable::colDef(
        name = "Upper 95% CI",
        format = reactable::colFormat(digits = 2)),
      logFC = reactable::colDef(
        name = "logFC", 
        format = reactable::colFormat(digits = 2),
        style = function(value) {
          if (value > 0) {
            color <- "firebrick"
          } else if (value < 0) {
            color <- "navy"
          } else {
            color <- "lightgrey"
          }
          list(color = color, fontWeight = "bold")
        }
      )
    ),
    theme = reactable::reactableTheme(
      stripedColor = "#f6f8fa",
      highlightColor = "#f0f5f9",
      cellPadding = "8px 12px",
      style = list(
        fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, 
        Arial, sans-serif")
    ),
    striped = FALSE,
    bordered = FALSE,
    highlight = TRUE,
    searchable = TRUE,
    defaultPageSize = 10L,
    elementId = NULL,
    ...
) {
  reactable::reactable(
    data = data,
    searchable = searchable,
    striped = striped,
    bordered = bordered,
    highlight = highlight,
    selection = "multiple",
    onClick = "select",
    rowStyle = list(cursor = "pointer"),
    theme = theme,
    defaultPageSize = defaultPageSize,
    defaultColDef = defaultColDef,
    columns = columns,
    elementId = elementId,
    ...
  )
}

#' Wrap stats_table() output in a div html tag
#' 
#' @param style Scalar character, the style tag for the tag
#' @param elementId Scalar character, the element identifier
#' @param ... Arguments passed on to the `stats_table` function.
#' @return A `shiny.tag` object.
#' @importFrom htmltools div tags
.stats_table_div <- function(
    style = paste0(
      "width: 50%;",
      "float: right;",
      "padding-top: 1rem;"
    ),
    elementId = NULL,
    ...) {
  if (is.null(elementId)) {
    elementId <- basename(tempfile(pattern = "id"))
  }    
  htmltools::div(
    style = style,
    htmltools::tagList(
      stats_table(..., elementId = elementId),
      # download button
      htmltools::tags$button(
        "\u21E9 Download as CSV",
        onclick = sprintf("Reactable.downloadDataCSV('%s', 'gene-results.csv')",
                          elementId)
      )
    )    
  )
}

#' Create an interactive volcano plot
#' 
#' @param data A data.frame or a `SharedData` object.
#' @param x A `formula` defining the column of `data` mapped to the x-axis.
#' @param y A `formula` defining the column of `data` mapped to the y-axis.
#' @param text A `formula` defining the column of `data` mapped to the tooltip.
#' @param title Scalar character, the title of the plot.
#' @param xlab Scalar character, the title of the x-axis
#' @param ylab Scalar character, the title pf the y-axis
#' @param title.width Scalar integer, the target line width (passed on to the
#' [stringr::str_wrap] function.
#' @param opacity Scalar numeric between 0 and 1, the opacity of the points.
#' @param marker A list defining the size, line and color limits of the points.
#' @param colors Character vector of colors used to shade the points.
#' @param highlight.color Scalar character, the color used to highlight selected
#' points.
#' @param webGL Scalar flag, use webGL to render the plot?
#' @param width Scalar numeric or scalar character, width of the plot
#' @param height Scalar numeric or scalar character, height of the plot
#' @param ... Additional arguments passed to the [plotly::plot_ly] function.
#' @return A `plotly` object.
#' @importFrom plotly plot_ly add_trace config layout highlight toWebGL
#' @importFrom grDevices colorRampPalette
#' @export
#' @examples
#' \dontrun{
#' df <- data.frame(
#'    symbol = letters,
#'    pval = runif(length(letters), 0, 1),
#'    logFC = rnorm(length(letters))
#' )
#' volcano_plot(df)
#' }
volcano_plot <- function(
    data, 
    x = ~logFC,
    y = ~-log10(pval),
    text = ~symbol,
    title = "",
    xlab = "Fold change (log2)",
    ylab = "-log10(pval)",
    title.width = 35L,
    opacity = 0.5,
    marker = list(
      color = ~logFC,
      size = 10, 
      cmax = 3,
      cmid = 0,
      cmin = -3,
      line = list(color = "grey", width = 1)),
    colors = grDevices::colorRampPalette(
      c('navy', 'lightgrey', 'firebrick'))(15),
    highlight.color = "red",
    webGL = FALSE,
    width = NULL,
    height = NULL,
    ...) {
  p <- plotly::plot_ly(
    width = width,
    height = height
  ) %>% 
    plotly::add_trace(
      data = data, 
      name = "",
      type = 'scatter',
      mode = 'markers',
      x = x,
      y = y,
      text = text,
      hoverinfo ="text",
      opacity = opacity,
      colors = colors,
      marker = marker,
      ...
    ) %>%
    plotly::config(displaylogo = FALSE) %>%
    plotly::layout(
        xaxis = list(title = xlab),
        yaxis = list(title = ylab),
        title = stringr::str_wrap(
          stringr::str_replace_all(title, "_", " "),
          width = title.width)
    ) %>%
    plotly::highlight(
      color = highlight.color,
      on = "plotly_selected",
      off = "plotly_deselect"
    )
  if (isTRUE(webGL)) p <- plotly::toWebGL(p)
  return(p)
}

