Biological impact of missing-value imputation on downstream analyses of gene expression profiles | Zendy

Sunghee Oh | Zendy; Dongwan Kang | Zendy; Guy Brock | Zendy; George C. Tseng | Zendy

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Biological impact of missing-value imputation on downstream analyses of gene expression profiles

Author(s) -

Sunghee Oh,

Dongwan Kang,

Guy Brock,

George C. Tseng

Publication year - 2010

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/btq613

Subject(s) - imputation (statistics) , cluster analysis , mean squared error , statistics , missing data , data mining , computer science , correlation , bayesian probability , hierarchical clustering , mathematics , geometry

Microarray experiments frequently produce multiple missing values (MVs) due to flaws such as dust, scratches, insufficient resolution or hybridization errors on the chips. Unfortunately, many downstream algorithms require a complete data matrix. The motivation of this work is to determine the impact of MV imputation on downstream analysis, and whether ranking of imputation methods by imputation accuracy correlates well with the biological impact of the imputation.

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