Braun Rosemary
Biostatistics Division, Department of Preventive Medicine and Northwestern Institute on Complex Systems, Northwestern University, 680 N. Lake Shore Dr., Suite 1400, 60611, Chicago, IL, USA,
Adv Exp Med Biol. 2014;844:153-87. doi: 10.1007/978-1-4939-2095-2_8.
Modern high-throughput assays yield detailed characterizations of the genomic, transcriptomic, and proteomic states of biological samples, enabling us to probe the molecular mechanisms that regulate hematopoiesis or give rise to hematological disorders. At the same time, the high dimensionality of the data and the complex nature of biological interaction networks present significant analytical challenges in identifying causal variations and modeling the underlying systems biology. In addition to identifying significantly disregulated genes and proteins, integrative analysis approaches that allow the investigation of these single genes within a functional context are required. This chapter presents a survey of current computational approaches for the statistical analysis of high-dimensional data and the development of systems-level models of cellular signaling and regulation. Specifically, we focus on multi-gene analysis methods and the integration of expression data with domain knowledge (such as biological pathways) and other gene-wise information (e.g., sequence or methylation data) to identify novel functional modules in the complex cellular interaction network.
现代高通量检测能够详细表征生物样本的基因组、转录组和蛋白质组状态，使我们能够探究调控造血作用或引发血液疾病的分子机制。与此同时，数据的高维度以及生物相互作用网络的复杂性质，在识别因果变异和构建潜在的系统生物学模型方面带来了重大的分析挑战。除了识别明显失调的基因和蛋白质外，还需要采用综合分析方法，以便在功能背景下研究这些单个基因。本章概述了当前用于高维数据统计分析以及细胞信号传导和调控系统水平模型开发的计算方法。具体而言，我们重点关注多基因分析方法，以及将表达数据与领域知识（如生物途径）和其他基因层面信息（如序列或甲基化数据）进行整合，以在复杂的细胞相互作用网络中识别新的功能模块。