#' Create an interactive volcano plot for gene-set results
#' 
#' @param data A data.frame or a `SharedData` object.
#' @param x A `formula` defining the column of `data` mapped to the x-axis.
#' @param y A `formula` defining the column of `data` mapped to the y-axis.
#' @param text A `formula` defining the column of `data` mapped to the tooltip.
#' @param xlab Scalar character, the title of the x-axis
#' @param text.width Scalar integer, the target line width (passed on to the
#' [stringr::str_wrap] function.
#' @param hovertemplate Scalar character defining the tooltip template.
#' @param marker A list defining the size, line and color limits of the points.
#' @param width Scalar numeric or scalar character, width of the plot
#' @param height Scalar numeric or scalar character, height of the plot
#' @param ... Additional arguments passed to the [volcano_plot] function.
#' @return A `plotly` object.
#' @importFrom grDevices colorRampPalette
#' @importFrom stringr str_wrap str_replace_all
#' @export
#' @examples
#' \dontrun{
#' df <- data.frame(
#'    name = paste("Set", letters),
#'    pval = runif(length(letters), 0, 1),
#'    mean.logFC.trim = rnorm(length(letters)),
#'    n = sample(1:100, size = length(letters))
#' )
#' volcano_gene_set_plot(df)
#' }
volcano_gene_set_plot <- function(
    data,
    text = ~stringr::str_wrap(
          stringr::str_replace_all(name, "_", " "),
          width = text.width),
    text.width = 25,
    x = ~mean.logFC.trim,
    y = ~-log10(pval),
    marker = list(
      color = ~mean.logFC.trim,
      size = ~n, 
      sizemode = 'area', 
      cmax = 2,
      cmid = 0,
      cmin = -2,
      line = list(color = "grey", width = 1)
    ), 
    hovertemplate = paste(
            '<b>%{text}</b>',
            '<br><i>logFC</i>: %{x:.2f}',
            '<br><i>-log10(pval)</i>: %{y:.2f}',
            '<br><i>n</i>: %{marker.size}',
            '<br>'),
    xlab = "Fold change (log2)",
    width = NULL,
    height = NULL,
    ...)
{
  volcano_plot(
    data = data, 
    text = text, 
    x = x, 
    y = y,
    marker = marker, 
    xlab = xlab,
    hovertemplate = hovertemplate,
    width = width,
    height = height,
    ...)
}

