Title :
Sparse Nonnegative Matrix Factorization with Genetic Algorithms for Microarray Analysis
Author :
Stadlthanner, K. ; Lutter, D. ; Theis, F.J. ; Lang, E.W. ; Tomé, A.M. ; Georgieva, P. ; Puntonet, C.G.
Author_Institution :
Univ. of Regensburg, Regensburg
Abstract :
Nonnegative Matrix Factorization (NMF) has proven to be a useful tool for the analysis of nonnegative multivariate data. Gene expression profiles naturally conform to assumptions about data formats raised by NMF. However, it is known not to lead to unique results concerning the component signals extracted. In this paper we consider an extension of the NMF algorithm which provides unique solutions whenever the underlying component signals are sufficiently sparse. A new sparseness measure is proposed most appropriate to suitably transformed gene expression profiles. The resulting fitness function is discontinuous and exhibits many local minima, hence we use a genetic algorithm for its optimization. The algorithm is applied to toy data to investigate its properties as well as to a microarray data set related to Pseudo-Xanthoma Elasticum (PXE).
Keywords :
biology computing; data analysis; genetic algorithms; genetics; matrix decomposition; sparse matrices; gene expression profiles; genetic algorithms; microarray analysis; nonnegative multivariate data; pseudo-xanthoma elasticum; sparse nonnegative matrix factorization; Algorithm design and analysis; Data analysis; Data mining; Gene expression; Genetic algorithms; Independent component analysis; Matrix decomposition; Neural networks; Principal component analysis; Sparse matrices;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4370971