Clustering Methods for Microarray Data Sets

Giuseppe Agapito; Giuseppe Fedele

doi:10.1007/978-1-0716-1839-4_16

Clustering Methods for Microarray Data Sets

Methods Mol Biol. 2022:2401:249-261. doi: 10.1007/978-1-0716-1839-4_16.

Authors

Giuseppe Agapito¹, Giuseppe Fedele²

Affiliations

¹ Department of Law, Economics and Sociology, University Magna Graecia of Catanzaro, Catanzaro, Italy.
² Department of Informatics, Modeling, Electronics and Systems Engineering, University of Calabria, Rende (CS), Italy. giuseppe.fedele@unical.it.

PMID: 34902133
DOI: 10.1007/978-1-0716-1839-4_16

Abstract

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.

Keywords: Data analysis; Edit distance; Microarray; Multiparameterized edit distance; Sequence alignment; Unsupervised learning.

MeSH terms

Algorithms
Cluster Analysis
Gene Expression
Gene Expression Profiling
Genomics
Microarray Analysis*
Oligonucleotide Array Sequence Analysis