Fast Network Component Analysis (FastNCA) for gene regulatory network reconstruction from microarray data.

@article{citeulike:2682604, abstract = {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 semiquantitative 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.}, address = {Department of Electrical and Electronic Engineering, Department of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, Department of Electrical and Computer Engineering, University of California, Davis, CA95616, USA.}, author = {Chang, Chunqi and Ding, Zhi and Hung, Yeung Sam S. and Fung, Peter Chin Wan C. }, citeulike-article-id = {2682604}, doi = {http://dx.doi.org/10.1093/bioinformatics/btn131}, issn = {1460-2059}, journal = {Bioinformatics (Oxford, England)}, keywords = {network, regulation, software}, month = {April}, posted-at = {2008-06-17 16:42:35}, priority = {2}, title = {Fast Network Component Analysis (FastNCA) for gene regulatory network reconstruction from microarray data.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn131}, year = {2008} }

TY - JOUR ID - citeulike:2682604 L3 - citeulike-article-id:2682604 TI - Fast Network Component Analysis (FastNCA) for gene regulatory network reconstruction from microarray data. JF - Bioinformatics (Oxford, England) AD - Department of Electrical and Electronic Engineering, Department of Medicine, The University of Hong Kong, Pokfulam Road, Hong Kong, Department of Electrical and Computer Engineering, University of California, Davis, CA95616, USA. SN - 1460-2059 N2 - 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 semiquantitative 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. KW - network KW - regulation KW - software AU - Chang, Chunqi AU - Ding, Zhi AU - Hung, Yeung Sam AU - Fung, Peter Chin Wan PY - 2008/04/09/ UR - http://dx.doi.org/10.1093/bioinformatics/btn131 ER -