Make a faceted boxplot of the top differential genes in a contrast

Draws one boxplot facet per gene, samples grouped by condition, with an optional beeswarm overlay of individual points. This function only renders: gene ranking, significance filtering and q values are computed upstream by the caller (e.g. the contrasts module).

Usage

static_topgene_boxplots(
  assay,
  groupby,
  genes,
  annotations = NULL,
  labels = NULL,
  beeswarm = TRUE,
  ncol = NULL,
  palette = NULL,
  palette_name = COLORBLIND_PALETTE_NAME,
  expressiontype = "expression",
  base_size = 11,
  should_transform = NULL
)

Arguments

assay: Numeric matrix, genes (rows) by samples (columns)
groupby: Vector of group labels, one per column of assay
genes: Character vector of row names of assay to facet on, in the order facets should appear. Also used to look up values in assay, so must match its row names even when labels is supplied.
annotations: Optional named character vector keyed by the values in genes, rendered as a per-facet annotation (e.g. a q value string)
labels: Optional named character vector keyed by the values in genes, used as the facet title in place of the raw gene identifier (e.g. a gene symbol where genes holds Ensembl IDs). Genes missing from labels fall back to their raw identifier. genes itself still drives the lookup into assay and the matching of annotations.
beeswarm: Overlay individual points using geom_quasirandom?
ncol: Number of facet columns. Defaults to min(3, length(genes))
palette: Palette of colours, one for each unique value of groupby
palette_name: Valid R color palette name
expressiontype: Expression type for use in y axis label
base_size: Passed to ggplot's theme()
should_transform: A boolean indicating if the log2 transformation should be applied. If TRUE, log2 transformation is applied unconditionally. If FALSE, no transformation is applied. If NULL, a conditional transformation based on threshold is applied.

Value

output A ggplot output

Examples

require(airway)
data(airway, package = "airway")
mat <- assays(airway)[[1]][1:4, ]
groupby <- as.character(colData(airway)$dex)
static_topgene_boxplots(mat, groupby, rownames(mat))