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Effects of threshold choice on biological conclusions reached during analysis of gene expression by DNA microarrays

机译:阈值选择对DNA芯片分析基因表达过程中得出的生物学结论的影响

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Global analysis of gene expression by using DNA microarrays is employed increasingly to search for differences in biological properties between normal and diseased tissue. In such studies, expression that deviates from defined thresholds commonly is used for creating genetic signatures that characterize disease vs. normality. Although it is axiomatic that the threshold parameters applied to microarray analysis will alter the contents of such genetic signatures, the extent to which threshold choice can affect the fundamental conclusions made from microarray-based studies has not been elucidated. We used GABRIEL (Genetic Analysis By Rules Incorporating Expert Logic), a platform of knowledge-based algorithms for the global analysis of gene expression, together with conventional statistical approaches, to examine the sensitivity of conclusions to threshold choice in recently published microarray-based studies. An analysis of the effects of threshold decisions in one of these studies [Ramaswamy, S., Ross, K. N., Lander, E. S. & Golub, T. R. (2003) Nat. Genet. 33, 49-54], which arrived at the important conclusion that the metastatic potential of primary tumors is encoded by the bulk of cells in the tumor, is the focus of this article. We discovered that support for this conclusion highly depends on the threshold used to create gene expression signatures. We also found that threshold choice dramatically affected the gene function categories represented nonrandomly in signatures. Our results suggest that the robustness of biological conclusions made by using microarray analysis should be routinely assessed by examining the validity of the conclusions by using a range of threshold parameters.
机译:通过使用DNA芯片对基因表达进行全局分析的方法越来越多地用于寻找正常组织和患病组织之间生物学特性的差异。在此类研究中,通常使用偏离定义阈值的表达来创建表征疾病与正常性的遗传特征。尽管毫无疑问地,应用于微阵列分析的阈值参数将改变此类遗传标记的内容,但是尚未阐明阈值选择可影响基于微阵列的研究得出的基本结论的程度。我们使用GABRIEL(通过规则结合专家逻辑进行遗传分析),一种基于知识的算法来对基因表达进行全局分析,并结合传统的统计方法,以检查最近发表的基于微阵列的研究中结论对阈值选择的敏感性。 。在其中一项研究中对阈值决策的影响进行了分析[Ramaswamy,S.,Ross,K. N.,Lander,E. S.&Golub,T. R.(2003)Nat。基因[33,49-54],得出了一个重要的结论,即原发性肿瘤的转移潜力是由肿瘤中大量细胞编码的,这是本文的重点。我们发现对这一结论的支持高度依赖于用于创建基因表达签名的阈值。我们还发现阈值选择会极大地影响签名中非随机表示的基因功能类别。我们的结果表明,应通过使用一系列阈值参数检查结论的有效性来常规评估使用微阵列分析得出的生物学结论的稳健性。

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