Collaborative Research: Bayesian ANOVA for Microarrays

DNA微阵列可以让我们深入了解癌症等疾病阶段性发展过程中的基因变化。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是具有挑战性的。使用新的微阵列技术，可以测量每个分析组织样本的近60,000个转录本的表达。为了正确地理解一种进行性疾病的演变，在所有可能的生物学阶段收集表达值，因此在这类问题中参数的数量可以达到数十万，甚至数百万。高维度提出了理论问题，标准的基于方差分析的扩展双样本z检验，一种流行的方法来检测差异表达的基因在两组。此外，专注于控制误检率的标准方法主要适用于更简单的实验设计；此外，这些方法往往是保守的，预计在多群体环境下会更糟。这项工作引入了一种新的方法，称为微阵列贝叶斯方差分析（BAM），用于在复杂的实验环境中可靠地检测差异表达基因。该方法基于一种高维变量选择方法，该方法利用了重新缩放的尖峰和平板分层模型。就错误分类基因的总数而言，BAM被证明是风险最佳的。这种风险最优性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，以适应最优的遗传选择。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存储库作为该方法的测试平台。与此同时，开发用于实现BAM的基于java的软件。软件使用菜单驱动的GUI，并包括一个最小数量的用户指定的调整参数，从而使其用户友好的使用其他分子生物学实验室。DNA微阵列允许对癌症等疾病的潜在遗传决定因素进行高通量分析。现在，在分析的每个组织样本中，有近6万个转录本表达是典型的。这些信息可以潜在地提供有关哪些基因参与癌症的分阶段发展的信息，并指出新的治疗和诊断靶点。然而，由于进行了大量的统计测试，通过统计推断来识别有趣的基因是具有挑战性的。标准方法采用方差分析检验统计量，容易出现高误检。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。这项工作引入了一种新的方法，称为微阵列贝叶斯方差分析（BAM），它在多组实验设计设置中可靠地检测差异表达基因。该方法采用了一种特殊的分层模型，该模型模拟了基因选择的神谕行为——也就是说，最终只有那些真正表达差异的基因才会被选择。本研究从理论上描述了这种行为的原因，该理论指导了在实际微阵列数据集中进行自适应最佳基因选择的小说设备的发展。凯斯西储大学爱尔兰癌症中心收集的大型多阶段结肠癌微阵列存储库作为该方法的测试平台，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话题。虽然结肠癌有一个明确的临床分期，但对其分子进化知之甚少。与此同时，开发基于java的软件，使用菜单驱动的gui，具有最少数量的用户指定的调优参数，因此可以将软件移植到分子生物学实验室，用于分析其他疾病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.