TY - JOUR
T1 - ELB-Q
T2 - A new method for improving the robustness in DNA microarray image quantification
AU - Ma, Marc Q.
AU - Zhang, Kai
AU - Wang, Hui Yun
AU - Shih, Frank Y.
N1 - Funding Information:
Dr. Shih was the recipient of the Research Initiation Award from the National Science Foundation in 1991, the Honorable Mention Award from the International Pattern Recognition Society for Outstanding Paper, and the Best Paper Award in the International Symposium on Multimedia Information Processing. He has received several awards for distinguished research at the New Jersey Institute of Technology.
Funding Information:
Manuscript received February 17, 2006; revised June 21, 2006. This work was supported in part by a grant from the New Jersey Institute of Technology. M. Q. Ma is with the Department of Computer Science and the Center of Applied Mathematics and Statistics, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: [email protected]). K. Zhang was with the Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102 USA. He is now with Inform Technologies LLC, New York, NY 10022 USA (e-mail: [email protected]). H.-Y. Wang is with the Public Health Research Institute, Newark, NJ 07103 USA (e-mail: [email protected]). F. Y. Shih is with the Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TITB.2006.884360
PY - 2007/9
Y1 - 2007/9
N2 - Reliable and robust quantification of signal intensities is a critical step in microarray-based biomedical studies. However, traditional techniques for microarray image processing would face significant challenges if the number of pixels used for the quantification of the local background and the foreground decreases dramatically. We have developed a new method, ELB-Q, which, by design, is well suited for the image quantification of microarrays with very high density of spot layout (large number of spots arranged in unit area). In ELB-Q, a large extended local background (ELB) interspot region excluding those "noise of the background" pixels is used for estimating the local background, and the quantification of spot intensities (mean and median) in the putative target spot regions is performed after further excluding background pixels in these areas based on the cutoff values established during the ELB calculation. ELB-Q takes advantage of the abundant spatial information around each spot of interest, makes no assumption of the shape and size of the spots, and needs no sophisticated adjustment. We show results of image processing using ELB-Q on both the simulated data and real DNA microarrays, which compare favorably in robustness and accuracy against those obtained with GenePix Pro 6.0 (Axon Instruments, 1999) and the Markov random field (MRF) modeling approach (O. Demirkaya et al., Bioinformatics, vol. 21, pp. 2994-3000, 2005). The ELB-Q software is developed in Matlab, and is available upon request.
AB - Reliable and robust quantification of signal intensities is a critical step in microarray-based biomedical studies. However, traditional techniques for microarray image processing would face significant challenges if the number of pixels used for the quantification of the local background and the foreground decreases dramatically. We have developed a new method, ELB-Q, which, by design, is well suited for the image quantification of microarrays with very high density of spot layout (large number of spots arranged in unit area). In ELB-Q, a large extended local background (ELB) interspot region excluding those "noise of the background" pixels is used for estimating the local background, and the quantification of spot intensities (mean and median) in the putative target spot regions is performed after further excluding background pixels in these areas based on the cutoff values established during the ELB calculation. ELB-Q takes advantage of the abundant spatial information around each spot of interest, makes no assumption of the shape and size of the spots, and needs no sophisticated adjustment. We show results of image processing using ELB-Q on both the simulated data and real DNA microarrays, which compare favorably in robustness and accuracy against those obtained with GenePix Pro 6.0 (Axon Instruments, 1999) and the Markov random field (MRF) modeling approach (O. Demirkaya et al., Bioinformatics, vol. 21, pp. 2994-3000, 2005). The ELB-Q software is developed in Matlab, and is available upon request.
KW - Extended local background (ELB)
KW - High-density spot layout
KW - Local background
KW - Microarray image processing
KW - Quantification
KW - Segmentation
KW - Spotted microarray
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U2 - 10.1109/TITB.2006.884360
DO - 10.1109/TITB.2006.884360
M3 - Article
C2 - 17912974
AN - SCOPUS:34548671868
SN - 1089-7771
VL - 11
SP - 574
EP - 582
JO - IEEE Transactions on Information Technology in Biomedicine
JF - IEEE Transactions on Information Technology in Biomedicine
IS - 5
ER -