mrkVecToMat {pRoloc} | R Documentation

## Create a marker vector or matrix.

### Description

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.

### 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.

### Value

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

### Author(s)

Laurent Gatto and Lisa Breckels

### 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.

### Examples

    library("pRolocdata")
    data(dunkley2006)
    dunk <- mrkVecToMat(dunkley2006)
    head(fData(dunk)$Markers)
    fData(dunk)$markers <- NULL
    dunk <- mrkMatToVec(dunk)
    stopifnot(all.equal(fData(dunkley2006)$markers,
                        fData(dunk)$markers))

[Package pRoloc version 1.30.0 Index]