Exploring Matrix Factorization Techniques for Classification of Gene Expression Profiles

In this study we focus on diagnostic classification tasks and the extraction of related marker genes from gene expression profiles. We apply ICA and sparse NMF to various microarray data sets. The latter monitor the gene expression levels of either human breast cancer (HBC) cell lines [1] or the famous leucemia data set [2] under various environmental conditions. We show that these matrix decomposition techniques are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles. With these marker genes corresponding test data sets can be classified into related diagnostic categories.