Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

Abstract

This research provides a new way to measure error in microarray data in order to improve gene expression analysis. Microarray data contains many sources of error. In order to glean information about mRNA expression levels, the true signal must first be segregated from noise. This research focuses on the variation that can be captured at the spot level in cDNA microarray images. Variation at other levels, due to differences at the array, dye, and block levels, can be corrected for by a variety of existing normalization procedures. Two signal quality estimates that capture the reliability of each spot printed on a microarray are described. A parametric estimate of within-spot variance, referred to here as σ²_spot, assumes that pixels follow a normal distribution and are spatially correlated. A non-parametric estimate of error, called the mean square prediction error (MSPE), assumes that spots of high quality possess pixels that are similar to their neighbors. This paper will provide a framework to use either spot quality measure in downstream analysis, specifically as weights in regression models. Using these spot quality estimates as weights can result in greater efficiency, in a statistical sense, when modeling microarray data.
- microarrays,
- signal quality,
- prediction,
- measurement error models

FullText(HTML)

References(29)

References

[1]	Bergemann, T.L. (2004). Image Analysis and Signal Extraction from cDNA Microarrays. Dissertation (University of Washington), pp.120–130.
[2]	Bergemann, T.L., Zhao, L.P. Signal quality measurements for cDNA microarray data IEEE/ACM Trans. Comput. Biol. Bioinform., 9 (2008),p. 1
[3]	Bergemann, T.L., Laws, R.J., Quiaoit, F. et al. A statistically driven approach for image segmentation and signal extraction in cDNA microarrays J. Comput. Biol., 11 (2004),pp. 695-713
[4]	Dondrup, M., Hueser, A.T., Mertens, D. et al. An evaluation framework for statistical tests on microarray data J. Biotechnol., 140 (2009),pp. 18-26
[5]	Efron, B., Tibshirani, R., Storey, J.D. et al. Empirical Bayes analysis of a microarray experiment J. Amer. Stat. Assoc., 96 (2001),pp. 1151-1160
[6]	Eisenstein, M. Quality control Nature, 442 (2006),pp. 1067-1170
[7]	Glasbey, C.A., Khondoker, M.R. Efficiency of functional regression estimators for combining multiple laser scans of cDNA microarrays Biom. J., 51 (2009),pp. 45-55
[8]	Golkari, S., Gilbert, J., Ban, T. et al. QTL-specific microarray gene expression analysis of wheat resistance to Fusarium head blight in Sumai-3 and two susceptible NILs Genome, 52 (2009),pp. 409-418
[9]	Handran, S., Zhai, J.Y.
[10]	Hastie, T., Tibshirani, R., Friedman, J.
[11]	Imbeaud, S., Auffray, C. The 39 steps in gene expression profiling: critical issues and proposed best practices for microarray experiments Drug Discov. Today, 10 (2005),pp. 1175-1182
[12]	Lee, M.T.
[13]	Liang, K.Y., Zeger, S.L. Longitudinal data analysis for discrete and continuous outcomes Biometrics, 42 (1986),pp. 121-130
[14]	Liu, X., Lin, K.K., Andersen, B. et al. Including probe-level uncertainty in model-based gene expression clustering BMC Bioinformatics, 8 (2007),p. 98
[15]	Moinfar, F. Is ‘basal-like’ carcinoma of the breast a distinct clinicopathological entity? A critical review with cautionary notes Pathobiology, 75 (2008),pp. 119-131
[16]	Murie, C., Woody, O., Lee, A.Y. et al. Comparison of small n statistical tests of differential expression applied to microarrays BMC Bioinformatics, 10 (2009),p. 45
[17]	Patterson, T.A., Lobenhofer, E.K., Fulmer-Smentek, S.B. et al. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project Nat. Biotechnol., 24 (2006),pp. 1140-1150
[18]	Paustian, M.L., Zhu, X., Sreevatsan, S. et al. BMC Genomics, 9 (2008),p. 135
[19]	Pitman, E.J.G. (1948). Nonparametric statistical inference. Lecture Notes (Institute of Statistics, University of North Carolina, Chapel Hill).
[20]	R Development Core Team (2009). R Foundation for Statistical Computing. (Vienna, Austria: {ISBN} 3-900051-07-0) URL: http://www.R-project.org.
[21]	Ritchie, M.E., Diyagama, D., Neilson, J. et al. Empirical array quality weights in the analysis of microarray data BMC Bioinformatics, 7 (2006),pp. 261-276
[22]	Schena, M., Shalon, D., Davis, R.W. et al. Quantitative monitering of gene expression patterns with a complementary DNA microarray Science, 270 (1995),pp. 467-470
[23]	Stangegaard, M. Gene expression analysis using Agilent DNA microarrays Methods Mol. Biol., 529 (2009),pp. 133-145
[24]	Storey, J.D., Taylor, J.E., Siegmund, D. Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: a unified approach J. R. Stat. Soc. Series B Stat. Methodol., 66 (2004),pp. 187-205
[25]	Tran, P.H., Peiffer, D.A., Shin, Y. Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals Nucleic Acids Res., 30 (2002),pp. E54-E62
[26]	Wang, M., Qi, X., Zhao, S. et al. BMC Genomics, 10 (2009),p. 215
[27]	Wang, Y.H., Bower, N.I., Reverter, A. et al. Gene expression patterns during intramuscular fat development in cattle J. Anim. Sci., 87 (2009),pp. 119-130
[28]	Yang, Y. Use of genomic DNA as reference in DNA microarrays Methods Mol. Biol., 544 (2009),pp. 439-450
[29]	Zhao, L.P., Prentice, R., Breeden, L. Statistical modeling of large microarray data sets to identify stimulus-response profiles Proc. Natl. Acad. Sci. USA, 98 (2001),pp. 5631-5636

