SparseImagingExperiment-class {Cardinal} | R Documentation |
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 = XDataFrame(), 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 |
featureData |
A |
pixelData |
A |
metadata |
A |
processing |
A |
imageData
:An object inheriting from ImageArrayList
, storing one or more array-like data elements with conformable dimensions.
featureData
:Contains feature information in a XDataFrame
. 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:
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).
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.
collect(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)