Heterogeneous in situ data: Kernals, Distances and Trees

描述（由申请人提供）：该项目旨在为生物学家提供新的工具，以帮助他们了解复杂的系统，他们有不同来源的异构“原位”数据。这些数据呈现出许多层次的异质性，并与需要纳入综合数据结构的时空和先验信息同时出现。 这种合作始于收集过程的设计，并提供围绕统计数据包R编写的数据集成和分析工具以及用JAVA编写的交互式图像分析程序GEMEDENT。 该项目集中于两种特定类型的异质数据：新型焦磷酸测序仪提供的宏基因组数据和序列混合物，以及自动显微镜提供的细胞图像数据。 第一类异质数据是由斯坦福大学土木与环境工程系的Alfred Spormann收集的微生物土壤样本数据。该提案的重点是应用贝叶斯计算的样本位置和收集的序列数的设计，然后使用光谱多元方法分析多样性指数的表（而不是摘要），从而将数据结构的分解。这些方法也将是有用的，在研究的混合物数据从焦磷酸测序艾滋病毒，细菌，病毒和癌细胞。 第二项研究与斯坦福大学的血液学家Peter Lee合作，重点关注免疫细胞与乳腺癌之间的相互作用。我们将分析染色淋巴结的显微镜图像数据。集成的图像分析系统能够从染色图像中自动检测许多不同细胞类型的位置和大小。随机森林已被纳入图像分析系统和有效的交互式提升组件提供了用户的可能性，以重复学习过程，直到达到所需的准确度。这些数据使我们能够推断肿瘤和免疫细胞之间的空间和动态相互作用。博士后研究员将负责将细胞数据与来自同一患者的临床病史和微阵列表达数据相结合。异质性将通过使用基于谱分析、核方法和图形表示的探索性多变量技术来处理。

DESCRIPTION (provided by applicant): This project aims to provide biologists with new tools to help them understand complex systems for which they have different sources of heterogeneous 'in situ' data. These data present many levels of heterogeneity and come concurrently with spatio-temporal and prior information that need to be incorporated into integrated data structures. This collaboration starts with the design of the collection process and provides tools for data integration and analysis written around the statistics package R and an interactive image analysis program GEMEDENT written in JAVA. The project concentrates on two specific types of heterogeneous data: metagenomic data and sequence mixtures provided by the new pyrosequencing machines and cell image data provided by automated microscopes. The first type of heterogeneous data are microbial soil sample data collected by Alfred Spormann from Civil and Environmental Engineering at Stanford. The proposal focuses on applying Bayesian computations in the design of sample locations and number of sequences collected and then using spectral multivariate methods to analyze diversity indices as tables (instead of summaries), thus incorporating the data structure into the decompositions. These methods will also be useful in the study of mixture data from pyrosequencing HIV, bacteria, viruses and cancer cells. The second study focuses on the interaction between immune cells and breast cancer in a collaboration with Peter Lee, hematologist at Stanford. We will analyze data from microscope images of stained lymph nodes. An integrated image analysis system enables the automatic detection of the location and size of many different cell types from stained images. Random forests have been incorporated into the image analysis system and an effective interactive boosting component provides the user with the possibility to iterate the learning process until a desired level of accuracy is attained. These data enable us to infer the spatial and dynamic interaction between the tumors and the immune cells. A postdoctoral fellow will be in charge of combining the cell data with the clinical history and the micro-array expression data from the same patient. The heterogeneity will be dealt with by using exploratory multivariate techniques based on spectral analysis, kernel methods and graphical representations.