潜在秩变化检测法用于非线性效应下拼接芯片的分析
Latent rank change detection for analysis of splice-junction microarrays with nonlinear effects
摘要：
基因转录物的选择性剪接极大地扩展了基因组的功能能力，并且某些剪接异构体可以指示特定的疾病状态，例如癌症。剪接点微阵列询问数以千计的剪接点，但数据分析是困难的，并且容易出错，因为与差异基因表达分析相比增加了复杂性。我们目前的秩变化检测（RCD）作为一种方法来识别差异剪接事件的基础上一个简单的概率模型比较两个或两个以上的竞争异构体的过度或代表性不足。RCD具有优于常用方法的优点，因为它对由于微阵列测量中的非线性趋势引起的假阳性误差具有鲁棒性。此外，RCD不依赖于剪接异构体的先验知识，但它利用了互斥连接的固有结构，并且它在概念上可推广到其他类型的剪接阵列或RNA-Seq。RCD特别确定了生物学上重要的情况下，当一个剪接点变得或多或少普遍相比，其他相互排斥的连接。实例数据来自用Agilent微阵列测定的成胶质细胞瘤肿瘤的不同细胞系。
英文摘要：
Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over- or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.
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