Relative Articles

[1]	Boxun Zhang, Xuan Zhang, Zhen Luo, Jixiang Ren, Xiaotong Yu, Haiyan Zhao, Yitian Wang, Wenhui Zhang, Weiwei Tian, Xiuxiu Wei, Qiyou Ding, Haoyu Yang, Zishan Jin, Xiaolin Tong, Jun Wang, Linhua Zhao. Microbiome and metabolome dysbiosis analysis in impaired glucose tolerance for the prediction of progression to diabetes mellitus[J]. Journal of Genetics and Genomics. doi: 10.1016/j.jgg.2023.08.005
[2]	Rong Qiao, Feifei Di, Jun Wang, Yujie Wei, Tian Xu, Liping Dai, Wanjian Gu, Baohui Han, Rongxi Yang. Identification of FUT7 hypomethylation as the blood biomarker in the prediction of early-stage lung cancer[J]. Journal of Genetics and Genomics, 2023, 50(8): 573-581. doi: 10.1016/j.jgg.2023.02.014
[3]	Yuxue Li, Gang Xie, Yuguo Zha, Kang Ning. GAN-GMHI: a generative adversarial network with high discriminative power for microbiome-based disease prediction[J]. Journal of Genetics and Genomics. doi: 10.1016/j.jgg.2023.03.009
[4]	Yuyang Zhang, Haoyu Wang, Jing Liu, Junlin Li, Qing Zhang, Bixia Tang, Zhihua Zhang. Delta.EPI: a probabilistic voting-based enhancer-promoter interaction prediction platform[J]. Journal of Genetics and Genomics, 2023, 50(7): 519-527. doi: 10.1016/j.jgg.2023.02.006
[5]	Liangbing Fang, Liuyang Ma, Shaolu Zhao, Ruijie Cao, Guiai Jiao, Peisong Hu, Xiangjin Wei. Alanine aminotransferase (OsAlaAT1) modulates nitrogen utilization, grain yield, and quality in rice[J]. Journal of Genetics and Genomics, 2022, 49(5): 510-513. doi: 10.1016/j.jgg.2022.02.028
[6]	Bo Li, Ya-Qiu Li, Dongdong Zhao, Jie Yang, Yan-He Ma, Chang-Hao Bi, Xue-Li Zhang. Sequence motifs and prediction model of GBE editing outcomes based on target library analysis and machine learning[J]. Journal of Genetics and Genomics, 2022, 49(3): 254-257. doi: 10.1016/j.jgg.2021.11.007
[7]	Weijun Guo, Hanqing Liu, Yifan Wang, Pingxian Zhang, Dongwei Li, Tuoyu Liu, Qian Zhang, Liwen Yang, Li Pu, Jian Tian, Xiaofeng Gu. SMOC: a smart model for open chromatin region prediction in rice genomes[J]. Journal of Genetics and Genomics, 2022, 49(5): 514-517. doi: 10.1016/j.jgg.2022.02.012
[8]	Xiaotong Han, Tianzi Liu, Xiaohu Ding, Jialin Liu, Xingyan Lin, Decai Wang, Moeen Riaz, Paul N. Baird, Zhi Xie, Yuan Cheng, Yi Li, Yuki Mori, Masahiro Miyake, Hengtong Li, Ching-Yu Cheng, Changqing Zeng, Kyoko Ohno-Matsui, Xiangtian Zhou, Fan Liu, Mingguang He. Identification of novel loci influencing refractive error in East Asian populations using an extreme phenotype design[J]. Journal of Genetics and Genomics, 2022, 49(1): 54-62. doi: 10.1016/j.jgg.2021.08.011
[9]	Fangyoumin Feng, Bihan Shen, Xiaoqin Mou, Yixue Li, Hong Li. Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine[J]. Journal of Genetics and Genomics, 2021, 48(7): 540-551. doi: 10.1016/j.jgg.2021.03.007
[10]	Chuanbo Huang, Weili Yang, Junpei Wang, Yuan Zhou, Bin Geng, Georgios Kararigas, Jichun Yang, Qinghua Cui. The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes[J]. Journal of Genetics and Genomics, 2018, 45(7): 389-397. doi: 10.1016/j.jgg.2018.07.002
