Upset plot for genesets — gs

Create an upset plot for genesets

gs_upset(
  res_enrich,
  res_de = NULL,
  annotation_obj = NULL,
  n_gs = 10,
  gtl = NULL,
  gs_ids = NULL,
  add_de_direction = FALSE,
  add_de_gsgenes = FALSE,
  col_upDE = "#E41A1C",
  col_downDE = "#377EB8",
  upset_geom = geom_point(size = 2),
  return_upsetgsg = FALSE
)

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.
n_gs: Integer value, corresponding to the maximal number of gene sets to be included
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
gs_ids: Character vector, containing a subset of gs_id as they are available in res_enrich. Lists the gene sets to be included in addition to the top ones (via n_gs)
add_de_direction: Logical, whether to add an annotation with info on the DE direction of single genes
add_de_gsgenes: Logical, if set to TRUE adds an annotation with detail on the single components of each defined subset
col_upDE: Character, specifying the color value to be used to mark upregulated genes
col_downDE: Character, specifying the color value to be used to mark downregulated genes
upset_geom: A geom specification to be used in the upset chart. Defaults sensibly to geom_point(size = 2)
return_upsetgsg: Logical, controlling the returned value. If set to TRUE, this function will not generate the plot but only create the corresponding data.frame, in case the user wants to proceed with a custom call to create an upset plot.

Value

A ggplot object (if plotting), or alternatively a data.frame

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_upset(res_enrich,
  n_gs = 10
)


gs_upset(res_enrich,
  res_de = res_de, annotation_obj = anno_df,
  n_gs = 8,
  add_de_direction = TRUE, add_de_gsgenes = TRUE
)


# or using the practical gtl (GeneTonicList)
gtl_macrophage <- GeneTonic_list(
  dds = dds_macrophage,
  res_de = res_de,
  res_enrich = res_enrich,
  annotation_obj = anno_df
)
#> ---------------------------------
#> ----- GeneTonicList object ------
#> ---------------------------------
#> 
#> ----- dds object -----
#> Providing an expression object (as DESeqDataset) of 58294 features over 24 samples
#> 
#> ----- res_de object -----
#> Providing a DE result object (as DESeqResults), 17806 features tested, 928 found as DE
#> Upregulated:     599
#> Downregulated:   329
#> 
#> ----- res_enrich object -----
#> Providing an enrichment result object, 500 reported
#> 
#> ----- annotation_obj object -----
#> Providing an annotation object of 58294 features with information on 2 identifier types

gs_upset(
  gtl = gtl_macrophage,
  n_gs = 15,
  add_de_direction = TRUE, add_de_gsgenes = TRUE
)