Paul Fogel, Consultant (Paris, France)
Douglas M. Hawkins (University of Minnesota)
S. Stanley Young (NISS)
email:irMF@niss.org
New Version 4.3.1 released 8/23/11. It runs only under JMP version 9.0 and later.
- Supports robust and regular singular value decomposition
- Supports four algorithms for non-negative matrix factorization + sparsity options.
- Provides visualization, heatmaps, score plots of the factorization
- Gives a novel method to judge the number of NMF factors
- Provides fitted and residual matrices
- Effects a clustering of the rows and columns of the matrix
- Provides association statistics between NMF clusters and actual groups, ROC curves
- Gives novel methods of determining which columns can be used to predict class labels that is more powerful than traditional methods
- New user interface
The most recent version of this software can be obtained through the JMP Community web page:
https://community.jmp.com/t5/JMP-Add-Ins/irMF-inferential-robust-Matrix-Factorization/ta-p/29143
Related Publications
Fogel, P., Young, S.S., Hawkins, D.M., Ledirac N. "Inferential, robust non-negative matrix factorization analysis of microarray data". Bioinformatics. (PDF File). And supplemental material mentioned in this article (ZIP file).
Liu, L., Hawkins, D.M., Ghosh, S., Young, S.S. (2003). "Robust singular value decomposition analysis of microarray data". PNAS 100, 13167-13172. Free download from http://www.pnas.org/cgi/content/full/100/23/13167
Young, S.S., Fogel, P., Hawkins, D. M."Clustering Scotch Whiskies using Non-Negative Matrix Factorization". (PDF File)