Blind source separation and the analysis of microarray data
Blind source separation and the analysis of microarray data
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DOI：
10.1089/cmb.2004.11.1090
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发表时间：
2004-12-01
影响因子：
1.7
通讯作者：
Torrésani, B
中科院分区：
文献类型：
--
作者：
Chiappetta, P;Roubaud, MC;Torrésani, B
我们开发了一种基于盲源分离技术的基因表达数据探索性分析方法。这种方法利用高阶统计来识别表达谱的线性模型，描述为“独立来源”的线性组合。因此，它产生了“基本表达模式”（“来源”），这可能被解释为潜在的调控途径。对所获得的来源的进一步分析表明，它们通常以少量特异性共表达或抗表达基因为特征。此外，表达谱到估计来源上的投影通常提供显著的病症聚类。该算法依赖于随机初始化的“独立成分分析”的大量运行，然后搜索“共识源”。“然后，它提供了独立来源的估计，以及对其稳健性的评估。两个数据集（即乳腺癌数据和枯草芽孢杆菌硫代谢数据）上获得的结果表明，一些获得的基因家族对应于众所周知的共调节基因家族，这验证了所提出的方法。
We develop an approach for the exploratory analysis of gene expression data, based upon blind source separation techniques. This approach exploits higher-order statistics to identify a linear model for ( logarithms of) expression profiles, described as linear combinations of "independent sources." As a result, it yields "elementary expression patterns" ( the "sources"), which may be interpreted as potential regulation pathways. Further analysis of the so-obtained sources show that they are generally characterized by a small number of specific coexpressed or antiexpressed genes. In addition, the projections of the expression profiles onto the estimated sources often provides significant clustering of conditions. The algorithm relies on a large number of runs of "independent component analysis" with random initializations, followed by a search of "consensus sources." It then provides estimates for independent sources, together with an assessment of their robustness. The results obtained on two datasets ( namely, breast cancer data and Bacillus subtilis sulfur metabolism data) show that some of the obtained gene families correspond to well known families of coregulated genes, which validates the proposed approach.