Alluvial (sankey) plot for a set of genesets and the associated genes

Source: R/gs_alluvial.R

Generate an interactive alluvial plot linking genesets to their associated genes

gs_alluvial(
  res_enrich,
  res_de,
  annotation_obj,
  gtl = NULL,
  n_gs = 5,
  gs_ids = NULL
)

gs_sankey(
  res_enrich,
  res_de,
  annotation_obj,
  gtl = NULL,
  n_gs = 5,
  gs_ids = 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).

res_de

A DESeqResults object.

annotation_obj

A data.frame object with the feature annotation information, with at least two columns, gene_id and gene_name.

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

n_gs

Integer value, corresponding to the maximal number of gene sets to be displayed

gs_ids

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

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_alluvial(
  res_enrich = res_enrich,
  res_de = res_de,
  annotation_obj = anno_df,
  n_gs = 4
)


# or using the alias...
gs_sankey(
  res_enrich = res_enrich,
  res_de = res_de,
  annotation_obj = anno_df,
  n_gs = 4
)