R/AllGenerics.R
, R/GeneSetDb-methods.R
, R/SparrowResult-methods.R
geneSet.Rd
Gene sets inside a GeneSetDb()
are indexed by their collection,name
compound key. There is no special class to represent an individual gene set.
Instead, gene sets are returned as a data.frame, the rows of which enumerate
the features that belong to them.
When x
is a SparrowResult()
, this function will append
the differential expression statistics for the individual features generated
across the contrast that defined x
.
geneSet(x, i, j, ...)
# S4 method for class 'GeneSetDb'
geneSet(
x,
i,
j,
active.only = is.conformed(x),
with.feature.map = FALSE,
...,
collection = NULL,
name = NULL,
as.dt = FALSE
)
# S4 method for class 'SparrowResult'
geneSet(
x,
i,
j,
active.only = TRUE,
with.feature.map = FALSE,
...,
collection = NULL,
name = NULL,
as.dt = FALSE
)
Object to retrieve the gene set from, either a GeneSetDb
or a
SparrowResult
.
The collection,name compound key identifier of the gene set
passed down to inner functinos
only look for gene sets that are "active"? Defaults to
TRUE
if x
is conformed to a target expression object, else FALSE
.
conform()
for further details.
If TRUE
, then details of the feature mapping
from the original feature_id space to the target feature space are included
(default: FALSE
).
using i
as the parameter for "collection" isn't intuitive
so if speficially set this paramter, it will replace the value for i
.
the same for the collection
:i
parameter relationship, but for
j
:name
.
If FALSE
(default), the data.frame like thing that
this funciton returns will be set to a data.frame. Set this to TRUE
to keep this object as a data.table
a data.(frame|table)
of gene set information. If x
is a
SparrowResult
object, then differential expression statistics
are added as columns to this result.
gdb <- exampleGeneSetDb()
geneSet(gdb, "c2", "KOMMAGANI_TP63_GAMMA_TARGETS")
#> collection name active N n feature_id
#> 1 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 1026
#> 2 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 10971
#> 3 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 3486
#> 4 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 3714
#> 5 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 4193
#> 6 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 4854
#> 7 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 6604
#> 8 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 7421
#> 9 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 9540
geneSet(gdb, collection = "c2", name = "KOMMAGANI_TP63_GAMMA_TARGETS")
#> collection name active N n feature_id
#> 1 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 1026
#> 2 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 10971
#> 3 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 3486
#> 4 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 3714
#> 5 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 4193
#> 6 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 4854
#> 7 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 6604
#> 8 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 7421
#> 9 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 9540
geneSet(gdb, name = "KOMMAGANI_TP63_GAMMA_TARGETS")
#> collection name active N n feature_id
#> 1 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 1026
#> 2 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 10971
#> 3 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 3486
#> 4 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 3714
#> 5 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 4193
#> 6 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 4854
#> 7 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 6604
#> 8 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 7421
#> 9 c2 KOMMAGANI_TP63_GAMMA_TARGETS FALSE 9 NA 9540