gs_scoresheat: Plots a matrix of geneset scores

In GeneTonic: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

Description

Plots a matrix of geneset Z scores, across all samples

Usage

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)
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
dds_macrophage <- estimateSizeFactors(dds_macrophage)

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)
)

# 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)
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)

GeneTonic documentation built on Nov. 8, 2020, 5:27 p.m.