Analysis of Microarray Gene Expression Data

PI将在位于博尔德的科罗拉多大学的分子、细胞和发育生物学系（MCDB）居住一学年，学习分子生物学，并进行微阵列数据分析（MDA）研究。PI的核心专长是开发适用于特征值问题的数值方法，例如，在大型数据集的主成分分析（PCA）中，以及与典型相关分析（CCA）密切相关的子空间之间的主角度的调查和计算中。PI的团队正在开发用于PCA和CCA的大规模并行软件，可用于数据聚类。 现代微阵列数据提供了大量有用的生物信息，但它们的分析在计算上具有挑战性。 分子生物学家需要快速、可靠和先进的MDA工具来定位负责特定生物过程的基因簇。 本提案的目的是让PI获得所需的背景知识和实践经验以及分子生物学方面的培训，以便与该领域的专家进行互动和合作，开展MDA研究。PI将在MCDB的Min Han实验室进行托管。Min Han实验室参与了前沿研究，通过使用Affirons微阵列芯片技术，通过脂肪酸（FA）信号转导发现对生物体生长和发育的新调节有反应的功能基因簇。这些数据将用于案例研究。拟议研究的目标是应用PI在PCA和CCA方面的专业知识，开发专门为微阵列数据集定制的新型数学算法，包括最近开发的GeneChip平铺阵列。PI将研究谱聚类和双聚类技术的新类别。PI将应用所获得的分子生物学知识，使用真实的微阵列实验数据，在案例研究中对新的PCA和CCA方法进行实验。 PI获得的新知识将使学生的论文研究与计算分子生物学的应用相结合成为可能，使PI能够更有效地监督学生的研究，并用于新课程的开发。如果成功，PI为MDA开发和实施的新PCA和CCA方法将有助于微阵列用于科学发现。

The PI will spend one academic year in residence at the Department of Molecular, Cellular, and Developmental Biology (MCDB) at the University of Colorado at Boulder to study molecular biology, and to perform research in microarray data analysis (MDA). The core expertise of the PI is in development of numerical methods for eigenvalue problems applicable, e.g., to the Principal Component Analysis (PCA) of large data sets, and in investigation and computation of principal angles between subspaces that are closely connected to the Canonical Correlation Analysis (CCA). The PI's team is developing massively parallel software for PCA and CCA that can be used for data clustering. Modern microarray data provide vast amounts of useful biological information, but their analysis is computationally challenging. Molecular biologists need fast, reliable, and advanced MDA tools to locate clusters of genes responsible for specific biological processes. The purpose of this proposal is to allow the PI to gain the needed background and hands-on experience and training in molecular biology to pursue research in MDA in interaction and collaboration with experts in the area. The PI will be hosted in Min Han's laboratory in the MCDB. The Min Han laboratory is involved in cutting-edge research on finding functional gene clusters responsive to the novel regulation of growth and development of an organism through the fatty acid (FA) signaling using Affimetrix microarray chip technology. The data will be used for case studies. The goal of the proposed research is to apply the PI's expertise in PCA and CCA for developing novel mathematical algorithms specifically tailored for microarray datasets, including the recently developed GeneChip tiling arrays. The PI will investigate new classes of spectral clustering and bi-clustering techniques. The PI will apply the acquired knowledge in molecular biology to experiment with the novel PCA and CCA methods on case studies using real microarray experiments data. The new knowledge gained by the PI will make it possible to integrate the students' thesis research with applications in computational molecular biology, enable the PI to supervise students' research more efficiently, and be used for new course development. If successful, the new PCA and CCA methods developed and implemented by the PI for MDA will contribute to microarray use for scientific discovery.