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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >VISUALIZING MICROARRAY DATA FOR BIOMARKER DISCOVERY BY MATRIX REORDERING AND REPLICATOR DYNAMICS
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VISUALIZING MICROARRAY DATA FOR BIOMARKER DISCOVERY BY MATRIX REORDERING AND REPLICATOR DYNAMICS

机译：通过矩阵重定序和复制动力学对生物标记物的可视化微阵列数据进行可视化

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摘要

In most microarray data sets, there are often multiple sample classes, which are categorized into the normal or diseased type Traditional feature selection methods consider multiple classes equally without paying attention to the upregulation/downregulation across the normal and diseased classes; while the specific gene selection methods for biomarker discovery particularly consider differential gene expressions across the normal and diseased classes, but ignore the existence of multiple classes More importantly, there are few visualization algorithms to assist biomarker discovery from microarray data. In this paper, to help users visually analyze microarray data and improve biomarker discovery, we propose to employ matrix reordering techniques that have been developed and used in matrix computation In particular, we generalized a well-known population genetic algorithm, namely, replicator dynamics, to reorder a microarray data matrix with multiple classes The new algorithm simultaneously takes into account the global between-class data pattern and local within-class data pattern Our results showed that our matrix reordering algorithm not only provides a visualization method to effectively analyze microarray data on both genes and samples, but also improves the accuracy of classifying the samples.
机译：在大多数微阵列数据集中，通常有多个样本类别，分为正常或患病类型。传统特征选择方法平等地考虑了多个类别，而没有关注正常和患病类别的上调/下调。虽然用于生物标记物发现的特定基因选择方法特别考虑了正常和患病类别之间差异的基因表达，但忽略了多个类别的存在。更重要的是，很少有可视化算法可帮助从微阵列数据中发现生物标记物。在本文中，为了帮助用户直观地分析微阵列数据并改善生物标志物的发现，我们建议采用已开发并用于矩阵计算的矩阵重排序技术。特别是，我们推广了一种众所周知的种群遗传算法，即复制子动力学，重新排序具有多个类别的微阵列数据矩阵新算法同时考虑了全局类间数据模式和局部类内数据模式我们的结果表明，我们的矩阵重排序算法不仅提供了一种可视化方法，可以有效地分析微阵列数据。包括基因和样本，还提高了样本分类的准确性。

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