Create a marker vector or matrix.

Functions producing a new vector (matrix) marker vector set from an existing matrix (vector) marker set.

Usage

mrkVecToMat(object, vfcol = "markers", mfcol = "Markers")

mrkMatToVec(object, mfcol = "Markers", vfcol = "markers")

mrkMatAndVec(object, vfcol = "markers", mfcol = "Markers")

showMrkMat(object, mfcol = "Markers")

isMrkMat(object, fcol = "Markers")

isMrkVec(object, fcol = "markers")

mrkEncoding(object, fcol = "markers")

Arguments

object

An MSnSet object

vfcol

The name of the vector marker feature variable. Default is "markers".

mfcol

The name of the matrix marker feature variable. Default is "Markers".

fcol

A marker feature variable name.

Value

An updated MSnSet with a new vector (matrix) marker set.

Details

Sub-cellular markers can be encoded in two different ways. Sets of spatial markers can be represented as character vectors (character or factor, to be accurate), stored as feature metadata, and proteins of unknown or uncertain localisation (unlabelled, to be classified) are marked with the "unknown" character. While very handy, this encoding suffers from some drawbacks, in particular the difficulty to label proteins that reside in multiple (possible or actual) localisations. The markers vector feature data is typically named markers. A new matrix encoding is also supported. Each spatial compartment is defined in a column in a binary markers matrix and the resident proteins are encoded with 1s. The markers matrix feature data is typically named Markers. If proteins are assigned unique localisations only (i.e. no multi-localisation) or their localisation is unknown (unlabelled), then both encodings are equivalent. When the markers are encoded as vectors, features of unknown localisation are defined as fData(object)[, fcol] == "unknown". For matrix-encoded markers, unlabelled proteins are defined as rowSums(fData(object)[, fcol]) == 0.

The mrkMatToVec and mrkVecToMat functions enable the conversion from matrix (vector) to vector (matrix). The mrkMatAndVec function generates the missing encoding from the existing one. If the destination encoding already exists, or, more accurately, if the feature variable of the destination encoding exists, an error is thrown. During the conversion from matrix to vector, if multiple possible label exists, they are dropped, i.e. they are converted to "unknown". Function isMrkVec and isMrkMat can be used to test if a marker set is encoded as a vector or a matrix. mrkEncoding returns either "vector" or "matrix" depending on the nature of the markers.

See also

Other functions that operate on markers are getMarkers, getMarkerClasses and markerMSnSet. To add markers to an existing MSnSet, see the addMarkers function and pRolocmarkers, for a list of suggested markers.

Author

Laurent Gatto and Lisa Breckels

Examples

library("pRolocdata")
data(dunkley2006)
dunk <- mrkVecToMat(dunkley2006)
head(fData(dunk)$Markers)
#>           ER lumen ER membrane Golgi Mitochondrion PM Plastid Ribosome TGN
#> AT1G09210        1           0     0             0  0       0        0   0
#> AT1G21750        1           0     0             0  0       0        0   0
#> AT1G51760        1           0     0             0  0       0        0   0
#> AT1G56340        1           0     0             0  0       0        0   0
#> AT2G32920        1           0     0             0  0       0        0   0
#> AT2G47470        1           0     0             0  0       0        0   0
#>           vacuole
#> AT1G09210       0
#> AT1G21750       0
#> AT1G51760       0
#> AT1G56340       0
#> AT2G32920       0
#> AT2G47470       0
fData(dunk)$markers <- NULL
dunk <- mrkMatToVec(dunk)
stopifnot(all.equal(fData(dunkley2006)$markers,
                    fData(dunk)$markers))