Collaborative Research: Bayesian ANOVA for Microarrays

DNA微阵列可以提供对癌症等疾病分期进展过程中发生的遗传变化的深入了解。 准确识别这些变化具有重要的治疗和诊断意义。 然而，由于信息量巨大，对这些数据进行统计分析具有挑战性。 有了新的微阵列技术，就有可能对每一个被分析的组织样本测量近60,000个转录本的表达。 为了正确理解疾病进展的过程，我们收集了所有可能的生物学阶段的表达值，因此这些问题中的参数数量可能是数十万，甚至数百万。 高维性给基于标准方差分析的双样本Z检验扩展带来了理论问题，这是一种检测两组差异表达基因的流行方法。此外，专注于控制错误检测率的标准方法主要适用于更简单的实验设计;此外，这些方法往往保守，在多组环境中预计会更糟。 这项工作介绍了一种新的方法，称为贝叶斯方差分析的微阵列（BAM），可靠地检测差异表达的基因在复杂的实验设置。 该方法依赖于一个高维变量选择方法，该方法利用了一个重新标度的穗和板层次模型。BAM被证明是风险最优的总数量ofmisclassified基因。 这种风险最优的确切机制在理论上被描述为选择性收缩效应。理论指导图形设备的发展适应性最佳基因选择。 凯斯西储大学爱尔兰癌症中心收集的一个大型多阶段结肠癌微阵列库作为该方法的试验平台。 与此同时，开发了基于JAVA的BAM软件，该软件使用菜单驱动的GUI，并包括最少数量的用户指定的调整参数，从而使其他分子生物学实验室使用该软件变得用户友好。DNA微阵列允许高通量分析癌症等疾病的潜在遗传决定因素。 现在典型的情况是，每个组织样本分析的转录本有近60,000个。 这些信息可以潜在地提供关于哪些基因参与癌症的分期发展的信息，以及指示新的治疗和诊断靶点。 然而，统计推断，以确定感兴趣的基因是challengingdue大量的统计测试运行。 标准方法采用ANOVA检验统计量，并且容易出现高错误检测。 误检率控制方法往往过于保守，不能自然地扩展到更复杂的多阶段实验设计。 本工作介绍了一种新的方法，称为Bayesian ANOVA for Microarrays（BAM），它可以在多组实验设计中可靠地检测差异表达的基因。 该方法采用了一种特殊的层次模型，该模型体现了基因选择的神谕般的行为-也就是说，最终只有那些真正差异表达的基因被选中。 这种行为的原因在理论上delineatein这项研究，和理论指导的novelgraphical设备的发展，自适应最佳基因选择在realmicroarray数据集。 凯斯西储大学爱尔兰癌症中心收集的一个大型多阶段结肠癌微阵列库作为该方法的试验平台，也为了解结肠癌疾病过程提供了巨大的机会，这是一个具有重要医学意义的主题。 虽然结肠癌有一个明确的演变定义的临床阶段，很少有人知道它的分子演变。 与此同时，是基于JAVA的软件的开发，使用菜单驱动的GUI，具有最小数量的用户指定的调整参数，从而使其可行的端口软件的分子生物学实验室积极使用在分析其他疾病的过程和潜在的其他高通量数据源。

DNA microarrays can provide insight into genetic changes occurringduring stagewise progression of diseases like cancer. Accurateidentification of these changes has significant therapeutic anddiagnostic implications. Statistical analysis of such data is howeverchallenging due to the sheer volume of information. With newmicroarray technology it is possible to measure expressions on nearly60,000 transcripts for each sample of tissue analyzed. To properlyunderstand the evolution of a progressive disease, expression valuesare collected over all possible biological stages, thus the number ofparameters in such problems can be in the hundreds of thousands, oreven millions. The high dimensionality presents theoretical problemsto standard ANOVA-based extensions of two-sample Z-tests, a popularmethod for detecting differentially expressed genes in two groups.Additionally, standard approaches that focus on controlling falsedetection rates primarily apply to simpler experimental designs;moreover these approaches tend to be conservative and are expected tobe worse in multigroup settings. This work introduces a newmethodology called Bayesian ANOVA for Microarrays (BAM) for reliablydetecting differentially expressed genes in complex experimentalsettings. The method rests on a high dimensional variable selectionmethod that exploits a rescaled spike and slab hierarchical model.BAM is shown to be risk optimal in terms of the total number ofmisclassified genes. The exact mechanisms for this risk optimalityare theoretically delineated as a selective shrinkage effect. Theoryguides development of graphical devices for adaptive optimal geneselection. A large multistage colon cancer microarray repositorycollected at the Ireland Cancer Center of Case Western ReserveUniversity serves as a testbed for the methods. In parallel to thisis the development of JAVA-based software for implementing BAM.Software uses a menu driven GUI and includes a minimal number ofuser-specified tuning parameters, thus making it user friendly for useby other molecular biology laboratories.DNA microarrays allow for high throughput analysis of potentialgenetic determinants of diseases like cancer. It is now typical tohave expression on nearly 60,000 transcripts for each sample of tissueanalyzed. This information can potentially provide information aboutwhich genes are involved in stagewise development of cancer as well asindicate novel therapeutic and diagnostic targets. However,statistical inferences to identify interesting genes is challengingdue to the large number of statistical tests that are run. Standardapproaches employ ANOVA test statistics and are prone to high falsedetections. False detection rate control methods tend to be overlyconservative and do not extend naturally to more complex multistageexperimental designs. This work introduces a new methodology calledBayesian ANOVA for Microarrays (BAM) which reliably detectsdifferentially expressed genes in multigroup experimental designsettings. The method employs a special hierarchical model thatimparts an oracle like behaviour for gene selection --- that is,ultimately, only those truly differentially expressing genes areselected. The reasons for this behaviour are theoretically delineatedin this research, and the theory guides the development of novelgraphical devices for adaptively optimal gene selection in realmicroarray datasets. A large multistage colon cancer microarrayrepository collected at the Ireland Cancer Center of Case WesternReserve University serves as a testbed for the methods and alsoprovides a tremendous opportunity to understand the colon cancerdisease process, a topic which is of great medical importance. Whilecolon cancer has a well defined evolution defined by clinical stage,very little is known about its molecular evolution. In parallel tothis, is the development of JAVA-based software using a menu drivenGUI having a minimal number of user-specified tuning parameters, thusmaking it feasible to port the software to molecular biologylaboratories for active use in analysis of other disease processes andpotentially other high throughput sources of data.