Heterogeneous in situ data: Kernals, Distances and Trees

异构原位数据：核、距离和树

• 批准号: 7596500
• 负责人: SUSAN P HOLMES
• 金额: $ 22.53万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7596500
• 关键词: Bacteria; Breast Cancer Cell; Cells; Charge; Clinical; Collaborations; Collection; Complex; Data; Data Analyses; Detection; Environmental Engineering technology; HIV; Hematologist; Heterogeneity; Image; Image Analysis; Immune; In Situ; Java; Learning; Location; Machine Learning; Metagenomics; Methods; Microscope; Numbers; Patients; Postdoctoral Fellow; Process; Recording of previous events; Sampling; Source; Staining method; Stains; System; Techniques; Trees; Virus; Writing; base; cancer cell; cell type; cellular imaging; data integration; data structure; design; desire; forest; indexing; lymph nodes; microbial; programs; size; soil sampling; statistics; tool; tumor

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.

描述(由申请人提供)：这个项目旨在为生物学家提供新的工具，帮助他们理解复杂的系统，他们对这些系统有不同来源的异质“原位”数据。这些数据呈现出许多级别的异质性，并且与需要合并到综合数据结构中的时空信息和先验信息同时出现。这种协作始于收集过程的设计，并提供了围绕统计资料包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：由新的焦磷酸测序机提供的元基因组数据和序列混合物，以及由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学院的阿尔弗雷德·斯波尔曼收集的微生物土壤样本数据。该方案的重点是将贝叶斯计算应用于样本位置和收集的序列数量的设计，然后使用谱多元方法以表格(而不是汇总)的形式分析多样性指数，从而将数据结构融入到分解中。这些方法也将有助于研究来自焦糖测序的艾滋病毒、细菌、病毒和癌细胞的混合数据。第二项研究与斯坦福大学血液学家彼得·李合作，重点研究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中的数据。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，有效的交互式助推组件向用户提供反复学习过程的可能性，直到达到所需的准确度水平。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。一名博士后将负责将细胞数据与临床病史和来自同一患者的微阵列表达数据结合起来。非均质性将通过基于谱分析、核方法和图形表示的探索性多变量技术来处理。