Nonnegative Network Component Analysis by Linear Programming for Gene Regulatory Network Reconstruction

Authors:
Chunqi Chang;Zhi Ding;Yeung Sam Hung
Affiliations:
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong,;Department of Electrical and Computer Engineering, University of California, Davis, USA CA 95616;Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong,
Venue:
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Year:
2009

Citing 2
Cited 1

Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data

Bioinformatics

An Efficient Convex Nonnegative Network Component Analysis for Gene Regulatory Network Reconstruction

PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics

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Abstract

We consider a systems biology problem of reconstructing gene regulatory network from time-course gene expression microarray data, a special blind source separation problem for which conventional methods cannot be applied. Network component analysis (NCA), which makes use of the structural information of the mixing matrix, is a tailored method for this specific blind source separation problem. In this paper, a new NCA method called nonnegative NCA (nnNCA) is proposed to take into account of the non-negativity constraint on the mixing matrix that is based on a reasonable biological assumption. The nnNCA problem is formulated as a linear programming problem which can be solved effectively. Simulation results on spectroscopy data and experimental results on time-course microarray data of yeast cell cycle demonstrate the effectiveness and anti-noise robustness of the proposed nnNCA method.