slice-methods {Cardinal} | R Documentation

## Slice an image

### Description

Slice an imaging dataset as a "data cube".

### Usage

    ## S4 method for signature 'SparseImagingExperiment'
    slice(.data, ..., .preserve=FALSE)

### Arguments

`.data` | An imaging dataset.
`...` | Conditions describing features to slice, passed to `features()`.
`.preserve` | Should all array dimensions be retained? If FALSE, dimensions with only one level are dropped using `drop`.

### Details

Because `SparseImagingExperiment` objects may be pixel-sparse, their data is always internally represented as a matrix rather than an array, where each column is a feature-vector. Only columns for non-missing pixels are retained. This is simpler and more space-efficient if the image is non-rectangular, non-gridded, or has many missing values.

However, it is often necessary to index into the data as if it were an actual "data cube", with explicit array dimensions for each spatial dimension. `slice()` allows this by slicing the object as a "data cube", and returning an image array from the object.

For non-rectangular data, this may result in missing values. For non-gridded data, images must be projected to an array (with a regular grid), and the result may not represent the underlying values exactly.

### Value

An array representing the sliced image(s).

### Author(s)

Kylie A. Bemis

### Examples

    register(SerialParam())

    set.seed(1)
    x <- simulateImage(preset=1, npeaks=10, dim=c(10,10), representation="centroid")
    m <- mz(metadata(x)$design$featureData)

    # slice image for first feature
    slice(x, 1)

    # slice by m/z-value
    slice(x, mz=m[1])

    # slice multiple
    slice(x, mz=m[1:3])

[Package Cardinal version 2.4.0 Index]