Binary matrix factorization for analyzing gene exp

Authors: ZhongYuan Zhang Tao Li Chris Ding XianWen Ren XiangSun Zhang

Publish Date: 2009/09/02

Volume: 20, Issue: 1, Pages: 28-

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Abstract

The advent of microarray technology enables us to monitor an entire genome in a single chip using a systematic approach Clustering as a widely used data mining approach has been used to discover phenotypes from the raw expression data However traditional clustering algorithms have limitations since they can not identify the substructures of samples and features hidden behind the data Different from clustering biclustering is a new methodology for discovering genes that are highly related to a subset of samples Several biclustering models/methods have been presented and used for tumor clinical diagnosis and pathological research In this paper we present a new biclustering model using Binary Matrix Factorization BMF BMF is a new variant rooted from nonnegative matrix factorization NMF We begin by proving a new boundedness property of NMF Two different algorithms to implement the model and their comparison are then presented We show that the microarray data biclustering problem can be formulated as a BMF problem and can be solved effectively using our proposed algorithms Unlike the greedy strategybased algorithms our proposed algorithms for BMF are more likely to find the global optima Experimental results on synthetic and real datasets demonstrate the advantages of BMF over existing biclustering methods Besides the attractive clustering performance BMF can generate sparse results ie the number of genes/features involved in each biclustering structure is very small related to the total number of genes/features that are in accordance with the common practice in molecular biology

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