#' Wrap volcano_plot() output in a div html tag
#' 
#' @param helptext Scalar character, text to display below the plot.
#' @param style Scalar character, the style tag for the tag
#' @param ... Arguments passed on to the `volcano_plot` function.
#' @return A `shiny.tag` object.
#' @importFrom htmltools div tagList p
.volcano_plot_div <- function(
    helptext = paste("Draw a rectangle / use the lasso tool to select points,",
                     "double-click to deselect all."), 
    style = paste0(
      "width: 50%;",
      "float: left;",
      "padding-right: 1rem;",
      "padding-top: 4rem;"
    ), 
    ...) {
  htmltools::div(
    style = style, {
      htmltools::tagList(
        volcano_plot(...),
        htmltools::p(helptext)
      )
    }
  )
}

#' Helper function to combine gene-level outputs into a single div
#' 
#' @param data A data.frame with gene-set results.
#' @param stats A named list of data.frames whose names much match the `name`
#' column of `data`.
#' @param index Scalar count, the row of `data` to plot.
#' @return A `shiny.tag` object containing the output of the 
#' `.volcano_plot_div()` and `.stats_table_div()` functions.
#' @importFrom crosstalk SharedData
#' @importfrom htmltools tagList div
.row_details <- function(data, mg, index) {
  gene_data <- .get_gene_data(mg = mg, gene_set_name = data$name[index])
  gd <- crosstalk::SharedData$new(gene_data)
  htmltools::div(
    htmltools::tagList(
      # volcano plot
      .volcano_plot_div(data = gd, title = data$name[index]),
      # interactive gene-stat table
      .stats_table_div(data = gd)
    )
  )
}

