SparseImagingExperiment-class: SparseImagingExperiment: Pixel-sparse imaging experiments
In Cardinal: A mass spectrometry imaging toolbox for statistical analysis

Description Usage Arguments Slots Methods Author(s) See Also Examples

The SparseImagingExperiment class specializes the virtual ImagingExperiment class by assuming that each pixel may be a high-dimensional feature vector (e.g., a spectrum), but the pixels themselves may be sparse. Therefore, the data may be more efficiently stored as a matrix where rows are features and columns are pixels, rather than storing the full, dense datacube.

Both 2D and 3D data are supported. Non-gridded pixel coordinates are allowed.

The MSImagingExperiment subclass adds design features for mass spectrometry imaging experiments.

## Instance creation
SparseImagingExperiment(
    imageData = matrix(nrow=0, ncol=0),
    featureData = DataFrame(),
    pixelData = PositionDataFrame(),
    metadata = list(),
    processing = SimpleList())

## Additional methods documented below

`imageData`	Either a matrix-like object with number of rows equal to the number of features and number of columns equal to the number of pixels, or an `ImageArrayList`.
`featureData`	A `DataFrame` with feature metadata, with a row for each feature.
`pixelData`	A `PositionDataFrame` with pixel metadata, with a row for each pixel.
`metadata`	A `list` with experimental-level metadata.
`processing`	A `SimpleList` with processing steps. This should typically be empty for new objects.

imageData:: An object inheriting from ImageArrayList, storing one or more array-like data elements with conformable dimensions.
featureData:: Contains feature information in a DataFrame. Each row includes the metadata for a single feature (e.g., a color channel, a molecular analyte, or a mass-to-charge ratio).
elementMetadata:: Contains pixel information in a PositionDataFrame. Each row includes the metadata for a single observation (e.g., a pixel), including specialized slot-columns for tracking pixel coordinates and experimental runs.
metadata:: A list containing experiment-level metadata.
processing:: A SimpleList containing processing steps (including both queued and previously executed processing steps).

All methods for ImagingExperiment also work on SparseImagingExperiment objects. Additional methods are documented below:

pixels(object, ...):: Returns the row indices of pixelData corresponding to conditions passed via ....
features(object, ...):: Returns the row indices of featureData corresponding to conditions passed via ....
run(object), run(object) <- value:: Get or set the experimental run slot-column from pixelData.
runNames(object), runNames(object) <- value:: Get or set the experimental run levels from pixelData.
coord(object), coord(object) <- value:: Get or set the spatial position slot-columns from pixelData.
coordLabels(object), coordLabels(object) <- value:: Get or set the names of the spatial position slot-columns from pixelData.
gridded(object), gridded(object) <- value:: Get or set whether the spatial positions are gridded or not. Typically, this should not be set manually.
resolution(object), resolution(object) <- value:: Get or set the spatial resolution of the spatial positions. Typically, this should not be set manually.
dims(object):: Get the gridded dimensions of the spatial positions (i.e., as if projected to an image raster).
is3D(object):: Check if the data is 3D or not.
slice(object, ...):: Slice the data as a data cube (i.e., as if projected to an multidimensional image raster).
processingData(object), processingData(object) <- value:: Get or set the processing slot.
preproc(object):: List the preprocessing steps queued and applied to the dataset.
pull(x, ...):: Pull all data elements of imageData into memory as matrices.
object[i, j, ..., drop]:: Subset based on the rows (featureData) and the columns (pixelData). The result is the same class as the original object.
rbind(...), cbind(...):: Combine SparseImagingExperiment objects by row or column.

Kylie A. Bemis

ImagingExperiment, MSImagingExperiment

data <- matrix(1:9^2, nrow=9, ncol=9)
t <- seq_len(9)
a <- seq_len(9)
coord <- expand.grid(x=1:3, y=1:3)

idata <- ImageArrayList(data)
fdata <- XDataFrame(t=t)
pdata <- PositionDataFrame(coord=coord, a=a)

x <- SparseImagingExperiment(
        imageData=idata,
        featureData=fdata,
        pixelData=pdata)

print(x)

Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Loading required package: BiocParallel
Loading required package: EBImage
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: ‘S4Vectors’

The following object is masked from ‘package:base’:

    expand.grid

Loading required package: ProtGenerics

Attaching package: ‘ProtGenerics’

The following object is masked from ‘package:stats’:

    smooth


Attaching package: ‘Cardinal’

The following object is masked from ‘package:stats’:

    filter

An object of class 'SparseImagingExperiment'
  <9 feature, 9 pixel> imaging dataset
    imageData(1): data0
    featureData(1): t
    pixelData(1): a
    run(1): run0
    raster dimensions: 3 x 3
    coord(2): x = 1..3, y = 1..3