Volcano plot for gene sets

Source: R/gs_volcano.R

Volcano plot for gene sets, to summarize visually the functional enrichment results

gs_volcano(
  res_enrich,
  gtl = NULL,
  p_threshold = 0.05,
  color_by = "aggr_score",
  volcano_labels = 10,
  scale_circles = 1,
  gs_ids = NULL,
  plot_title = NULL
)

Arguments

res_enrich

A data.frame object, storing the result of the functional enrichment analysis. See more in the main function, GeneTonic(), to check the formatting requirements (a minimal set of columns should be present). This object needs to be processed first by a function such as get_aggrscores() to compute the term-wise z_score or aggr_score, which will be used for plotting

gtl

A GeneTonic-list object, containing in its slots the arguments specified above: dds, res_de, res_enrich, and annotation_obj - the names of the list must be specified following the content they are expecting

p_threshold

Numeric, defines the threshold to be used for filtering the gene sets to display. Defaults to 0.05

color_by

Character specifying the column of res_enrich to be used for coloring the plotted gene sets. Defaults to aggr_score.

volcano_labels

Integer, maximum number of labels for the gene sets to be plotted as labels on the volcano scatter plot.

scale_circles

A numeric value, to define the scaling factor for the circle sizes. Defaults to 1.

gs_ids

Character vector, containing a subset of gs_id as they are available in res_enrich. Lists the gene sets to be labeled.

plot_title

Character string, used as title for the plot. If left NULL, it defaults to a general description of the plot and of the DE contrast

Value

A ggplot object

Details

It is also possible to reduce the redundancy of the input res_enrich object, if it is passed in advance to the gs_simplify() function.

See also

gs_simplify() can be applied in advance to res_enrich to reduce the redundancy of the displayed gene sets

Examples


library("macrophage")
library("DESeq2")
library("org.Hs.eg.db")
library("AnnotationDbi")

# dds object
data("gse", package = "macrophage")
dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
#> using counts and average transcript lengths from tximeta
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)
#> using 'avgTxLength' from assays(dds), correcting for library size

# annotation object
anno_df <- data.frame(
  gene_id = rownames(dds_macrophage),
  gene_name = mapIds(org.Hs.eg.db,
    keys = rownames(dds_macrophage),
    column = "SYMBOL",
    keytype = "ENSEMBL"
  ),
  stringsAsFactors = FALSE,
  row.names = rownames(dds_macrophage)
)
#> 'select()' returned 1:many mapping between keys and columns

# res object
data(res_de_macrophage, package = "GeneTonic")
res_de <- res_macrophage_IFNg_vs_naive

# res_enrich object
data(res_enrich_macrophage, package = "GeneTonic")
res_enrich <- shake_topGOtableResult(topgoDE_macrophage_IFNg_vs_naive)
#> Found 500 gene sets in `topGOtableResult` object.
#> Converting for usage in GeneTonic...
res_enrich <- get_aggrscores(res_enrich, res_de, anno_df)

gs_volcano(res_enrich)
#> Warning: Removed 16 rows containing missing values or values outside the scale range
#> (`geom_point()`).