A statistical method for identifying differential gene-gene co-expression patterns
A statistical method for identifying differential gene-gene co-expression patterns
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DOI：
10.1093/bioinformatics/bth379
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发表时间：
2004-11-22
期刊：
影响因子：
5.8
通讯作者：
Zhao, HY
中科院分区：
文献类型：
--
作者：
Lai, YL;Wu, BL;Zhao, HY
动机：为了了解癌症的病因，探索细胞从正常状态到癌变状态过程中的分子变化是很重要的。由于基因在细胞过程中相互作用，致癌相关基因可能在不同细胞状态下与其他基因形成差异的共表达模式。在这项研究中，我们开发了一种统计方法来识别不同细胞状态下的差异基因-基因共表达模式。结果：为了有效的模式识别，我们扩展了传统的f统计量，得到了一个包含位置和相关性统计信息的期望条件f统计量（ecf -统计量）。我们还提出了一种数据转换的统计方法。我们的方法应用于前列腺癌研究的微阵列基因表达数据集。对于感兴趣的基因，我们的方法可以选择在不同细胞状态下与该基因具有差异基因-基因共表达模式的其他基因。10个最常被选择的基因包括hepsin、GSTP1和AMACR，它们最近被认为与前列腺癌的发生有关。然而，GSTP1和AMACR基因无法通过单独研究差异基因表达来鉴定。通过肿瘤抑制基因TP53、PTEN和RB1，我们鉴定出了包括hepsin、GSTP1和AMACR在内的7个基因。我们发现与癌症相关的基因在不同的细胞状态下可能与许多其他基因具有不同的基因表达模式。通过发现这样的模式，我们可能能够识别致癌相关的基因。
Motivation: To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states.Results: For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.