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.

Clustering Methods for Microarray Data Sets

Fedele G.
2022

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/335267
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