A Report on the Inter-Gene Correlations in cDNA Microarray Data Sets |
Kim, Byung-Soo
(Department of Applied Statistics, Yonsei University)
Jang, Jee-Sun (Korea Economic Research Institute) Kim, Sang-Cheol (Department of Applied Statistics, Yonsei University) Lim, Jo-Han (Department of Statistics, Seoul National University) |
1 | Qui, X., Brooks, A. I., Klebanov, L. and Yakovlev, A. (2005a). The effects of normalization on the correlation structure of microarray data, BMC Bioinformatics, 6, 120 DOI ScienceOn |
2 | Qui, X., Klebanov, L. and Yakovlev, A. (2005b). Correlation between gene expression levels and limitations of the empirical Bayes methodology for finding differentially expressed genes, Statistical Applications in Genetics and Molecular Biology, 4, Ariticle 34 |
3 | Qui, X., Xiao, Y., Gordon, A. and Yakovlev, A. (2006). Assessing stability of gene selection in microarray data analysis, BMC Bioinformatics, 7, 50 DOI |
4 | Qui, X. and Yakovlev, A. (2006). Some comments on instability of false discovery rate estimation, Journal of Bioinformatics and Computational Biology, 4, 1057-1068 DOI ScienceOn |
5 | Stolovitzky, G. (2003). Gene selection in microarray data: The elephant, the blind men and our algorithm, Current Opinions in Structural Biology, 13, 370-376 DOI ScienceOn |
6 | Yang,S., Jeung, H. C., Jeong, H. J., Choi, Y. H., Kim, J. E., Jung, J. J., Rha, S. Y., Yang, W. I. and Chung, H. C. (2007a). Identification of genes with correlated patterns of variations in DNA copy number and gene expression level in gastric cancer, Genomics, 89, 451-459 DOI ScienceOn |
7 | Yang, S., Shin, J., Park, K. H., Jeung, H-C., Rha, S. Y., Noh, S. H., Yang, W. I. and Chung, H. C. (2007b). Molecular basis of the difference between normal and tumor tissues of gastric cancer, Biochimica et Biophysica Acta, 1772, 1033-1040 DOI ScienceOn |
8 | Efron, B. (2003). Robbins, empirical Bayes and microarrays, The Annals of Statistics, 31, 366-378 DOI ScienceOn |
9 | Efron, B. (2004). Large-scale simultaneous hypothesis testing: The choice of a null hypothesis, Journal of the American Statistical Association, 99, 96-104 DOI ScienceOn |
10 | Efron, B. (2007). Correlation and large-scale simultaneous significance testing, Journal of the American Statistical Association, 102, 93-103 DOI ScienceOn |
11 | Efron, B., Tibshirani, R., Storey, J. D. and Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment, Journal of the American Statistical Association, 96, 1151-1160 DOI ScienceOn |
12 | Frantz,S. (2005). An array of problems, Nature Reviews Drug Discovery, 4, 302-303 DOI ScienceOn |
13 | Kim, B. S., Kim, I., Lee, S., Kim, S., Rha, S. Y. and Chung, H. C. (2005). Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer, Bioinformatics, 21, 517-528 DOI ScienceOn |
14 | Klebanov, L., Jordan, C. and Yakovlev, A. (2006). A new type of stochastic dependence revealed in gene expression data, Statistical Applications in Genetics and Molecular Biology, 5, Ariticle 7 |
15 | Klebanov, L. and Yakovlev, A. (2006). Treating expression levels of different genes as a sample in microarray data analysis: Is it worth a risk?, Statistical Applications in Genetics and Molecular Biology, 5, Ariticle 9 |
16 | Klebanov, L. and Yakovlev, A. (2007). Diverse correlation structures in gene expression data and their utility in improving statistical inference, The Annals oj Applied Statistics, 1, 538-559 DOI |
17 | Marshall, E. (2004). Getting the noise out of gene arrays, Science, 306, 630-631 DOI ScienceOn |