The expandFeatureVars and mergeFeatureVars respectively expand
and merge groups of feature variables. Using these functions, a
set of columns in a feature data can be merged into a single new
data.frame-column variables and a data.frame-column can be
expanded into single feature columns. The original feature
variables are removed.
Arguments
- x
An object of class
MSnSet.- fcol
A
character()of feature variables to expand (forexpandFeatureVars) or merge (formergeFeatureVars).- prefix
A
character(1)to use as prefix to the new feature variables. If missing (default), thenfcolis used instead. IfNULL, then no prefix is used.- fcol2
A
character(1)defining the name of the new feature variable.
Examples
library("pRolocdata")
data(hyperLOPIT2015)
fvarLabels(hyperLOPIT2015)
#> [1] "entry.name" "protein.description"
#> [3] "peptides.rep1" "peptides.rep2"
#> [5] "psms.rep1" "psms.rep2"
#> [7] "phenodisco.input" "phenodisco.output"
#> [9] "curated.phenodisco.output" "markers"
#> [11] "svm.classification" "svm.score"
#> [13] "svm.top.quartile" "final.assignment"
#> [15] "first.evidence" "curated.organelles"
#> [17] "cytoskeletal.components" "trafficking.proteins"
#> [19] "protein.complexes" "signalling.cascades"
#> [21] "oct4.interactome" "nanog.interactome"
#> [23] "sox2.interactome" "cell.surface.proteins"
#> [25] "markers2015" "TAGM"
## Let's merge all svm prediction feature variables
(k <- grep("^svm", fvarLabels(hyperLOPIT2015), value = TRUE))
#> [1] "svm.classification" "svm.score" "svm.top.quartile"
hl <- mergeFeatureVars(hyperLOPIT2015, fcol = k, fcol2 = "SVM")
fvarLabels(hl)
#> [1] "entry.name" "protein.description"
#> [3] "peptides.rep1" "peptides.rep2"
#> [5] "psms.rep1" "psms.rep2"
#> [7] "phenodisco.input" "phenodisco.output"
#> [9] "curated.phenodisco.output" "markers"
#> [11] "final.assignment" "first.evidence"
#> [13] "curated.organelles" "cytoskeletal.components"
#> [15] "trafficking.proteins" "protein.complexes"
#> [17] "signalling.cascades" "oct4.interactome"
#> [19] "nanog.interactome" "sox2.interactome"
#> [21] "cell.surface.proteins" "markers2015"
#> [23] "TAGM" "SVM"
head(fData(hl)$SVM)
#> svm.classification svm.score svm.top.quartile
#> Q9JHU4 Peroxisome 0.303 unknown
#> Q9QXS1-3 60S Ribosome 0.223 unknown
#> Q9ERU9 Nucleus - Chromatin 0.737 unknown
#> P26039 Actin cytoskeleton 1.000 Actin cytoskeleton
#> Q8BTM8 Endoplasmic reticulum/Golgi apparatus 0.218 unknown
#> A2ARV4 Lysosome 0.681 unknown
## Let's expand the new SVM into individual columns
hl2 <- expandFeatureVars(hl, "SVM")
fvarLabels(hl2)
#> [1] "entry.name" "protein.description"
#> [3] "peptides.rep1" "peptides.rep2"
#> [5] "psms.rep1" "psms.rep2"
#> [7] "phenodisco.input" "phenodisco.output"
#> [9] "curated.phenodisco.output" "markers"
#> [11] "final.assignment" "first.evidence"
#> [13] "curated.organelles" "cytoskeletal.components"
#> [15] "trafficking.proteins" "protein.complexes"
#> [17] "signalling.cascades" "oct4.interactome"
#> [19] "nanog.interactome" "sox2.interactome"
#> [21] "cell.surface.proteins" "markers2015"
#> [23] "TAGM" "SVM.svm.classification"
#> [25] "SVM.svm.score" "SVM.svm.top.quartile"
## We can set the prefix manually
hl2 <- expandFeatureVars(hl, "SVM", prefix = "Expanded")
fvarLabels(hl2)
#> [1] "entry.name" "protein.description"
#> [3] "peptides.rep1" "peptides.rep2"
#> [5] "psms.rep1" "psms.rep2"
#> [7] "phenodisco.input" "phenodisco.output"
#> [9] "curated.phenodisco.output" "markers"
#> [11] "final.assignment" "first.evidence"
#> [13] "curated.organelles" "cytoskeletal.components"
#> [15] "trafficking.proteins" "protein.complexes"
#> [17] "signalling.cascades" "oct4.interactome"
#> [19] "nanog.interactome" "sox2.interactome"
#> [21] "cell.surface.proteins" "markers2015"
#> [23] "TAGM" "Expanded.svm.classification"
#> [25] "Expanded.svm.score" "Expanded.svm.top.quartile"
## If we don't want any prefix
hl2 <- expandFeatureVars(hl, "SVM", prefix = NULL)
fvarLabels(hl2)
#> [1] "entry.name" "protein.description"
#> [3] "peptides.rep1" "peptides.rep2"
#> [5] "psms.rep1" "psms.rep2"
#> [7] "phenodisco.input" "phenodisco.output"
#> [9] "curated.phenodisco.output" "markers"
#> [11] "final.assignment" "first.evidence"
#> [13] "curated.organelles" "cytoskeletal.components"
#> [15] "trafficking.proteins" "protein.complexes"
#> [17] "signalling.cascades" "oct4.interactome"
#> [19] "nanog.interactome" "sox2.interactome"
#> [21] "cell.surface.proteins" "markers2015"
#> [23] "TAGM" "svm.classification"
#> [25] "svm.score" "svm.top.quartile"