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Noniterative Convex Optimization Methods for Network Component Analysis
Sept.-Oct. 2012 (vol. 9 no. 5)
pp. 1472-1481
Neil Jacklin, Dept. of Electr. & Comput. Eng., Univ. of California, Davis, CA, USA
Zhi Ding, Dept. of Electr. & Comput. Eng., Univ. of California, Davis, CA, USA
Wei Chen, Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
Chunqi Chang, State Key Lab. of Brain & Cognitive Sci., Univ. of Hong Kong, Hong Kong, China
This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.
Index Terms:
optimisation,bioinformatics,cellular biophysics,genetics,least squares approximations,FastNCA approach,noniterative convex optimization methods,network component analysis,gene regulatory network reconstruction,transcription factor control strengths,transcription factor activities,network connectivity strength estimation phase,multiple error metrics,total least square formulation,Saccharomyces cerevisiae,Bayesian decomposition,Noise,Estimation,Convex functions,Least squares approximation,Gene expression,Data models,Computational modeling,bilinear model.,Transcriptional network reconstruction,network component analysis,total least squares
Citation:
Neil Jacklin, Zhi Ding, Wei Chen, Chunqi Chang, "Noniterative Convex Optimization Methods for Network Component Analysis," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 5, pp. 1472-1481, Sept.-Oct. 2012, doi:10.1109/TCBB.2012.81
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