Metagenes and molecular pattern discovery using matrix factorization
Metagenes and molecular pattern discovery using matrix factorization
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DOI：
10.1073/pnas.0308531101
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发表时间：
2004-03-23
影响因子：
11.1
通讯作者：
Mesirov, JP
中科院分区：
文献类型：
--
作者：
Brunet, JP;Tamayo, P;Mesirov, JP
我们在这里描述了非负矩阵分解（NMF）的使用，这是一种基于零件分解的算法，可以将表达数据的尺寸从数千个基因降低到少数metagenes。结合模型选择机制，适用于任何随机聚类算法的工作，NMF是一种有效的方法，用于识别不同的分子模式，并为类发现提供了强大的方法。我们证明了NMF从与癌症相关的微阵列数据中恢复有意义的生物学信息的能力。 NMF似乎比其他方法具有优势，例如分层聚类或自组织图。我们发现它对基因或初始条件的先验选择不太敏感，并且能够检测复杂生物系统中基因表达的替代或上下文依赖性模式。这种能力类似于文本中的语义多义，为鲁棒分子模式发现提供了一种通用方法。
We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.