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Cardinal

Mass spectrometry imaging tools

Cardinal provides an abstracted interface to manipulating mass spectrometry imaging datasets, simplifying most of the basic programmatic tasks encountered during the statistical analysis of imaging data. These include image manipulation and processing of both images and mass spectra, and dynamic plotting of both.

While pre-processing steps including normalization, baseline correction, and peak-picking are provided, the core functionality of the package is statistical analysis. The package includes classification and clustering methods based on nearest shrunken centroids, as well as traditional tools like PCA and PLS.

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Version

Version

1.4.0

License

Artistic-2.0

Maintainer

Kyle D Bemis

Last Published

February 15th, 2017

Functions in Cardinal (1.4.0)

imageData-methods

Retrieve Image Data from iSets
Binmat-class

On-Disk Matrix Class Using On-Demand Disk Reads
MIAPE-Imaging-class

Class for Storing Mass Spectrometry Imaging Experiment Information
readMSIData

Read Mass Spectrometry Imaging Data Files
SImageSet-class

Class to Contain Pixel-Sparse Imaging Data
topLabels-methods

Retrieve Top-Ranked Labels from Analysis Results
OPLS-methods

Orthogonal Partial Least Squares
iSet-class

Class to Contain High-Throughput Imaging Experiment Data and Metadata
generateImage

Generate a Simulated Image
reduceDimension-methods

Reduce the Dimension of an Imaging Dataset
peakFilter-methods

Peak Filter an Imaging Dataset
Hashmat-class

Sparse Matrix Class Using Lists as Hash Tables
ImageData-class

Class Containing Arrays of Imaging Data
MSImageSet-class

Class to Contain Mass Spectrometry Imaging Experiment Data
pixels-methods

Retrieve Pixel or Feature Indices Based on Metadata
IAnnotatedDataFrame-class

Class Containing Measured Variables and Their Meta-Data Description for Imaging Experiments
coregister-methods

Coregister Images
Cardinal-package

Mass spectrometry imaging tools
standardizeSamples-methods

Standardize the Samples in an Imaging Dataset
pixelNames-methods

Retrieve Pixel Names from iSets
processingData-methods

Retrieve Pre-Processing Information from MSImageSets
peakPick-methods

Peak Pick an Imaging Dataset
image-methods

Plot the Pixel-Space of an Imaging Dataset
MSImageData-class

Class Containing Mass Spectrometry Image Data
intensity.colors

Color Palettes for Imaging
plot-methods

Plot the Feature-Space of an Imaging Dataset
generateSpectrum

Generate a Simulated Spectrum
SImageData-class

Class Containing Sparse Image Data
cvApply-methods

Apply Cross-Validated Analysis to Imaging Datasets
batchProcess-methods

Batch Pre-Processing on an Imaging Dataset
MSImageProcess-class

Class Containing Mass Spectral Pre-Processing Information
PLS-methods

Partial Least Squares
spatialKMeans-methods

Spatially-Aware K-Means Clustering
PCA-methods

Principal Components Analysis
ResultSet-class

Class to Contain Analysis Results for Imaging Experiments
pixelData-methods

Retrieve Information on Pixels in iSet-derived Classes
normalize-methods

Normalize an Imaging Dataset
coord-methods

Retrieve Pixel Coordinates from iSets
pixelApply-methods

Apply Functions over Imaging Datasets
peakAlign-methods

Peak Align an Imaging Dataset
spatialShrunkenCentroids-methods

Spatially-Aware Shrunken Centroid Clustering and Classification
mz-methods

Retrieve m/z-values from MSImageSets
smoothSignal-methods

Smooth the Feature-Vectors of an Imaging Dataset
reduceBaseline-methods

Reduce the Baseline for an Imaging Dataset
select-methods

Select Regions of an Imaging Dataset