Department of Law, Economics and Sociology, University Magna Graecia of Catanzaro, Catanzaro, Italy.
Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Rende (CS), Italy.
Methods Mol Biol. 2022;2401:249-261. doi: 10.1007/978-1-0716-1839-4_16.
Microarrays are experimental methods that can provide information about gene expression and SNP data that hold great potential for new understanding, driving advances in functional genomics and clinical and molecular biology. Cluster analysis is used to analyze data that are not a priori to contain any specific subgroup. The goal is to use the data itself to recognize meaningful and informative subgroups. Also, cluster analysis helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. This chapter outlines a collection of cluster methods suitable for the analysis of microarray data sets.
微阵列是一种实验方法，可以提供有关基因表达和 SNP 数据的信息，这些数据具有新的理解的巨大潜力，推动了功能基因组学和临床及分子生物学的发展。聚类分析用于分析事先不包含任何特定亚组的数据。其目的是使用数据本身来识别有意义和有用的亚组。此外，聚类分析有助于数据减少的目的，揭示隐藏的模式，并生成关于基因和表型之间关系的假设。本章概述了一系列适用于微阵列数据集分析的聚类方法。
