A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human Medulloblastoma (MB) patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA (Principal Component Analysis) plots and heatmaps. Deena Gendoo and Benjamin Haibe-Kains (2016) <doi:10.1186/s13029-016-0053-y>.
Version: | 1.0.6 |
Depends: | GSVA (≥ 1.13.1), kknn (≥ 1.2-5), parallel, lattice, pheatmap, R (≥ 2.10) |
Imports: | datasets, grDevices, graphics, stats, utils |
Suggests: | knitr, MM2Sdata |
Published: | 2019-03-22 |
Author: | Deena M.A. Gendoo, Benjamin Haibe-Kains |
Maintainer: | Benjamin Haibe-Kains <benjamin.haibe.kains at utoronto.ca> |
License: | GPL-3 |
URL: | https://scfbm.biomedcentral.com/articles/10.1186/s13029-016-0053-y, http://www.sciencedirect.com/science/article/pii/S0888754315000774, http://www.pmgenomics.ca/bhklab/software/mm2s |
NeedsCompilation: | no |
Citation: | MM2S citation info |
Materials: | README |
CRAN checks: | MM2S results |
Reference manual: | MM2S.pdf |
Vignettes: |
MM2S An Introduction (HowTo) MM2S An Introduction (HowTo) from Raw Data |
Package source: | MM2S_1.0.6.tar.gz |
Windows binaries: | r-devel: MM2S_1.0.6.zip, r-release: MM2S_1.0.6.zip, r-oldrel: MM2S_1.0.6.zip |
macOS binaries: | r-release (arm64): MM2S_1.0.6.tgz, r-oldrel (arm64): MM2S_1.0.6.tgz, r-release (x86_64): MM2S_1.0.6.tgz, r-oldrel (x86_64): MM2S_1.0.6.tgz |
Old sources: | MM2S archive |
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