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Bioinformatics Advance Access originally published online on June 5, 2008
Bioinformatics 2008 24(15):1688-1697; doi:10.1093/bioinformatics/btn245
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/)which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Knowledge-based gene expression classification via matrix factorization

R. Schachtner 1, D. Lutter 1,2,3, P. Knollmüller 1, A. M. Tomé 4, F. J. Theis 1,2, G. Schmitz 3, M. Stetter 5, P. Gómez Vilda 6 and E. W. Lang 1,*

1CIML/Biophysics, University of Regensburg, D-93040 Regensburg, 2CMB/IBI, GSF Munich, 3Clinical Chemistry, University Hospital Regensburg, D-93042 Regensburg, Germany, 4IEETA/DETI, Universidade de Aveiro, 3810-193 Aveiro, Portugal, 5Siemens Corporate Technology, Siemens AG, Munich, Germany and 6DATSI/FI, Universidad Polit'ecnica de Madrid, E-18500 Madrid, Spain

*To whom correspondence should be addressed.


  Abstract

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks.

Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact: elmar.lang{at}biologie.uni-regensburg.de

Associate Editor: Olga Troyanskaya

Received on September 24, 2007; revised on May 14, 2008; accepted on May 23, 2008

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