Linear model tools for high-throughput gene expression data

用于高通量基因表达数据的线性模型工具

• 批准号: 177628764
• 负责人: Professor Dr. Hans-Peter Piepho
• 金额: --
• 依托单位: Institut für Kulturpflanzenwissenschaften (340)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2010
• 资助国家: 德国
• 起止时间: 2009-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/177628764?language=en
• 关键词: Linear; model; tools; throughput; gene

Data generated from high-throughput platforms in plant-biological research are often analysed by linear model procedures. Checking model assumptions and subsequently transforming data or taking other measures to remedy violations poses a formidable task with these kinds of large datasets because of the need to automate the analysis pipeline as much as possible in order to save computing time and overall time needed for analysis. This project will develop procedures for critically inspecting and testing residuals from linear model fits. Moreover, data transformations and parametric link functions will be investigated regarding their suitability for highthroughput data. Particular emphasis will be given to transcriptome sequencing (RNAseq) using the Illumina sequencing technology and metabolite profiling. The proposed procedures will be investigated by simulation as well as by application to empirical datasets provided by our collaborators. A pipeline implementing these procedures will be developed and made available as an R-package. Computationally intensive components such as Monte Carlo simulation modules will be programmed in a low-level programming language.

植物生物学研究中高通量平台生成的数据通常通过线性模型程序进行分析。检查模型假设并随后转换数据或采取其他措施来纠正违规行为对于此类大型数据集构成了一项艰巨的任务，因为需要尽可能地自动化分析管道，以节省计算时间和分析所需的总体时间。该项目将开发严格检查和测试线性模型拟合残差的程序。此外，还将研究数据转换和参数链接函数对高通量数据的适用性。将特别强调使用 Illumina 测序技术和代谢物分析进行转录组测序 (RNAseq)。所提出的程序将通过模拟以及应用我们的合作者提供的经验数据集进行研究。将开发实施这些程序的管道并作为 R 包提供。计算密集型组件（例如蒙特卡罗模拟模块）将使用低级编程语言进行编程。