[11]	Lu Qiao, Yajun Yang, Pengcheng Fu, Sile Hu, Hang Zhou, Shouneng Peng, Jingze Tan, Yan Lu, Haiyi Lou, Dongsheng Lu, Sijie Wu, Jing Guo, Li Jin, Yaqun Guan, Sijia Wang, Shuhua Xu, Kun Tang. Genome-wide variants of Eurasian facial shape differentiation and a prospective model of DNA based face prediction[J]. Journal of Genetics and Genomics, 2018, 45(8): 419-432. doi: 10.1016/j.jgg.2018.07.009
[12]	Chuan-Chao Wang, Ling-Xiang Wang, Rukesh Shrestha, Shaoqing Wen, Manfei Zhang, Xinzhu Tong, Li Jin, Hui Li. Convergence of Y Chromosome STR Haplotypes from Different SNP Haplogroups Compromises Accuracy of Haplogroup Prediction[J]. Journal of Genetics and Genomics, 2015, 42(7): 403-407. doi: 10.1016/j.jgg.2015.03.008
[13]	Yunfei Ma, Haibing Xie, Xuman Han, David M. Irwin, Ya-Ping Zhang. QcReads: An Adapter and Quality Trimming Tool for Next-Generation Sequencing Reads[J]. Journal of Genetics and Genomics, 2013, 40(12): 639-642. doi: 10.1016/j.jgg.2013.11.001
[14]	Zhen Pan, Yang Zhao, Yuan Zheng, Juntao Liu, Xiangning Jiang, Yan Guo. A High-Throughput Method for Screening Arabidopsis Mutants with Disordered Abiotic Stress-Induced Calcium Signal[J]. Journal of Genetics and Genomics, 2012, 39(5): 225-235. doi: 10.1016/j.jgg.2012.04.002
[15]	Lian Wang, Danling Pan, Xihao Hu, Jinyu Xiao, Yangyang Gao, Huifang Zhang, Yan Zhang, Juan Liu, Shanfeng Zhu. BiodMHC: an online server for the prediction of MHC class II-peptide binding affinity[J]. Journal of Genetics and Genomics, 2009, 36(5): 289-296. doi: 10.1016/S1673-8527(08)60117-4
[16]	Aidong Zhou, Jianlin Zhou, Liping Yang, Mingjun Liu, Hong Li, Su Xu, Mei Han, Jian Zhang. A nuclear localized protein ZCCHC9 is expressed in cerebral cortex and suppresses the MAPK signal pathway[J]. Journal of Genetics and Genomics, 2008, 35(8): 467-472. doi: 10.1016/S1673-8527(08)60064-8
[17]	Chao Tang, Xiaolong Shi, Xiujie Li, Zuhong Lu. DNA sequencing by synthesis with degenerate primers[J]. Journal of Genetics and Genomics, 2008, 35(9): 545-551. doi: 10.1016/S1673-8527(08)60074-0
[18]	Lingyun Zou, Zhengzhi Wang, Jiaomin Huang. Prediction of Subcellular Localization of Eukaryotic Proteins Using Position-Specific Profiles and Neural Network with Weighted Inputs[J]. Journal of Genetics and Genomics, 2007, 34(12): 1080-1087. doi: 10.1016/S1673-8527(07)60123-4
[19]	Hao Wang, Jishuai Zhang, Qiang Sun, Xiao Yang. Altered Gene Expression in Articular Chondrocytes of Smad3ex8/ex8 Mice, Revealed by Gene Profiling Using Microarrays[J]. Journal of Genetics and Genomics, 2007, 34(8): 698-708. doi: 10.1016/S1673-8527(07)60079-4
[20]	Volodymyr Dvornyk, Yaozhong Liu, Yan Lu, Hui Shen, Joan M Lappe, Robert R Recker, Hongwen Deng, Shufeng Lei. Effect of Menopause on Gene Expression Profiles of Circulating Monocytes: A Pilot in vivo Microarray Study[J]. Journal of Genetics and Genomics, 2007, 34(11): 974-983. doi: 10.1016/S1673-8527(07)60110-6