Plots a matrix of geneset scores

Plots a matrix of geneset Z scores, across all samples

gs_scoresheat(
  mat,
  n_gs = nrow(mat),
  gs_ids = NULL,
  clustering_distance_rows = "euclidean",
  clustering_distance_cols = "euclidean",
  cluster_rows = TRUE,
  cluster_cols = TRUE
)

Arguments

mat

A matrix, e.g. returned by the gs_scores() function

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.

clustering_distance_rows

Character, a distance measure used in clustering rows

clustering_distance_cols

Character, a distance measure used in clustering columns

cluster_rows

Logical, determining if rows should be clustered

cluster_cols

Logical, determining if columns should be clustered

Value

A ggplot object

See also

gs_scores() computes the scores plotted by this function

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

vst_macrophage <- vst(dds_macrophage)

# 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)

scores_mat <- gs_scores(
  vst_macrophage,
  res_de,
  res_enrich[1:30, ],
  anno_df
)
gs_scoresheat(scores_mat,
  n_gs = 30
)