Compute gene set scores

Source: R/gs_heatmap.R

Compute gene set scores for each sample, by transforming the gene-wise change to a geneset-wise change

gs_scores(se, res_de, res_enrich, annotation_obj = NULL, gtl = NULL)

Arguments

se

A SummarizedExperiment object, or an object derived from this class, such as a DESeqTransform object (variance stabilized transformed data, or regularized logarithm transformed), in where the transformation has been applied to make the data more homoscedastic and thus a better fit for visualization.

res_de

A DESeqResults object.

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).

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

Value

A matrix with the geneset Z scores, e.g. to be plotted with gs_scoresheat()

See also

gs_scoresheat() plots these scores

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[1:50, ],
  anno_df
)