Gaussian mixture clustering and imputation of microarray data | Zendy

Ming Ouyang | Zendy; William J. Welsh | Zendy; Panos G. Georgopoulos | Zendy

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Gaussian mixture clustering and imputation of microarray data

Author(s) -

Ming Ouyang,

William J. Welsh,

Panos G. Georgopoulos

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bth007

Subject(s) - imputation (statistics) , missing data , cluster analysis , data mining , computer science , statistics , gaussian , matlab , mean squared error , mathematics , physics , quantum mechanics , operating system

In microarray experiments, missing entries arise from blemishes on the chips. In large-scale studies, virtually every chip contains some missing entries and more than 90% of the genes are affected. Many analysis methods require a full set of data. Either those genes with missing entries are excluded, or the missing entries are filled with estimates prior to the analyses. This study compares methods of missing value estimation.

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