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Bioinformatics Advance Access published online on December 23, 2005
Bioinformatics, doi:10.1093/bioinformatics/btk019
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 14, 2005
Revised December 20, 2005
Accepted December 20, 2005

Article

Improving missing value estimation in microarray data with gene ontology

Johannes Tuikkala 1 *, Laura Elo 2, Olli S. Nevalainen 3, and Tero Aittokallio 2

1 Department of Information Technology, University of Turku, Lemminkäisenkatu 14A, FIN-20520, Finland; Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14A, FIN-20520, Finland
2 Department of Mathematics, University of Turku, FIN-20014 Finland; Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14A, FIN-20520, Finland; Turku Centre for Biotechnology, Tykistökatu 6, FIN-20521, Finland
3 Department of Information Technology, University of Turku, Lemminkäisenkatu 14A, FIN-20520, Finland; Turku Centre for Computer Science (TUCS), Lemminkäisenkatu 14A, FIN-20520, Finland; Turku Centre for Biotechnology, Tykistökatu 6, FIN-20521, Finland

* To whom correspondence should be addressed.
Johannes Tuikkala, E-mail: jotatu{at}utu.fi


  Abstract

Motivation: Gene expression microarray experiments produce data sets with frequent missing expression values. Accurate estimation of missing values is an important prerequisite for efficient data analysis as many statistical and machine learning techniques either require a complete data set or their results are significantly dependent on the quality of such estimates. A limitation of the existing estimation methods for microarray data is that they use no external information but the estimation is based solely on the expression data. We hypothesized that utilizing a priori information on functional similarities available from public databases facilitates the missing value estimation.

Results: We investigated whether semantic similarity originating from gene ontology (GO) annotations could improve the selection of relevant genes for missing value estimation. The relative contribution of each information source was automatically estimated from the data using an adaptive weight selection procedure. Our experimental results in yeast cDNA microarray data sets indicated that by considering GO information in the k-nearest neighbor algorithm we can enhance its performance considerably, especially when the number of experimental conditions is small and the percentage of missing values is high. The increase of performance was less evident with a more sophisticated estimation method. We conclude that even a small proportion of annotated genes can provide improvements in data quality significant for the eventual interpretation of the microarray experiments.

Availability: Java and Matlab codes are available on request from the authors.

Supplementary material: available online at http://users.utu.fi/jotatu/GOImpute.html.


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