Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
BioMediTech Institute, Tampere University, Tampere, Finland.
Methods Mol Biol. 2022;2401:121-146. doi: 10.1007/978-1-0716-1839-4_9.
The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.
微阵列提供的数据量使研究人员有机会深入研究生物系统的复杂性。然而，这种数据的嘈杂和极高维度的性质带来了重大的挑战。微阵列允许对跨越不同相互作用层的数千个分子对象进行并行测量。为了能够发现隐藏的模式，已经提出了最不同的分析技术。在这里，我们描述了基本的方法来处理微阵列数据集的分析，这些方法集中于（子）组发现的任务。