#' Create a nested gene set result table
#' 
#' @param mg A `SparrowResult` object
#' @param max.pval Scalar numeric, the largest (uncorrected) p-value for which
#' to return results.
#' @param max.results Scalar integer, the top number of rows to return
#' (ordered by p-value).
#' @param color.up Scalar character, the color for positive log2 fold changes.
#' @param color.down Scalar character, the color for negative log2 fold changes.
#' @param color.ns Scalar character, the color for zero log2 fold change.
#' @param theme A `reactableTheme` object, typically generated with a call to
#' the [reactable::reactableTheme] function.
#' @param defaultColDef A list that defines the default configuration for a 
#' column, typically the output of the [reactable::colDef] function.
#' @param columns A list of column definitions, each generated with the 
#' [reactable::colDef] function.
#' @param bordered Scalar flag, display borders?
#' @param highlight Scalar flag, highlight selected rows?
#' @param searchable Scalar flag, add search box?
#' @param striped Scalar flag, alternate row shading?
#' @param defaultPageSize Scalar integer, the default number of rows to display.
#' @param pageSizeOptions Integer vector that will populate the pagination menu.
#' @param paginationType Scalar character, the pagination control to use. Either
#' `numbers` for page number buttons (the default), `jump` for a page jump, or 
#' `simple` to show 'Previous' and 'Next' buttons only.
#' @param elementId Scalar character, an (optional) element identifier
#' @param defaultSorted Character vector of column names to sort by default. Or
#' to customize sort order, a named list with values of `asc` or `desc`.
#' @param name_url A function that returns a `shiny.tag` (usually an 
#' `<a href></a>` tag) for each element of the `name` column of `data` to link
#' to more information about the gene set (e.g. on the MSigDb website, etc).
#' @param ... Additional arguments for the [reactable::reactable] function.
#' @importFrom reactable reactable reactableTheme colDef colFormat
#' @return A `reactable` object with one row for each row in `data`, each of
#' which can be expanded into the output of the `.row_details()` function
#' for that specific gene set.
#' @export
#' @examples
#' \dontrun{
#' library(sparrow)
#' vm <- sparrow::exampleExpressionSet()
#' gdb <- sparrow::exampleGeneSetDb()
#' mg <- sparrow::seas(vm, gdb, c("camera"), design = vm$design, 
#'                     contrast = 'tumor')
#' gene_set_table(mg, max.results = 10)
#' }
gene_set_table <- function(
    mg,
    max.pval = 0.05,
    max.results = Inf,
    keep.cols = c("collection", "name", "n", "pval", "padj", 
                      "mean.logFC.trim"),
    method = resultNames(mg)[1],
    color.up = "firebrick", 
    color.down = "navy",
    color.ns = "grey50",
    theme = reactable::reactableTheme(
      stripedColor = "grey95",
      highlightColor = "grey80",
      cellPadding = "8px 12px",
      style = list(
        fontFamily = "-apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, 
        Arial, sans-serif")
    ),
    defaultColDef = reactable::colDef(
      header = function(value) value,
      align = "center",
      minWidth = 100,
      headerStyle = list(background = "#f7f7f8"),
      sortNALast = TRUE
    ),
    name_url = function(value) {value},
    columns = list(
      collection = reactable::colDef(
        name = "Collection"),
      name = reactable::colDef(
        name = "Gene set",
        cell = name_url,
        minWidth = 150),
      pval = reactable::colDef(
        name = "P-value", aggregate = "min",
        format = reactable::colFormat(digits = 4)),
      padj = reactable::colDef(
        name = "FDR", aggregate = "min",
        format = reactable::colFormat(digits = 4)),
      Direction =  reactable::colDef(
        name = "dir", minWidth = 45, 
        cell = function(value) {
          if (value == "Up")  "\u2B06" else "\u2B07"
        }),
      logFC = reactable::colDef(
        name = "logFC", format = reactable::colFormat(digits = 2),
        style = function(value) {
          if (value > 0) {
            color <- color.up
          } else if (value < 0) {
            color <- color.down
          } else {
            color <- color.ns
          }
          list(color = color, fontWeight = "bold")
        }
      ),
      mean.logFC.trim = reactable::colDef(
        name = "logFC", format = reactable::colFormat(digits = 2),
        style = function(value) {
          if (value > 0) {
            color <- color.up
          } else if (value < 0) {
            color <- color.down
          } else {
            color <- color.ns
          }
          list(color = color, fontWeight = "bold")
        }
      )
    ),
    elementId = "expansion-table",
    static = TRUE,
    filterable = TRUE,
    searchable = TRUE,
    bordered = TRUE,
    striped = FALSE,
    highlight = TRUE,
    defaultPageSize = 25L,
    showPageSizeOptions = TRUE,
    pageSizeOptions = sort(unique(c(25, 50, 100, nrow(data)))),
    paginationType = "simple",
    defaultSorted = list(pval = "asc")
) {
  data = sparrow::result(mg, method) %>%
    dplyr::slice_min(n = max.results, order_by = pval) %>%
    dplyr::filter(pval <= max.pval) %>%
    dplyr::select(tidyselect::any_of(keep.cols))
  if (nrow(data) == 0) {
    warning("None of the gene sets pass the `max.pval` threshold.")
    return(NULL)
  }
  reactable::reactable(
    data,
    elementId = elementId,
    defaultColDef = defaultColDef,
    static = static,
    filterable = filterable,
    searchable = searchable,
    bordered = bordered,
    highlight = highlight,
    theme = theme,
    defaultPageSize = defaultPageSize,
    showPageSizeOptions = showPageSizeOptions,
    pageSizeOptions = pageSizeOptions,
    paginationType = paginationType,
    defaultSorted = defaultSorted,
    columns = columns,
    details = function(index) {
      .row_details(data = data, mg = mg, index)
    }
  )
}

