Browse > Article

Poor Correlation Between the New Statistical and the Old Empirical Algorithms for DNA Microarray Analysis  

Kim, Ju Han (SNUBI: Seoul National University Biomedical Informatics, Seoul National University College of Medicine)
Kuo, Winston P. (Childern's Hospital Informatics Program, and Division of Endocrinology, Department of Medicine, Harvard Medical School, Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School)
Kong, Sek-Won (Cardiovascular Division, Beth Israel Deaconess Medical Center, and Department of Medicine, Harvard Medical School)
Ohno-Machado, Lucila (Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School)
Kohane, Isaac S. (Childern's Hospital Informatics Program, and Division of Endocrinology, Department of Medicine, Harvard Medical School)
Publication Information
Genomics & Informatics / v.1, no.2, 2003 , pp. 87-93 More about this Journal
Abstract
DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. $Affymetrix^{(R)}$ recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0$^{(R)}$. Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.
Keywords
gene expression; DNA microarray; correlation; method comparison;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Edgar, R., Domrachev, M., Lash, A.E (2002) Gene Expression Omnibus: NCBI geneexpression and hybridization array data repository. Nucleic AcidsResearch Jan1;30(1 ):207-210
2 Gardiner-Garden, M.andLittlejohn, T.G.. (2001) A comparison of microarray databases. Brief. Bioinform. May;2(2):143-158   DOI   ScienceOn
3 Tamayo, P., Sionim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R. (1999) Interpreting patterns of gene expression with self-organizing maps: methods andapplication to hematopoietic differentiation. Proc. Nat!. Acad. Sci. USA., 96:2907-2912   DOI   ScienceOn
4 Strausberg, R.L. (2001) The Cancer Genome Anatomy Project: newresources for reading the molecular signatures of cancer. J. Patho!. Sep;195(1):31-40   DOI   ScienceOn
5 Iyer, V.R., Eisen, M.B., Ross, D.T., Schuler, G., Moore, T., Lee, J.C.F., Trent, J.M., Staudt, L.M., Hudson, J. Jr., Boguski, M.S., Lashkari, D., Shalon, D., Botstein, D., Brown, P.O. (1999) The transcriptional program in the response of human fibroblasts to serum. Science 283(5398):83-87   DOI   PUBMED   ScienceOn
6 Aach, J., Rindone, W., Church, G.M. (2000) Systematic management and analysis of yeast gene expression data. Genome Res. Apr;10(4):431-445   DOI   ScienceOn
7 Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, CA, Causton, H.C., Gaasterland, T., Glenisson, P., Holstege, F.C., Kim, I.F., Markowitz, V., Matese, J.C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., Vingron, M. (2001) Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. Dec;29(4):365-371   DOI   ScienceOn
8 Heller, R.A., Schena, M., Chai, A., Shalon, D., Bedilion, T., Gilmore, J., Woolley, DE, Davis, RW. (1997) Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. Proc. Natl. Acad. Sci. U. S.A. 94(6):2150-2155   DOI   ScienceOn
9 Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M. (1999) ystematic determination of genetic network architecture. Nat. Genet. 22:281-285   DOI   ScienceOn
10 Kuo, W.P., Jenssen, T.K., Butte, A.J., Ohno-Machado, L., Kohane, I.S. (2002) nalysis of matched mRNA measurements from two different microarray technologies. Bioinformatics Mar;18(3):405-412   DOI   ScienceOn
11 Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D. (1998) Cluster analysis and display of genome-wide expression patterns, Proc. Nat!. Acad. Sci. U. S.A., 95(25):14863-14868   DOI   ScienceOn
12 Bittner, M., Meltzer, P., Chen, Y., Jiang, Y., Settor, E, Hendrix, M., Radmacher, M., Simon, R., Yakhini, Z., Ben-Dor, A., Sampas, N., Dougherty, E., Wang, E, Marincola, F., Gooden, C., Lueders, J., Glatfelter, A., Pollock, P., Carpten, J., Gillanders, E, Leja, D., Dietrich, K., Beaudry, C., Berens, M., Alberts, D., Sondak, V. (2000) Molecular classification of cutaneous malignant melanoma by geneexpression profiling. Nature, 406(3):536-540   DOI
13 Rockman, H.A., Ross, R.S., Harris, A.N., Knowlton, K.U., Steinhelper, M.E., Field, L.J., Ross, J. Jr., Chien, K.R. (1991) Segregation of atrial-specific and inducible expression of an atrial natriuretic factor transgene in an in vivo murine model of cardiac hypertrophy. Proc. Nat!. Acad. Sci. U. S. A., Sep 15;88(18):8277-8281
14 DeRisi, J., Penland, L., Brown, P.O., Bittner, M.L., Meltzer, P.S., Ray, M.,Chen, Y., Su,YA, Trent, J.M. (1996) Useof a cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet. 14(4):457-460   DOI   ScienceOn