A Comprehensive Approach to Pre-processing of Affymetrix GeneChip Data

描述（由申请人提供）：微阵列实验允许同时分析不同生物样品中数千种基因表达的差异。这些实验有助于检测不同阶段和类型癌症的细微基因表达变化，并使研究人员能够确定对化疗和其他外部刺激的分子反应。Affymetrix微阵列在生物和医学研究中得到了广泛的应用，因为它具有生产可重复性，便于实验结果的比较。为了获得可信赖的高水平分类和聚类分析，重要的是对探针级数据进行各种预处理步骤，以控制样品处理和阵列杂交的可变性。最终结果的质量取决于用于预处理微阵列数据的算法的有效性。因此，提高微阵列数据分析的质量可以对基础研究和由此产生的医学应用产生重要的广泛影响。在之前对Affymetrix基因芯片(r)数据的分析中，已经发现了几个对高水平结果有影响的重要模式。然而，目前任何流行的数组预处理算法都没有考虑这些模式。例如，对于人类基因组平台，30%的MM探针的强度水平大于其PM对应物，表明存在交叉杂交。此外，PM和MM探针的强度水平高度相关，表明MM探针可能与靶基因非特异性杂交。因此，从PM强度中减去MM强度会导致真实信号的减少，从而使差异表达的基因更难被检测到。该拨款概述了一项数据驱动模型的建议，该模型考虑了交叉杂交和非特异性杂交，用于分析Affymetrix GeneChip (r)品牌阵列。具体来说，该模型将检查观察到的PM强度作为自身荧光、非特异性杂交、交叉杂交和真信号的组合。MM强度将包括前三个分量，因为假设，一旦这些背景分量得到适当估计，只有PM探头将携带真实信号。对这些组成部分分别进行建模将有助于确定每个组成部分的贡献，并有助于在背景校正期间对它们进行解释。这种新的模型驱动方法处理Affymetrix微阵列数据的性能将与常用的算法（如MAS5.0， dChip和RMA）进行比较，使用特征良好的数据集来验证最终模型的准确性。该模型的实施将导致更好的高层次数据分析，并相应地更好地理解基因表达差异对疾病状态或环境变化的反应。基因表达微阵列允许同时确定数千个基因的表达水平，并为许多基础研究领域提供了见解，从决定肿瘤阶段的基因描述到发育过程中重要器官形成过程中的基因表达。该项目旨在通过创建更好的分析方法来从这些数据中提取真实表达值，从而提高使用微阵列数据的实验的可靠性、可重复性和适用性。

DESCRIPTION (provided by applicant): Microarray experiments allow the simultaneous analysis of differences in the expression of thousands of genes in different biological samples. Such experiments have been instrumental in detecting subtle gene expression changes in different stages and types of cancers, and enabling researchers to determine molecular responses to chemotherapy and other external stimuli. Affymetrix microarrays are widely used in biological and medical research because of production reproducibility, which facilitates the comparison of results between experiment runs. In order to obtain high- level classification and clustering analysis that can be trusted, it is important to perform various pre-processing steps on the probe-level data to control for variability in sample processing and array hybridization. The quality of the final results depends on the validity of the algorithm used for preprocessing microarray data. Therefore, improving the quality of the analysis of microarray data can have important wide-ranging effects on basic research and the resulting medical applications. In previous analysis of Affymetrix GeneChip (r) data, several important patterns that have an impact on high- level results have been uncovered. However, none of these patterns are currently considered by any of the popular algorithms for array preprocessing. For example, for the human genome platforms, thirty percent of MM probes have intensity levels that are greater than their PM counterparts, indicating the presence of cross- hybridization. Further, intensity levels of PM and MM probes are highly correlated, indicating that MM probes may be non-specifically hybridizing to the target gene. Thus, subtracting MM intensities from PM intensities, results in a reduction of the true signal, making differentially expressed genes harder to detect. This grant outlines a proposal for a data-driven model that takes into account cross-hybridization and non- specific hybridization for the analysis of Affymetrix GeneChip (r) brand arrays. Specifically, the model will examine observed PM intensities as a combination of autofluorescence, non-specific hybridization, cross- hybridization, and true signal. MM intensities will include the first three components since it is assumed that, once these background components are properly estimated, only the PM probes will carry true signal. Modeling these components separately will facilitate the determination of the contribution of each, and the ability to account for them during background correction. The performance of this new model-driven approach to the processing of Affymetrix microarray data will be evaluated in comparison with commonly used algorithms like MAS5.0, dChip and RMA using well characterized data sets to validate the improved accuracy of the final model. Implementation of this model should lead to better high-level data analysis, and correspondingly a better understanding of gene expression differences in response to disease states or environmental changes. Gene expression microarrays allow the determination of the expression levels of thousands of genes simultaneously, and have given insights into many areas of basic research, from a description of the genes that determine tumor stage, to the genes expressed during formation of vital organs during development. This project seeks to improve reliability, reproducibility, and applicability of experiments using microarray data by creating better analysis approaches for the extraction of true expression values from these data.