#' Wrapper to create a div HTML tag
#' @param mg A `SparrowResult` object
#' @param method Scalar character, which results to return from `mg`.
#' @param max.pal Scalar numeric, return only results wiht an (uncorrected)
#' <= `max.pal`.
#' @param verbose Scalar flag, show messages?
#' @param title Scalar character, the `h1` title for the element
#' @param elementId Scalar character, the element identifier for the interactive
#' table.
#' @param style Scalar character, the style tag for the tag
#' @param ... Additional arguments passed on to the `gene_set_table` function.
#' @return A `shiny.tag` object containing the output of the 
#' `gene_set_table()` function.
#' @importFrom htmltools div h1 tagList tags
#' @export
gene_set_report <- function(
    mg,
    method = resultNames(mg)[1], 
    max.pval = 0.05,
    max.results = Inf,
    verbose = TRUE,
    title = "Gene set enrichment analysis",
    elementId = "expansion-table",
    style = "",
    ...
) {
  if (!is.finite(max.results)) {
    message.log <- sprintf(
      paste("Reporting all '%s' results with (uncorrected)",
            "p-value <= %s"), 
      method, max.pval)
  } else {
    message.log <- sprintf(
      paste("Reporting up to %s '%s' results with (uncorrected)",
            "p-value <= %s"), 
      max.results, method, max.pval)
  }
  if (isTRUE(verbose)) {
    message(message.log)
  }
  htmltools::div(
    style = style, 
    {
      htmltools::tagList(
        htmltools::h1(title),
        htmltools::p(message.log),
        # volcano plot
        sparrow::result(mg, method) %>%
          dplyr::slice_min(n = max.results, order_by = pval) %>%
          volcano_gene_set_plot(width = "50%"),
        htmltools::br(),
        # expansion button
        htmltools::tags$button(
          "Expand/collapse all rows",
          onclick = sprintf("Reactable.toggleAllRowsExpanded('%s')", elementId)
        ),
        gene_set_table(mg = mg, max.pval = max.pval, max.results = max.results,
                       ...)
      )
    })
}

Next, we perform a gene set enrichment analysis with sparrow’s seas function. It returns a convenient SparrowResult S4 object with both gene-set statistics and gene-level differential expression results.

# gene set enrichment analysis with the sparrow Bioconductor package
vm <- exampleExpressionSet()
gdb <- exampleGeneSetDb()
mg <- seas(vm, gdb, methods = "camera", design = vm$design, contrast = 'tumor')

Finally, we pass the mg object to our gene_set_report() function, together with arguments requesting that all gene-set results passing a p-value threshold of < 0.05 are included. We also pass the .msigdb_url helper function to the name_url argument, to link the name of each gene set to its description on the msigdb website.

# create an interactive report
htmltools::browsable(
  gene_set_report(mg, method = "camera", max.pval = 0.05, max.results = Inf, 
                  name_url = .msigdb_url)
)

