R: SparseImagingExperiment: Pixel-sparse imaging experiments

SparseImagingExperiment-class {Cardinal}

R Documentation

SparseImagingExperiment: Pixel-sparse imaging experiments

Description

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.

Usage

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

## Additional methods documented below

Arguments

`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.

Slots

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).

Methods

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.

Author(s)

Kylie A. Bemis

Examples

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)

[Package Cardinal version 2.6.0 Index]