Skip Navigation



Bioinformatics Advance Access published online on December 23, 2005
Bioinformatics, doi:10.1093/bioinformatics/btk019
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
22/5/566    most recent
btk019v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Tuikkala, J.
Right arrow Articles by Aittokallio, T.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Tuikkala, J.
Right arrow Articles by Aittokallio, T.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
W. Torres-Garcia, W. Zhang, G. C. Runger, R. H. Johnson, and D. R. Meldrum
Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins
Bioinformatics, August 1, 2009; 25(15): 1905 - 1914.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
R. Varshavsky, A. Gottlieb, D. Horn, and M. Linial
Unsupervised feature selection under perturbations: meeting the challenges of biological data
Bioinformatics, December 15, 2007; 23(24): 3343 - 3349.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. Tao, L. Sam, J. Li, C. Friedman, and Y. A. Lussier
Information theory applied to the sparse gene ontology annotation network to predict novel gene function
Bioinformatics, July 1, 2007; 23(13): i529 - i538.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
D. S. V. Wong, F. K. Wong, and G. R. Wood
A multi-stage approach to clustering and imputation of gene expression profiles
Bioinformatics, April 15, 2007; 23(8): 998 - 1005.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
D. Hua and Y. Lai
An ensemble approach to microarray data-based gene prioritization after missing value imputation
Bioinformatics, March 15, 2007; 23(6): 747 - 754.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
J. S. Verducci, V. F. Melfi, S. Lin, Z. Wang, S. Roy, and C. K. Sen
Microarray analysis of gene expression: considerations in data mining and statistical treatment
Physiol Genomics, May 16, 2006; 25(3): 355 - 363.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.