Li Huai, Sun Yu, Zhan Ming
Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, NIH, Baltimore, MD 21224, USA.
Bioinformatics. 2007 Feb 15;23(4):473-9. doi: 10.1093/bioinformatics/btl640. Epub 2006 Dec 22.
We address the problem of identifying gene transcriptional modules from gene expression data by proposing a new approach. Genes mostly interact with each other to form transcriptional modules for context-specific cellular activities or functions. Unraveling such transcriptional modules is important for understanding biological network, deciphering regulatory mechanisms and identifying biomarkers.
The proposed algorithm is based on two-stage matrix decomposition. We first model microarray data as non-linear mixtures and adopt the non-linear independent component analysis to reduce the non-linear distortion and separate the data into independent latent components. We then apply the probabilistic sparse matrix decomposition approach to model the 'hidden' expression profiles of genes across the independent latent components as linear weighted combinations of a small number of transcriptional regulator profiles. Finally, we propose a general scheme for identifying gene modules from the outcomes of the matrix decomposition.
The proposed algorithm partitions genes into non-mutually exclusive transcriptional modules, independent from expression profile similarity measurement. The modules contain genes with not only similar but different expression patterns, and show the highest enrichment of biological functions in comparison with those by other methods. The usefulness of the algorithm was validated by a yeast microarray data analysis.
The software is available upon request to the authors.
我们提出了一种新方法来解决从基因表达数据中识别基因转录模块的问题。基因大多相互作用以形成针对特定环境的细胞活动或功能的转录模块。阐明此类转录模块对于理解生物网络、解读调控机制和识别生物标志物至关重要。
所提出的算法基于两阶段矩阵分解。我们首先将微阵列数据建模为非线性混合物，并采用非线性独立成分分析来减少非线性失真，将数据分离为独立的潜在成分。然后，我们应用概率稀疏矩阵分解方法，将跨独立潜在成分的基因“隐藏”表达谱建模为少量转录调节因子谱的线性加权组合。最后，我们提出了一种从矩阵分解结果中识别基因模块的通用方案。
所提出的算法将基因划分为非互斥的转录模块，与表达谱相似性测量无关。这些模块包含不仅具有相似表达模式而且具有不同表达模式的基因，并且与其他方法相比，显示出最高的生物功能富集度。通过酵母微阵列数据分析验证了该算法的有效性。
可向作者索取该软件。
