Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments. Bemis, K. D., Harry, A., Eberlin, L. S., Ferreira, C., van de Ven, S. M., Mallick, P., Stolowitz, M., & Vitek, O. Bioinformatics, 31(14):2418-2420, 2015. ![Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.Contact: o.vitek@neu.eduAvailability and implementation: The code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.
@Article{bemis15cardinal,
author = {Bemis, Kyle D. and Harry, April and Eberlin, Livia S. and Ferreira, Christina and van de Ven, Stephanie M. and Mallick, Parag and Stolowitz, Mark and Vitek, Olga},
title = {Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments},
journal = {Bioinformatics},
year = {2015},
volume = {31},
number = {14},
pages = {2418-2420},
abstract = {Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.Contact: o.vitek@neu.eduAvailability and implementation: The code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.},
doi = {10.1093/bioinformatics/btv146},
eprint = {http://bioinformatics.oxfordjournals.org/content/31/14/2418.full.pdf+html},
owner = {Purva},
timestamp = {2016-12-22},
url = {http://bioinformatics.oxfordjournals.org/content/31/14/2418.abstract},
}
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