Reporting all 'camera' results with (uncorrected) p-value <= 0.05

Gene set enrichment analysis

Reporting all 'camera' results with (uncorrected) p-value <= 0.05

Collection

Gene set

n

P-value

FDR

logFC

​

c2

SOTIRIOU_BREAST_CANCER_GRADE_1_VS_3_UP

149

0.0000

0.0000

1.83

​

c2

PUJANA_BREAST_CANCER_WITH_BRCA1_MUTATED_UP

55

0.0000

0.0000

0.95

​

c2

LOPEZ_MESOTELIOMA_SURVIVAL_TIME_UP

14

0.0000

0.0000

1.92

​

c2

TURASHVILI_BREAST_LOBULAR_CARCINOMA_VS_DUCTAL_NORMAL_DN

91

0.0000

0.0001

-0.94

​

c2

REACTOME_MRNA_SPLICING_MINOR_PATHWAY

42

0.0000

0.0001

0.34

​

c2

BOYAULT_LIVER_CANCER_SUBCLASS_G123_DN

41

0.0000

0.0005

-0.65

​

c7

GSE3982_BCELL_VS_TH2_DN

175

0.0003

0.0031

0.33

​

c7

GSE22886_IGG_IGA_MEMORY_BCELL_VS_BLOOD_PLASMA_CELL_UP

176

0.0013

0.0106

-0.29

​

c2

MOREAUX_MULTIPLE_MYELOMA_BY_TACI_DN

170

0.0013

0.0106

0.17

​

c2

BURTON_ADIPOGENESIS_PEAK_AT_2HR

50

0.0015

0.0106

-0.74

​

c2

CHARAFE_BREAST_CANCER_BASAL_VS_MESENCHYMAL_DN

45

0.0015

0.0106

-0.47

​

c2

SCHAEFFER_PROSTATE_DEVELOPMENT_48HR_DN

356

0.0017

0.0109

-0.33

​

c2

HUPER_BREAST_BASAL_VS_LUMINAL_UP

52

0.0022

0.0126

-0.76

​

c7

GSE13485_CTRL_VS_DAY3_YF17D_VACCINE_PBMC_DN

191

0.0025

0.0137

0.27

​

c2

GUTIERREZ_WALDENSTROEMS_MACROGLOBULINEMIA_1_UP

9

0.0073

0.0368

-0.60

​

c6

KRAS.50_UP.V1_UP

33

0.0133

0.0632

-0.67

​

c2

YAMASHITA_LIVER_CANCER_STEM_CELL_DN

47

0.0152

0.0662

-0.44

​

c6

ATM_DN.V1_UP

89

0.0157

0.0662

-0.41

​

c6

KRAS.LUNG_UP.V1_DN

98

0.0227

0.0823

-0.43

​

c2

BIOCARTA_AGPCR_PATHWAY

11

0.0227

0.0823

-0.50

​

c6

KRAS.LUNG.BREAST_UP.V1_DN

96

0.0227

0.0823

-0.34

​

c6

KRAS.BREAST_UP.V1_DN

90

0.0249

0.0848

-0.31

​

c6

KRAS.600.LUNG.BREAST_UP.V1_DN

189

0.0256

0.0848

-0.26

​

c6

TBK1.DN.48HRS_DN

50

0.0296

0.0888

0.17

​

c6

CAMP_UP.V1_DN

183

0.0306

0.0888

-0.23

1–25 of 31 rows

Show 253150100

1 of 2

Details

Gene set enrichment analysis

In this example, I am using Steve Lianoglou’s sparrow Bioconductor package to perform gene set enrichment analysis. But any other method could be used, as long as both set-level and gene-level differential expression statistics can be obtained.

sparrow supports numerous GSEA and ORA methods. Here, I am using the camera algorithm from the limma Bioconductor package for illustration.

