runEnrich | R Documentation |
This function tests the enrichment of the components of
an IcaSet
object using package
GOstats
through function hyperGTest
.
runEnrich(icaSet, params, dbs = c("KEGG", "GO"),
ontos = c("BP", "CC", "MF"), cond = TRUE,
hgCutoff = params["pvalCutoff"])
icaSet |
An object of class
|
params |
An object of class
|
dbs |
The database to use, default is
|
ontos |
A string specifying the GO ontology to use.
Must be one of |
cond |
A logical indicating whether the calculation
should condition on the GO structure, see
|
hgCutoff |
The threshold p-value for statistical
significance, default is |
An annotation package should be available in
annotation(icaSet)
to provide the contents of the
gene sets. If none corresponds to the technology you deal
with, please choose the org.*.eg.db package according to
the organism (for example org.Hs.eg.db for Homo sapiens).
By default, if annotation(icaSet)
is empty and
organism is one of
c("Human","HomoSapiens","Mouse","Mus Musculus")
,
then either org.Hs.eg.db
or org.Mm.eg.db
is
used.
Use of GOstats
requires the input IDs to be Entrez
Gene, this function will therefore annotate either the
feature names or the gene names into Entrez Gene ID using
either the annotation package (annotation(icaSet)
)
or biomaRt
.
Three types of enrichment tests are computed for each component: the threshold is first used to select gene based on their absolute projections, then positive and negative projections are treated individually.
For each database db
(each ontology if db
is "GO"), this function writes an HTML file containing
the outputs of the enrichment tests computed through the
function hyperGTest
. The
corresponding files are located in
resPath(icaSet)
/GOstatsEnrichAnalysis/byDb/. The
results obtained for each database/ontology are then
merged into an array for each component, this array is
written as an HTML file in the directory
resPath(icaSet)
/GOstatsEnrichmentAnalysis/ (this
directory is first deleted if it already exists). This
file is the one the user should look at.
The outputs of hyperGTest
that
are given in each table are:
the database, the gene set ID, and the gene Set name
probability of observing the number of genes annotated for the gene set among the selected gene list, knowing the total number of annotated genes among the universe
,
expected number of genes in the selected gene list to be found at each tested category term/gene set,
odds ratio for each category term tested which is an indicator of the level of enrichment of genes within the list as against the universe,
number of genes in the selected gene list that are annotated for the gene set,
number of genes from the universe annotated for the gene set.
NULL
Anne Biton
buildIcaSet
,
useMart
,
hyperGTest
,
GOHyperGParams
,
hypergeoAn
,
mergeGostatsResults
## Not run:
# Load examples of IcaSet object
data(icaSetCarbayo)
## Define parameters
# Use threshold 3 to select contributing genes on which enrichment analysis will be applied
# Results of enrichment analysis will be written in path 'resPath(params)/GOstatsEnrichAnalysis'
params <- buildMineICAParams(resPath="carbayo/", selCutoff=3)
## Run enrichment analysis on the first two components contained in the icaSet object 'icaSetCarbayo'
runEnrich(params=params,icaSet=icaSetCarbayo[,,1:2],dbs="GO", ontos="BP")
## End(Not run)
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