Convert a gseaResult object for straightforward use in GeneTonic()
shake_gsenrichResult(obj)
A data.frame
compatible for use in GeneTonic()
as res_enrich
This function is able to handle the output of clusterProfiler
's gseGO
and
GSEA
, as they both return an object of class gseaResult
- and this in turn
contains the information required to create correctly a res_enrich
object.
Other shakers:
shake_davidResult()
,
shake_enrichResult()
,
shake_enrichrResult()
,
shake_fgseaResult()
,
shake_gprofilerResult()
,
shake_topGOtableResult()
# dds
library("macrophage")
library("DESeq2")
data(gse)
dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
#> using counts and average transcript lengths from tximeta
rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
# res object
data(res_de_macrophage, package = "GeneTonic")
sorted_genes <- sort(
setNames(res_macrophage_IFNg_vs_naive$log2FoldChange,
res_macrophage_IFNg_vs_naive$SYMBOL),
decreasing = TRUE
)
if (FALSE) { # \dontrun{
library("clusterProfiler")
library("org.Hs.eg.db")
gsego_IFNg_vs_naive <- gseGO(
geneList = sorted_genes,
ont = "BP",
OrgDb = org.Hs.eg.db,
keyType = "SYMBOL",
minGSSize = 10,
maxGSSize = 500,
pvalueCutoff = 0.05,
verbose = TRUE
)
res_enrich <- shake_gsenrichResult(gsego_IFNg_vs_naive)
head(res_enrich)
gtl_macrophage <- GeneTonicList(
dds = dds_macrophage,
res_de = res_macrophage_IFNg_vs_naive,
res_enrich = res_enrich,
annotation_obj = anno_df
)
} # }