View source: R/enhance_table.R
get_aggrscores | R Documentation |
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)
res_enrich |
A |
res_de |
A |
annotation_obj |
A |
gtl |
A |
aggrfun |
Specifies the function to use for aggregating the scores for
each term. Common values could be |
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()
gs_volcano()
and enrichment_map()
make efficient use of the computed
aggregated scores
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)
# 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
)
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