Bioinformatics Vol. 19 no. 17 2003
pages 2302-2307
© 2003 Oxford University Press
Missing-value estimation using linear and non-linear regression with Bayesian gene selection
1 Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, USA, 2 Department of Electrical Engineering, Columbia University, New York, NY 10027, USA and 3 Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
Received on January 17, 2003
; revised on February 23, 2003
; accepted on June 11, 2003
Motivation: Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in use. The problem has two parts: (1) selection of genes for estimation and (2) design of an estimation rule.
Results: We propose Bayesian variable selection to obtain genes to be used for estimation, and employ both linear and nonlinear regression for the estimation rule itself. Fast implementation issues for these methods are discussed, including the use of QR decomposition for parameter estimation. The proposed methods are tested on data sets arising from hereditary breast cancer and small round blue-cell tumors. The results compare very favorably with currently used methods based on the normalized root-mean-square error.
Availability: The appendix is available from http://gspsnap.tamu.edu/gspweb/zxb/missing_zxb/ (user: gspweb; passwd: gsplab).
Contact: edward{at}ee.tamu.edu
* To whom correspondence should be addressed.
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