Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA, Corporate Research and Development, Qualcomm Technologies Inc., San Diego, CA 92121, USA, Department of Chemical Engineering and Department of Electrical Engineering, Texas A&M University at Qatar, Doha Qatar.
Bioinformatics. 2013 Oct 1;29(19):2410-8. doi: 10.1093/bioinformatics/btt433. Epub 2013 Aug 11.
Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications.
We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA.
The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA
网络成分分析（NCA）是一种有效的重构转录因子活性（TFA）的方法，它利用基因表达数据和可用的关于转录因子（TF）-基因调控的先验信息。大多数当代算法要么存在不一致性和可靠性差的缺点，要么存在计算复杂度高的问题。此外，现有的算法不具备抵抗微阵列数据中异常值的能力。因此，需要稳健且计算效率高的算法来实现实际应用。
我们提出了 ROBust Network Component Analysis（ROBNCA），这是一种新颖的迭代算法，它明确地对微阵列数据中的可能异常值进行建模。ROBNCA 算法的一个吸引人的特点是为估计连接矩阵推导出了一个闭式解，这在以前的贡献中是没有的。ROBNCA 算法与 FastNCA 和非迭代 NCA（NI-NCA）进行了比较。ROBNCA 算法可以更准确地估计 TF 活性谱以及 TF-基因控制强度矩阵，而不管在合成数据中存在不同的噪声、相关性和/或异常值数量如何，FastNCA 和 NI-NCA 都无法做到这一点。ROBNCA 算法还在酿酒酵母数据和大肠杆菌数据上进行了测试，结果表明它优于现有的算法。ROBNCA 算法的运行时间与 FastNCA 相当，比 NI-NCA 快数百倍。
ROBNCA 软件可在 http://people.tamu.edu/∼amina/ROBNCA 获得。
