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ICN: a normalization method for gene expression data considering the over-expression of informative genes

机译：ICN：考虑信息基因过表达的基因表达数据标准化方法

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摘要

The global increase of gene expression has been frequently established in cancer microarray studies. However, many genes may not deliver informative signals for a given experiment, due to insufficient expression or even non-expression, despite the DNA microarrays massively measuring genes in parallel. Hence the informative gene set, rather than the whole genome, should be more reasonable to represent the genome expression level. We observed that the trend of over-expression for informative genes is more obvious in human cancers, which is to some extent masked using the whole genome without any filtering. Accordingly we proposed a novel normalization method. Informative CrossNorm (ICN), which performs the cross normalization (CrossNorm) on the expression matrix merely containing the informative genes. ICN outperforms other methods with a consistently high precision, F-score, and Matthews correlation coefficient as well as an acceptable recall based on three available spiked-in datasets with ground truth. In addition, nine potential therapeutic target genes for esophageal squamous cell carcinoma (ESCC) were identified using ICN integrated with a protein-protein interaction network, which biologically demonstrates that ICN shows superior performance. Consequently, it is expected that ICN could be applied routinely in cancer microarray studies.
机译：在癌症微阵列研究中经常确定基因表达的全球增加。然而，尽管DNA芯片大量并行地测量基因，但由于表达不足甚至不表达，许多基因可能无法为给定实验传递信息信号。因此，提供信息的基因集而不是整个基因组应该更合理地代表基因组表达水平。我们观察到，信息基因过度表达的趋势在人类癌症中更为明显，这在某种程度上被整个基因组所掩盖，没有任何过滤。因此，我们提出了一种新颖的归一化方法。信息性交叉标准（ICN），仅在包含信息基因的表达矩阵上执行交叉归一化（CrossNorm）。 ICN具有始终如一的高精度，F得分和Matthews相关系数，并且基于具有基础真实性的三个可用加标数据集的可接受召回率优于其他方法。此外，使用整合了蛋白质-蛋白质相互作用网络的ICN，鉴定了食管鳞状细胞癌（ESCC）的九种潜在治疗靶基因，这从生物学上证明了ICN具有优越的性能。因此，预期ICN可常规应用于癌症微阵列研究。

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  • 来源
    《Molecular BioSystems》 |2016年第10期|3057-3066|共10页
  • 作者

    Lixin Cheng; Xuan Wang; Pak-Kan Wong; Kwan-Yeung Lee; Le Li; Bin Xu; Dong Wang; Kwong-Sak Leung;

  • 作者单位

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China;

    College of Pharmacy, Harbin Medical University, Harbin, China;

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China;

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China;

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China;

    School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China;

    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China;

    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China;

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