Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data

• Published in: Bioinformatics Vol. 24; no. 11; pp. 1349 - 1358
• Main Authors: Chang, Chunqi, Ding, Zhi, Hung, Yeung Sam, Fung, Peter Chin Wan
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.06.2008; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological and medical sciences; Computer Simulation; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; Gene Expression Regulation - physiology; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Biological; Oligonucleotide Array Sequence Analysis - methods; Principal Component Analysis; Proteome - metabolism; Signal Transduction - physiology; Reconstruction; Gene; Network analysis; DNA chip; Gene regulatory network; Microarray; Bioinformatics
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btn131

Motivation: Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. Results: Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33. Availability: Software and supplementary materials are available from http://www.eee.hku.hk/~cqchang/FastNCA.htm Contact: cqchang@eee.hku.hk