Compute aggregated scores for gene sets

Source: R/enhance_table.R

Computes for each gene set in the res_enrich object a Z score and an aggregated score (using the log2FoldChange values, provided in the res_de)

get_aggrscores(res_enrich, res_de, annotation_obj, gtl = NULL, aggrfun = mean)

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

aggrfun

Specifies the function to use for aggregating the scores for each term. Common values could be mean or median.

Value

A data.frame with the same columns as provided in the input, with additional information on the z_score and the aggr_score for each gene set. This information is used by other functions such as gs_volcano() or enrichment_map()

See also

gs_volcano() and enrichment_map() make efficient use of the computed aggregated 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

# 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
)