irMF

Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory; even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Using dimension reduction methods like... the singular value decomposition and its close cousin, principal component analysis (PCA) produces score and loading matrices representing the rows and the columns of the original table and these matrices may be used for both prediction purposes and to gain structural understanding. When the data entries, e.g. gene expression levels, are necessarily nonnegative, the matrix factors meant to represent them should arguably also contain only nonnegative elements. This thinking, and the desire for parsimony, underlies the development of an attractive alternative, nonnegative matrix factorization, or NMF, which directly seeks matrix factors containing only nonnegative elements, rather than attempting to transform a loading or score matrix of mixed signs into one with only nonnegative elements. The resulting factorization often leads to substantial improvements in interpretability of the factors and in prediction of outcome. inferential robust Matrix Factorization, or irMF, Supports robust and regular singular value decomposition, with four algorithms for non-negative matrix factorization + sparsity options Supports important variants of NMF, such as convex, kernel, PosNeg or semi-NMF Supports an extension to tensors, such as time-course data Provides association statistics between NMF clusters and actual groups, ROC curves The standard version of irMF includes all NMF algorithms and utilities. However, irMF non-interactive mode is part of the professional version only (see key features ). irMF is an exploratory software in the form of an Add-In to JMP (SAS Institute, Cary, NC, USA). Core calculation modules being written in Python 3 can be used independently through a ...Read More