774
Views
0
CrossRef citations to date
0
Altmetric
Articles

An evaluation of a novel approach for clustering genes with dissimilar replicates

ORCID Icon, ORCID Icon & ORCID Icon
Pages 7458-7471 | Received 16 Aug 2018, Accepted 14 Oct 2020, Published online: 08 Dec 2020

References

  • Bar-Joseph, Z. 2004. Analyzing time series gene expression data. Bioinformatics (Oxford, England) 20 (16):2493–503. doi:10.1093/bioinformatics/bth283.
  • Bar-Joseph, Z., G. K. Gerber, D. K. Gifford, T. S. Jaakkola, and I. Simon. 2003. Continuous representations of time-series gene expression data. Journal of Computational Biology : A Journal of Computational Molecular Cell Biology 10 (3-4):341–56. doi:10.1089/10665270360688057.
  • Bolshakova, N., and F. Azuaje. 2003. Cluster validation techniques for genome expression data. Signal Processing 83 (4):825–33.
  • Bremnes, R., R. Veve, E. Gabrielson, F. R. Hirsch, A. Baron, L. Bemis, R. M. Gemmill, H. A. Drabkin, and W. A. Franklin. 2002. High-throughput tissue microarray analysis used to evaluate biology and prognostic significance of the e-cadherin pathway in non-small-cell lung cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 20 (10):2417–28. doi:10.1200/JCO.2002.08.159.
  • Celeux, G., O. Martin, and C. Lavergne. 2005. Mixture of linear mixed models for clustering gene expression profiles from repeated microarray experiments. Statistical Modelling: An International Journal 5 (3):243–67.
  • Cho, R. J., M. J. Campbell, E. A. Winzeler, L. Steinmetz, A. Conway, L. Wodicka, T. G. Wolfsberg, A. E. Gabrielian, D. Landsman, D. J. Lockhart, et al. 1998. A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2 (1):65–73.
  • Chu, S., J. DeRisi, M. Eisen, J. Mulholland, D. Botstein, P. O. Brown, and I. Herskowitz. 1998. The transcriptional program of sporulation in budding yeast. Science (New York, N.Y.) 282 (5389):699–705. doi:10.1126/science.282.5389.699.
  • Cinar, O., O. Ilk, and C. Iyigun. 2018. Clustering of short time-course gene expression data with dissimilar replicates. Annals of Operations Research 263 (1-2):405–28.
  • Cooke, E. J., R. S. Savage, P. D. Kirk, R. Darkins, and D. L. Wild. 2011. Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements. BMC Bioinformatics 12 (1):399 doi:10.1186/1471-2105-12-399.
  • Core Team, R. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Déjean, S., P. G. Martin, A. Baccini, and P. Besse. 2007. Clustering time-series gene expression data using smoothing spline derivatives. EURASIP Journal on Bioinformatics and Systems Biology 2007 (1):1–10.
  • Deza, M. M., and E. Deza. 2009. Encyclopedia of distances. In Encyclopedia of distances, 1–583. Berlin, Heidelberg: Springer.
  • Eisen, M. B., P. T. Spellman, P. O. Brown, and D. Botstein. 1998. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America 95 (25):14863–8. doi:10.1073/pnas.95.25.14863.
  • Ernst, J., G. J. Nau, and Z. Bar-Joseph. 2005. Clustering short time series gene expression data. Bioinformatics 21 (Suppl 1):i159–i168.
  • Gan, G., C. Ma, and J. Wu. 2007. Data clustering: Theory, algorithms, and applications. Philadelphia, PA: SIAM; Alexandria, VA: ASA.
  • Greenman, C. D., G. Bignell, A. Butler, S. Edkins, J. Hinton, D. Beare, S. Swamy, T. Santarius, L. Chen, S. Widaa, et al. 2010. Picnic: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics (Oxford, England) 11 (1):164–75. doi:10.1093/biostatistics/kxp045.
  • Hackstadt, A. J., and A. M. Hess. 2009. Filtering for increased power for microarray data analysis. BMC Bioinformatics 10 (1):11. doi:10.1186/1471-2105-10-11.
  • Hakamada, K., M. Okamoto, and T. Hanai. 2006. Novel technique for preprocessing high dimensional time-course data from dna microarray: Mathematical model-based clustering. Bioinformatics (Oxford, England) 22 (7):843–8. doi:10.1093/bioinformatics/btl016.
  • Handl, J., J. Knowles, and D. B. Kell. 2005. Computational cluster validation in post-genomic data analysis. Bioinformatics (Oxford, England) 21 (15):3201–12. doi:10.1093/bioinformatics/bti517.
  • Heard, N. A., C. C. Holmes, D. A. Stephens, D. J. Hand, and G. Dimopoulos. 2005. Bayesian coclustering of anopheles gene expression time series: Study of immune defense response to multiple experimental challenges. Proceedings of the National Academy of Sciences of the United States of America 102 (47):16939–44. doi:10.1073/pnas.0408393102.
  • Heyer, L. J., S. Kruglyak, and S. Yooseph. 1999. Exploring expression data: Identification and analysis of coexpressed genes. Genome Research 9 (11):1106–15. doi:10.1101/gr.9.11.1106.
  • Irigoien, I.,. S. Vives, and C. Arenas. 2011. Microarray time course experiments: Finding profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 (2):464–75. doi:10.1109/TCBB.2009.79.
