Radar (spider) plot for gene sets — gs

Radar (spider) plot for gene sets, either for one or more results from functional enrichment analysis.

gs_radar(
  res_enrich,
  res_enrich2 = NULL,
  n_gs = 20,
  p_value_column = "gs_pvalue"
)

gs_spider(
  res_enrich,
  res_enrich2 = NULL,
  n_gs = 20,
  p_value_column = "gs_pvalue"
)

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).
res_enrich2: Analogous to res_enrich1, another data.frame object, storing the result of the functional enrichment analysis, but for a different setting (e.g. another contrast). Defaults to NULL (in this case, a single set of enrichment results is plotted).
n_gs: Integer value, corresponding to the maximal number of gene sets to be displayed
p_value_column: Character string, specifying the column of res_enrich where the p-value to be represented is specified. Defaults to gs_pvalue (it could have other values, in case more than one p-value - or an adjusted p-value - have been specified).

Value

A plotly object

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_radar(res_enrich = res_enrich)

# or using the alias...
gs_spider(res_enrich = res_enrich)


# with more than one set
res_enrich2 <- res_enrich[1:60, ]
set.seed(42)
shuffled_ones <- sample(seq_len(60)) # to generate permuted p-values
res_enrich2$gs_pvalue <- res_enrich2$gs_pvalue[shuffled_ones]
# ideally, I would also permute the z scores and aggregated scores
gs_radar(
  res_enrich = res_enrich,
  res_enrich2 = res_enrich2
)