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首页> 外文会议>2016 IEEE International Conference on Advances in Computer Applications >A comparative study of statistical and clustering techniques based meta-analysis to identify differentially expressed genes
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A comparative study of statistical and clustering techniques based meta-analysis to identify differentially expressed genes

机译：基于统计和聚类技术的荟萃分析识别差异表达基因的比较研究

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Gene expression data from microarray experiments is widely used for large scale gene expression analysis which facilitates the investigation of fundamental biological processes at molecular level. Such an investigation may be helpful for various biological purposes including disease diagnosis and prognosis, biomarker detection, differentially expressed gene detection, and predicting survival rate of patients. However, data from microarray experiments come with less sample size and thus have limited statistical power for any further biological investigation. To address this problem, researchers are now relying on a more powerful technique called meta-analysis, an integrated analysis of existing data from different but related independent studies. Gene expression data reveal that genes are normally expressed in related functionalities and exhibit hidden patterns, which on elucidating often offers a great opportunity to enhance biological understanding at molecular level. Clustering can play an important role to identify natural and interesting patterns in the underlying gene expression data. In this paper, we explore the applications of three well known clustering techniques i.e. k-Means, Partitioning Around Medoids (PAM) and Hierarchical Clustering (HC) to perform meta-analysis for differentially expressed gene detection in microarray gene expression data. The results of clustering techniques are compared with the results of various statistical meta-analysis techniques, which prove clustering as a robust alternative technique for meta-analysis of gene expression data.
机译：来自微阵列实验的基因表达数据被广泛用于大规模基因表达分析，这有助于在分子水平上研究基本的生物学过程。这样的研究对于多种生物学目的可能是有帮助的，包括疾病诊断和预后，生物标志物检测，差异表达基因检测以及预测患者的存活率。然而，来自微阵列实验的数据具有较小的样本量，因此对于任何进一步的生物学研究而言，统计能力有限。为了解决这个问题，研究人员现在依赖于一种更强大的技术，称为元分析，它是对来自不同但相关的独立研究的现有数据的综合分析。基因表达数据表明基因通常在相关功能中表达并表现出隐藏的模式，这在阐明后通常为在分子水平上增强生物学理解提供了巨大的机会。聚类可以在识别基础基因表达数据中的自然和有趣模式方面发挥重要作用。在本文中，我们探索了三种众所周知的聚类技术的应用，即k-均值，围绕类固醇的分区（PAM）和分层聚类（HC）在微阵列基因表达数据中进行差异表达基因检测的荟萃分析。将聚类技术的结果与各种统计荟萃分析技术的结果进行比较，证明聚类是基因表达数据荟萃分析的可靠替代技术。

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