Skip Navigation



Bioinformatics Advance Access published online on January 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth007
Bioinformatics © Oxford University Press 2004; all rights reserved
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
20/6/917    most recent
bth007v1
Right arrow Alert me when this article is cited
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 Ouyang, M.
Right arrow Articles by Georgopoulos, P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ouyang, M.
Right arrow Articles by Georgopoulos, P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Received September 15, 2003
Accepted November 4, 2003

Article

Gaussian mixture clustering and imputation of microarray data

Ming Ouyang 1*, William J. Welsh 2, Panos Georgopoulos 1

1 Environmental and Occupational Health Sciences Institute, UMDNJ - Robert Wood Johnson Medical School and Rutgers, The State University of New Jersey, 170 Frelinghuysen Road, Piscataway, NJ 08854
2 Department of Pharmacology, UMDNJ - Robert Wood Johnson Medical School and Informatics Institute, University of Medicine and Dentistry of New Jersey, 675 Hoes Lane, Piscataway, NJ 08854

* To whom correspondence should be addressed. E-mail: ouyang{at}.delio.rutgers.edu.


  Abstract

Motivation: 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.

Results: Two evaluation metrics of imputation accuracy are employed. First, the root mean squared error measures the difference between the true values and the imputed values. Second, the number of misclustered genes measures the difference between clustering with true values and that with imputed values; it examines the bias introduced by imputation to clustering. The Gaussian mixture clustering with model averaging imputation is superior to all other imputation methods, according to both evaluation metrics, on both time-series (correlated) and non-time series (uncorrelated) data sets.

Availability: Matlab code is available on request from the authors.


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
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.-W. Kim, K.-Y. Lee, K. H. Lee, and D. Lee
Towards clustering of incomplete microarray data without the use of imputation
Bioinformatics, January 1, 2007; 23(1): 107 - 113.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
X. Gan, A. W.-C. Liew, and H. Yan
Microarray missing data imputation based on a set theoretic framework and biological knowledge
Nucleic Acids Res., March 20, 2006; 34(5): 1608 - 1619.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
J. Tuikkala, L. Elo, O. S. Nevalainen, and T. Aittokallio
Improving missing value estimation in microarray data with gene ontology
Bioinformatics, March 1, 2006; 22(5): 566 - 572.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
I. Scheel, M. Aldrin, I. K. Glad, R. Sorum, H. Lyng, and A. Frigessi
The influence of missing value imputation on detection of differentially expressed genes from microarray data
Bioinformatics, December 1, 2005; 21(23): 4272 - 4279.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
R. Jornsten, H.-Y. Wang, W. J. Welsh, and M. Ouyang
DNA microarray data imputation and significance analysis of differential expression
Bioinformatics, November 15, 2005; 21(22): 4155 - 4161.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
M. S. B. Sehgal, I. Gondal, and L. S. Dooley
Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data
Bioinformatics, May 15, 2005; 21(10): 2417 - 2423.
[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.