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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Poisson-Based Self-Organizing Feature Maps and Hierarchical Clustering for Serial Analysis of Gene Expression Data
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Poisson-Based Self-Organizing Feature Maps and Hierarchical Clustering for Serial Analysis of Gene Expression Data

机译：基于泊松的自组织特征图和层次聚类用于基因表达数据的序列分析

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摘要

Serial analysis of gene expression (SAGE) is a powerful technique for global gene expression profiling, allowing simultaneous analysis of thousands of transcripts without prior structural and functional knowledge. Pattern discovery and visualization have become fundamental approaches to analyzing such large-scale gene expression data. From the pattern discovery perspective, clustering techniques have received great attention. However, due to the statistical nature of SAGE data {i.e., underlying distribution), traditional clustering techniques may not be suitable for SAGE data analysis. Based on the adaptation and improvement of self-organizing maps and hierarchical clustering techniques, this paper presents two new clustering algorithms, namely, PoissonS and PoissonHC, for SAGE data analysis. Tested on synthetic and experimental SAGE data, these algorithms demonstrate several advantages over traditional pattern discovery techniques. The results indicate that, by incorporating statistical properties of SAGE data, PoissonS and PoissonHC, as well as a hybrid approach (neuro-hierarchical approach) based on the combination of PoissonS and PoissonHC, offer significant improvements in pattern discovery and visualization for SAGE data. Moreover, a user-friendly platform, which may improve and accelerate SAGE data mining, was implemented. The system is freely available on request from the authors for nonprofit use
机译：基因表达的序列分析（SAGE）是一种用于进行全局基因表达谱分析的强大技术，可在无需事先了解结构和功能知识的情况下，同时分析成千上万个转录本。模式发现和可视化已成为分析此类大规模基因表达数据的基本方法。从模式发现的角度来看，聚类技术受到了极大的关注。但是，由于SAGE数据的统计性质（即基础分布），传统的聚类技术可能不适合SAGE数据分析。基于对自组织图的改进和改进以及层次聚类技术，提出了两种新的聚类算法，即PoissonS和PoissonHC，用于SAGE数据分析。经过对合成和实验性SAGE数据的测试，这些算法证明了优于传统模式发现技术的多个优势。结果表明，通过结合SAGE数据的统计特性，PoissonS和PoissonHC以及基于PoissonS和PoissonHC的组合的混合方法（神经分层方法），可以显着改善SAGE数据的模式发现和可视化。此外，实施了一个用户友好的平台，该平台可以改善和加速SAGE数据挖掘。该系统可应作者的要求免费提供以供非营利使用

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