  • Khan, J., R. Simon, M. Bittner, Y. Chen, S. B. Leighon, T. Pohida, P. D. Smith, Y. Jiang, G. C. Gooden, J. M. Trent, et al. 1998. Gene expression profiling of alveolar rhabdomyosarcoma with cdna microarrays. Genetics 180 (2):821–34.
  • Kim, B. R., L. Zhang, A. Berg, J. Fan, and R. Wu. 2008. A computational approach to the functional clustering of periodic gene-expression profiles. Genetics 180 (2):821–34. doi:10.1534/genetics.108.093690.
  • Lewohl, J. M., L. Wang, M. F. Miles, L. Zhang, P. R. Dodd, and R. A. Harris. 2000. Gene expression in human alcoholism: Microarray analysis of frontal cortex. Alcoholism: Clinical and Experimental Research 24 (12):1873–82. doi:10.1111/j.1530-0277.2000.tb01993.x.
  • Liang, M., A. G. Briggs, E. Rute, A. S. Greene, and A. W. Cowley. 2003. Quantitative assessment of the importance of dye switching and biological replication in cdna microarray studies. Physiological Genomics 14 (3):199–207. doi:10.1152/physiolgenomics.00143.2002.
  • Love, M. I., W. Huber, and S. Anders. 2014. Moderated estimation of fold change and dispersion for rna-seq data with DESeq2. Genome Biology 15 (12):550 doi:10.1186/s13059-014-0550-8.
  • Luan, Y., and H. Li. 2004. Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data. Bioinformatics (Oxford, England) 20 (3):332–9. doi:10.1093/bioinformatics/btg413.
  • Ma, P., C. I. Castillo-Davis, W. Zhong, and J. S. Liu. 2006. A data-driven clustering method for time course gene expression data. Nucleic Acids Res 34 (4):1261–9. doi:10.1093/nar/gkl013.
  • Möller-Levet, C. S., F. Klawonn, K.-H. Cho, H. Yin, and O. Wolkenhauer. 2005. Clustering of unevenly sampled gene expression time-series data. Fuzzy Sets and Systems 152 (1):49–66.
  • Ng, S. K., G. J. McLachlan, K. Wang, L. B. T. Jones, and S. Ng. 2006. A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics (Oxford, England) 22 (14):1745–52. doi:10.1093/bioinformatics/btl165.
  • Peyrot, W. J., Y. Milaneschi, A. Abdellaoui, P. F. Sullivan, J. J. Hottenga, D. I. Boomsma, and B. W. Penninx. 2014. Effect of polygenic risk scores on depression in childhood trauma. The British Journal of Psychiatry : The Journal of Mental Science 205 (2):113–9. doi:10.1192/bjp.bp.113.143081.
  • Ramoni, M. F., P. Sebastiani, and I. S. Kohane. 2002. Cluster analysis of gene expression dynamics. Proceedings of the National Academy of Sciences of the United States of America 99 (14):9121–6. doi:10.1073/pnas.132656399.
  • Richter, J., U. Wagner, J. Kononen, A. Fijan, J. Bruderer, U. Schmid, D. Ackerman, R. Maurer, G. Alund, H. Knönagel, et al. 2000. High-throughput tissue microarray analysis of cyclin e gene amplification and overexpression in urinary bladder cancer. The American Journal of Pathology 157 (3):787–94.
  • Sand, M., M. Skrygan, D. Sand, D. Georgas, T. Gambichler, S. A. Hahn, P. Altmeyer, and F. G. Bechara. 2013. Comparative microarray analysis of microrna expression profiles in primary cutaneous malignant melanoma, cutaneous malignant melanoma metastases, and benign melanocytic nevi. Cell and Tissue Research 351 (1):85–98. doi:10.1007/s00441-012-1514-5.
  • Schena, M., D. Shalon, R. W. Davis, and P. O. Brown. 1995. Quantitative monitoring of gene expression patterns with a complementary dna microarray. Science (New York, N.Y.) 270 (5235):467–70. doi:10.1126/science.270.5235.467.
  • Schliep, A., A. Schönhuth, and C. Steinhoff. 2003. Using hidden markov models to analyze gene expression time course data. Bioinformatics 19 (Suppl 1):i255–i263.
  • Spellman, P. T., G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botstein, and B. Futcher. 1998. Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9 (12):3273–97. doi:10.1091/mbc.9.12.3273.
  • Storey, J. D., W. Xiao, J. T. Leek, R. G. Tompkins, and R. W. Davis. 2005. Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences of the United States of America 102 (36):12837–42. doi:10.1073/pnas.0504609102.
  • Szekely, G. J., and M. L. Rizzo. 2005. Hierarchical clustering via joint between-within distances: Extending ward’s minimum variance method. Journal of Classification 22 (2):151–83.
  • Tamayo, P., D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E. S. Lander, and T. R. Golub. 1999. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences of the United States of America 96 (6):2907–12. doi:10.1073/pnas.96.6.2907.
  • Tibshirani, R., and G. Walther. 2005. Cluster validation by prediction strength. Journal of Computational and Graphical Statistics 14 (3):511–28.
  • Wockner, L. F., E. P. Noble, B. R. Lawford, R. M. Young, C. P. Morris, V. L. J. Whitehall, and J. Voisey. 2014. Genome-wide dna methylation analysis of human brain tissue from schizophrenia patients. Translational Psychiatry 4 (1):e339 doi:10.1038/tp.2013.111.
  • Yeung, K. Y., D. R. Haynor, and W. L. Ruzzo. 2001. Validating clustering for gene expression data. Bioinformatics (Oxford, England) 17 (4):309–18. doi:10.1093/bioinformatics/17.4.309.