In this vignette, we perform a functional Gene Set Enrichment Analysis (GSEA) from differential Expression analysis from genes of luminal cells in the mammary gland. (see utils::vignette("mouse_bioconducor", package ="ViSEAGO"))
We load examples files from ViSEAGO package using system.file from the locally installed package. We read gene identifiers (GeneID) and corresponding statistical values (BH padj) for all results.
in this example, gene identifiers were ranked based on the BH padj from Differential expression analysis.
# load gene identifiants and padj test results from Differential Analysis complete tables
PregnantvsLactate<-data.table::fread(
system.file(
"extdata/data/input",
"pregnantvslactate.complete.txt",
package = "ViSEAGO"
),
select = c("Id","padj")
)
VirginvsLactate<-data.table::fread(
system.file(
"extdata/data/input",
"virginvslactate.complete.txt",
package = "ViSEAGO"
),
select = c("Id","padj")
)
VirginvsPregnant<-data.table::fread(
system.file(
"extdata/data/input",
"virginvspregnant.complete.txt",
package = "ViSEAGO"
),
select = c("Id","padj")
)
# rank Id based on statistical value (BH padj in this example)
data.table::setorder(PregnantvsLactate,padj)
data.table::setorder(VirginvsLactate,padj)
data.table::setorder(VirginvsPregnant,padj)Here, we display the header from the PregnantvsLactate ranked data.table.
Id padj
1: 11941 1.722774e-09
2: 12992 1.722774e-09
3: 13358 1.722774e-09
4: 13645 1.722774e-09
5: 69219 1.836763e-09
---
15800: 79221 1.000000e+00
15801: 80979 1.000000e+00
15802: 83558 1.000000e+00
15803: 84111 1.000000e+00
15804: 97476 1.000000e+00In this study, we build a myGENE2GO object using the Bioconductor org.Mm.eg.db database package for the mouse species. This object contains all available GO annotations for categories Molecular Function (MF), Biological Process (BP), and Cellular Component (CC).
NB: Donโt forget to check if the last current annotation database version is installed in your R session! See ViSEAGO::available_organisms(Bioconductor).
# connect to Bioconductor
Bioconductor<-ViSEAGO::Bioconductor2GO()
# load GO annotations from Bioconductor
myGENE2GO<-ViSEAGO::annotate(
"org.Mm.eg.db",
Bioconductor
)- object class: gene2GO
- database: Bioconductor
- stamp/version: 2019-Jul10
- organism id: org.Mm.eg.db
GO annotations:
- Molecular Function (MF): 22707 annotated genes with 91986 terms (4121 unique terms)
- Biological Process (BP): 23210 annotated genes with 164825 terms (12224 unique terms)
- Cellular Component (CC): 23436 annotated genes with 107852 terms (1723 unique terms)We perform a functional Gene Set Enrichment Analysis (GSEA) from differential Expression analysis from genes of luminal cells in the mammary gland.
Here, gene list were ranked based on the BH padj from Differential expression analysis.
The enriched Biological process (BP) are obtained using a GSEA test with ViSEAGO::runfgsea, which is a wrapper from algorithms developped in fgsea package [1].
we perform the GO enrichment tests for BP category with fgseaMultilevelalgorithm.
# perform fgseaMultilevel tests
BP_PregnantvsLactate<-ViSEAGO::runfgsea(
geneSel=PregnantvsLactate,
ont="BP",
gene2GO=myGENE2GO,
method ="fgseaMultilevel",
params = list(
scoreType = "pos",
minSize=5
)
)
BP_VirginvsLactate<-ViSEAGO::runfgsea(
geneSel=VirginvsLactate,
gene2GO=myGENE2GO,
ont="BP",
method ="fgseaMultilevel",
params = list(
scoreType = "pos",
minSize=5
)
)
BP_VirginvsPregnant<-ViSEAGO::runfgsea(
geneSel=VirginvsPregnant,
gene2GO=myGENE2GO,
ont="BP",
method ="fgseaMultilevel",
params = list(
scoreType = "pos",
minSize=5
)
)We combine the results of the three GSEA tests into an object using ViSEAGO::merge_enrich_terms method.
# merge fgsea results
BP_sResults<-ViSEAGO::merge_enrich_terms(
cutoff=0.01,
Input=list(
PregnantvsLactate="BP_PregnantvsLactate",
VirginvsLactate="BP_VirginvsLactate",
VirginvsPregnant="BP_VirginvsPregnant"
)
)- object class: enrich_GO_terms
- ontology: BP
- method: fgsea
- summary:
PregnantvsLactate
method : fgseaMultilevel
sampleSize : 101
minSize : 5
maxSize : Inf
eps : 0
scoreType : pos
nproc : 0
gseaParam : 1
BPPARAM : fgseaMultilevel
absEps : 101
VirginvsLactate
method : fgseaMultilevel
sampleSize : 101
minSize : 5
maxSize : Inf
eps : 0
scoreType : pos
nproc : 0
gseaParam : 1
BPPARAM : fgseaMultilevel
absEps : 101
VirginvsPregnant
method : fgseaMultilevel
sampleSize : 101
minSize : 5
maxSize : Inf
eps : 0
scoreType : pos
nproc : 0
gseaParam : 1
BPPARAM : fgseaMultilevel
absEps : 101- enrichment pvalue cutoff:
PregnantvsLactate : 0.01
VirginvsLactate : 0.01
VirginvsPregnant : 0.01
- enrich GOs (in at least one list): 184 GO terms of 3 conditions.
PregnantvsLactate : 67 terms
VirginvsLactate : 58 terms
VirginvsPregnant : 64 terms
Now you can follow mouse bioconductor vignette for next steps beginning with 3.3 Graphs of GO enrichment tests section (utils::vignette("mouse_bioconducor", package ="ViSEAGO")).
1. Korotkevich G, Sukhov V, Sergushichev A. Fast gene set enrichment analysis. bioRxiv [Internet]. Cold Spring Harbor Labs Journals; 2019; Available from: http://biorxiv.org/content/early/2016/06/20/060012