Exploratory Analysis of Gene Expression Data

描述（由申请人提供）：微阵列是一种令人兴奋的新技术， 技术允许同时收集基因表达数据， 上千个基因最近的发展使这项技术 在不久的将来，越来越多的研究人员可以使用它， 微阵列技术可能变得足够可靠和便宜， 常规医疗用途。微阵列产生的数据可用于 区分细胞类型（例如，癌症与非癌症），对于许多人来说， 其他用途。研究人员才刚刚开始探索 这项新技术。 微阵列数据分析的主要困难在于其相对 丰度：收集了数千个基因的基因表达信息， 实验数量相对较少。简单的假设检验必须考虑 对于多重测试，以及标准的统计方法，如多维 缩放和聚类分析必须用于非常大的矩阵， 可能太大了，甚至无法放入计算机内存。 在这里，我们建议开发创新的软件，用于分析 从微阵列。其中一些软件将基于现有的自适应 使用B样条及其张量积的模型构建程序，但 在新的环境中工作。其他分析的算法将是创新的， 他们将能够在非常大的“距离”上工作的感觉 矩阵由此产生的软件模块应该在 微阵列实验数据的分析。 拟定商业应用： 随着信号提取和技术的其他方面的成熟，微阵列方法的使用将在未来几年内呈指数增长。统计分析方法，扩展了一个现成的统计分析软件包，如S-PLUS，也能够处理大量的变量，是由微阵列将非常受欢迎的生物学家和其他研究人员。

DESCRIPTION (provided by applicant): Microarray's are an exciting new technology allowing the simultaneous collection of gene expression data for literally thousands of genes. Recent developments have made this technology accessible to an ever widening group of researchers, and in the near future microarray technology may become reliable enough and inexpensive enough for routine medical use. Data arising from microarray's can be used to differentiate cell types (e.g., cancerous versus noncancerous), and for many other purposes. Researchers are only beginning to explore the potential uses of this new technology. The main difficulty in the analysis of microarray data is its relative abundance: gene expression information for thousands of genes is gathered for a relatively small number of experiments. Simple hypothesis tests must account for multiple testing, and standard statistical methods like multidimensional scaling and cluster analysis must be used on very large matrices, matrices that may be too big to even fit into a computers memory. Here we propose to develop innovative software for the analysis of data arising from microarrays. Some of this software will be based upon existing adaptive model building procedures using B-splines and their tensor products, but employed in a new setting. Algorithms for other analyses will be innovative in the sense that they will be capable of working on very large "distance" matrices. The resulting software module should find a wide range of uses in the analysis of data arising from microarray experiments. PROPOSED COMMERCIAL APPLICATION: Use of microarray methodologies will grow exponentially over the next few years as signal extraction and other aspects of the technology mature. Statistical analysis methods that extend a readily available statistical analysis package such as S-PLUS and are also capable of handling the large number of variables that are produced by microarrays will be very popular among biologists other researchers.