Reproducibility

SessionInfo

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.1 (2023-06-16)
 os       macOS Ventura 13.5.1
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/Los_Angeles
 date     2023-08-30
 pandoc   3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 ! package          * version    date (UTC) lib source
 P annotate           1.78.0     2023-05-08 [?] Bioconductor
 P AnnotationDbi      1.62.2     2023-07-02 [?] Bioconductor
 P askpass            1.1        2019-01-13 [?] CRAN (R 4.3.0)
 P babelgene          22.9       2022-09-29 [?] CRAN (R 4.3.0)
 P backports          1.4.1      2021-12-13 [?] CRAN (R 4.3.0)
 P Biobase          * 2.60.0     2023-05-08 [?] Bioconductor
 P BiocGenerics     * 0.46.0     2023-06-04 [?] Bioconductor
 P BiocIO             1.10.0     2023-05-08 [?] Bioconductor
 P BiocManager        1.30.22    2023-08-08 [?] CRAN (R 4.3.0)
 P BiocParallel       1.34.2     2023-05-28 [?] Bioconductor
 P BiocSet            1.14.0     2023-05-08 [?] Bioconductor
 P Biostrings         2.68.1     2023-05-21 [?] Bioconductor
 P bit                4.0.5      2022-11-15 [?] CRAN (R 4.3.0)
 P bit64              4.0.5      2020-08-30 [?] CRAN (R 4.3.0)
 P bitops             1.0-7      2021-04-24 [?] CRAN (R 4.3.0)
 P blob               1.2.4      2023-03-17 [?] CRAN (R 4.3.0)
 P cachem             1.0.8      2023-05-01 [?] CRAN (R 4.3.0)
 P checkmate          2.2.0      2023-04-27 [?] CRAN (R 4.3.0)
 P circlize           0.4.15     2022-05-10 [?] CRAN (R 4.3.0)
 P cli                3.6.1      2023-03-23 [?] CRAN (R 4.3.0)
 P clue               0.3-64     2023-01-31 [?] CRAN (R 4.3.0)
 P cluster            2.1.4      2022-08-22 [?] CRAN (R 4.3.0)
 P codetools          0.2-19     2023-02-01 [?] CRAN (R 4.3.1)
 P colorspace         2.1-0      2023-01-23 [?] CRAN (R 4.3.0)
 P ComplexHeatmap     2.16.0     2023-05-08 [?] Bioconductor
 P crayon             1.5.2      2022-09-29 [?] CRAN (R 4.3.0)
 R credentials        1.3.2      <NA>       [?] <NA>
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 P curl               5.0.2      2023-08-14 [?] CRAN (R 4.3.0)
 P data.table         1.14.8     2023-02-17 [?] CRAN (R 4.3.0)
 P DBI                1.1.3      2022-06-18 [?] CRAN (R 4.3.0)
 P digest             0.6.33     2023-07-07 [?] CRAN (R 4.3.0)
 P doParallel         1.0.17     2022-02-07 [?] CRAN (R 4.3.0)
 P dplyr            * 1.1.2      2023-04-20 [?] CRAN (R 4.3.0)
 P edgeR              3.42.4     2023-06-04 [?] Bioconductor
 P ellipsis           0.3.2      2021-04-29 [?] CRAN (R 4.3.0)
 P evaluate           0.21       2023-05-05 [?] CRAN (R 4.3.0)
 P fansi              1.0.4      2023-01-22 [?] CRAN (R 4.3.0)
 P fastmap            1.1.1      2023-02-24 [?] CRAN (R 4.3.0)
 P foreach            1.5.2      2022-02-02 [?] CRAN (R 4.3.0)
 P generics           0.1.3      2022-07-05 [?] CRAN (R 4.3.0)
 P GenomeInfoDb       1.36.1     2023-07-02 [?] Bioconductor
 P GenomeInfoDbData   1.2.10     2023-08-23 [?] Bioconductor
 P GetoptLong         1.0.5      2020-12-15 [?] CRAN (R 4.3.0)
 P ggplot2          * 3.4.3      2023-08-14 [?] CRAN (R 4.3.0)
 P GlobalOptions      0.1.2      2020-06-10 [?] CRAN (R 4.3.0)
 P glue               1.6.2      2022-02-24 [?] CRAN (R 4.3.0)
 P graph              1.78.0     2023-05-08 [?] Bioconductor
 P gridExtra          2.3        2017-09-09 [?] CRAN (R 4.3.0)
