Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors
Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six i...
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Published in | Statistical Applications in Genetics and Molecular Biology Vol. 4; no. 1; pp. 10 - Article10 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Germany
bepress
01.01.2005
De Gruyter |
Online Access | Get full text |
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Summary: | Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred. |
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Bibliography: | ArticleID:1544-6115.1120 istex:B4574B2A5A730122265372A412F54CE4445AF527 sagmb.2005.4.1.1120.pdf ark:/67375/QT4-GLQ7K1B1-G ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1544-6115 2194-6302 1544-6115 |
DOI: | 10.2202/1544-6115.1120 |