 P GSEABase           1.62.0     2023-05-08 [?] Bioconductor
 P gtable             0.3.4      2023-08-21 [?] CRAN (R 4.3.1)
 P htmltools        * 0.5.6      2023-08-10 [?] CRAN (R 4.3.0)
 P htmlwidgets      * 1.6.2      2023-03-17 [?] CRAN (R 4.3.0)
 P httpuv             1.6.11     2023-05-11 [?] CRAN (R 4.3.0)
 P httr               1.4.7      2023-08-15 [?] CRAN (R 4.3.0)
 P IRanges            2.34.1     2023-07-02 [?] Bioconductor
 P irlba              2.3.5.1    2022-10-03 [?] CRAN (R 4.3.0)
 P iterators          1.0.14     2022-02-05 [?] CRAN (R 4.3.0)
 P jsonlite           1.8.7      2023-06-29 [?] CRAN (R 4.3.0)
 P KEGGREST           1.40.0     2023-05-08 [?] Bioconductor
 P knitr              1.43       2023-05-25 [?] CRAN (R 4.3.0)
 P later              1.3.1      2023-05-02 [?] CRAN (R 4.3.0)
 P lattice            0.21-8     2023-04-05 [?] CRAN (R 4.3.1)
 P lazyeval           0.2.2      2019-03-15 [?] CRAN (R 4.3.0)
 P lifecycle          1.0.3      2022-10-07 [?] CRAN (R 4.3.0)
 P limma              3.56.2     2023-06-04 [?] Bioconductor
 P locfit             1.5-9.8    2023-06-11 [?] CRAN (R 4.3.0)
 P magrittr           2.0.3      2022-03-30 [?] CRAN (R 4.3.0)
 P Matrix             1.5-4.1    2023-05-18 [?] CRAN (R 4.3.1)
 P matrixStats        1.0.0      2023-06-02 [?] CRAN (R 4.3.0)
 P memoise            2.0.1      2021-11-26 [?] CRAN (R 4.3.0)
 P mime               0.12       2021-09-28 [?] CRAN (R 4.3.0)
 P munsell            0.5.0      2018-06-12 [?] CRAN (R 4.3.0)
 P ontologyIndex      2.11       2023-05-30 [?] CRAN (R 4.3.0)
 P openssl            2.1.0      2023-07-15 [?] CRAN (R 4.3.0)
 P pillar             1.9.0      2023-03-22 [?] CRAN (R 4.3.0)
 P pkgconfig          2.0.3      2019-09-22 [?] CRAN (R 4.3.0)
 P plotly           * 4.10.2     2023-06-03 [?] CRAN (R 4.3.0)
 P plyr               1.8.8      2022-11-11 [?] CRAN (R 4.3.0)
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 P Rcpp               1.0.11     2023-07-06 [?] CRAN (R 4.3.0)
 P RCurl              1.98-1.12  2023-03-27 [?] CRAN (R 4.3.0)
 P reactable        * 0.4.4.9000 2023-08-25 [?] Github (glin/reactable@86bd276)
 P reactR             0.4.4      2021-02-22 [?] CRAN (R 4.3.0)
   renv               1.0.2      2023-08-15 [1] CRAN (R 4.3.0)
 P rjson              0.2.21     2022-01-09 [?] CRAN (R 4.3.0)
 P rlang              1.1.1      2023-04-28 [?] CRAN (R 4.3.0)
 P rmarkdown          2.24       2023-08-14 [?] CRAN (R 4.3.0)
 P RSQLite            2.3.1      2023-04-03 [?] CRAN (R 4.3.0)
 P rstudioapi         0.15.0     2023-07-07 [?] CRAN (R 4.3.0)
 P S4Vectors          0.38.1     2023-05-08 [?] Bioconductor
 P scales             1.2.1      2022-08-20 [?] CRAN (R 4.3.0)
 P sessioninfo        1.2.2      2021-12-06 [?] CRAN (R 4.3.0)
 P shape              1.4.6      2021-05-19 [?] CRAN (R 4.3.0)
 P shiny              1.7.5      2023-08-12 [?] CRAN (R 4.3.0)
 P sparrow          * 1.6.0      2023-05-08 [?] Bioconductor
 P stringi            1.7.12     2023-01-11 [?] CRAN (R 4.3.0)
 P stringr          * 1.5.0      2022-12-02 [?] CRAN (R 4.3.0)
 P sys                3.4.2      2023-05-23 [?] CRAN (R 4.3.0)
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 [1] /Users/sandmann/repositories/blog/renv/library/R-4.3/aarch64-apple-darwin20
 [2] /Users/sandmann/Library/Caches/org.R-project.R/R/renv/sandbox/R-4.3/aarch64-apple-darwin20/ac5c2659

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