A method to identify differential expression profiles of time-course gene data with Fourier transformation

BMC Bioinformatics

Volumn 14, Issue 1, 2013, Pages

  Kim, Jaehee ^a   Ogden, Robert T ^b   Kim, Haseong ^c  

^a Duksung Women's University   (South Korea)

^b Och Spine at New York Presbyterian Hospitals   (United States)

^c Korea Research Institute of Bioscience and Biotechnology (KRIBB)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL EXPRESSIONS; FOURIER COEFFICIENTS; FOURIER TRANSFORMATIONS; GAUSSIAN PROCESS REGRESSION; GENE EXPRESSION PROFILES; MICROARRAY EXPRESSIONS; MODEL-BASED CLUSTERING; NONPARAMETRIC REPRESENTATION;

AUTOCORRELATION; FOURIER ANALYSIS; FOURIER TRANSFORMS; TIME SERIES; WAVEFORM ANALYSIS;

GENE EXPRESSION;

ARTICLE; BIOLOGICAL MODEL; BIOLOGY; CELL CYCLE; CLUSTER ANALYSIS; DNA MICROARRAY; FOURIER ANALYSIS; GENE EXPRESSION PROFILING; GENETICS; METHODOLOGY; SACCHAROMYCES CEREVISIAE; SENSITIVITY AND SPECIFICITY; TIME;

CELL CYCLE; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; FOURIER ANALYSIS; GENE EXPRESSION PROFILING; MODELS, GENETIC; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; SACCHAROMYCES CEREVISIAE; SENSITIVITY AND SPECIFICITY; TIME FACTORS;

EID: 84885494902     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-310     Document Type: Article

References (39)

1. Bar-Joseph Z. Analyzing time series gene expression data. Bioinformatics 2004, 20(16):2493-2503. 10.1093/bioinformatics/bth283, 15130923.
2. Benjamin Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 1995, 57:289-300.
3. Devore JL, Peck R. Statistics: the exploration and analysis of data 1997, Belmont: Duxbury Press.
4. Thomas JG, Olson JM, Tapscott SJ, Zhao LP. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res 2001, 11:1227-1236. 10.1101/gr.165101, 311075, 11435405.
5. Pan W, Lin J, Le C. A mixture model approach to detecting differentially expressed genes with microarray data 2001, Technical report: Division of Biostatistics, University of Minnesota.
6. Efron B, Tibshirani R, Gross V, Tusher VG. Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 2001, 96:1151-1160.
7. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001, 98:5116-5121. 10.1073/pnas.091062498, 33173, 11309499.
8. Reiner A, Yekutieli D, Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 2003, 19(3):368-375. 10.1093/bioinformatics/btf877, 12584122.
9. Datta S, Satten GA, Benos DJ, Xia J, Heslin MJ, Datta S. An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments. Bioinformatics 2004, 20:235-242. 10.1093/bioinformatics/btg396, 14734315.
10. Datta S, Datta S. Empirical Bayes screening of many p-values with applications to microarray studies. Bioinformatics 2005, 21(9):1987-1994. 10.1093/bioinformatics/bti301, 15691856.
11. Serban N, Wasserman L. CATS: clustering after transformation and smoothing. J Am Stat Assoc 2005, 471:990-999.
12. Ernst J, Nau G, Bar-Joseph Z. Clustering short time series gene expression data. Bioinformatics 2005, 21:159-168.
13. Murthy KRK, Hua LJ. Improved Fourier transform method for unsupervised cell-cycle regulated gene prediction. Proc IEEE Comp Sys Bioinformatics Conf Proceedings 2004, 194-203.
14. Kim B, Littell RC, Wu R. Clustering periodic patterns of gene expression based on Fourier approximations. Curr Genomics 2006, 7:197-203.
15. Johansson D, Lindgren P, Berglund A. A multivariate approach applied to microarray data for identification of genes with cell cycle-coupled transcription. Bioinformatics 2003, 19:467-473. 10.1093/bioinformatics/btg017, 12611801.
16. Schliep A, Schonhuth A, Steinhoff C. Using hidden Markov models to analyze gene expression time course data. Bioinformatics 2003, 19(Suppl):i255-i263.
17. Kim J, Kim H. Clustering of change patterns using Fourier coefficients. Bioinformatics 2008, 24:184-191. 10.1093/bioinformatics/btm568, 18025003.
18. Murtage C, Raftery AE. Fitting straight lines to point patterns. Pattern Recogn 1984, 17:479-483.
19. Fraley C, Raftery AE. MCLUST: software for model-based cluster analysis. J of Classification 1999, 16:297-306.
20. Yuan M. Flexible temporal expression profile modelling using the Gaussian process. Comput Stat Data Anal 2006, 51(3):1754-1764.
21. Kirk PDW, Stumpf MPH. Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data. Bioinformatics 2009, 25(10):1300-1306. 10.1093/bioinformatics/btp139, 2677737, 19289448.
22. Lawrence ND, Sanguinetti G, Rattray M. Modelling transcriptional regulation using Gaussian processes. Adv Neural Inf Process Syst 2007, 19:785-792.
23. Gao P, Honkela A, Rattray M, Lawrence ND. Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities. Bioinformatics 2008, 24(16):i70-i75. 10.1093/bioinformatics/btn278, 18689843.
24. Kalaitzis AA, Lawrence ND. A simple approach to ranking differentially expressed gene expression time course through Gaussian process regression. BMC Bioinforma 2011, 12:180.
25. Angelini C, Cutillo L, Canditiis D, Mutarelli M, Pensky M. BATS: a Bayesian user-friendly software for analyzing time series microarray experiments. BMC Bioinforma 2008, 9:415.
26. Spellman PT, Sherlock G, Zhang MQ, Lyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast saccaromyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9:3273-3297. 10.1091/mbc.9.12.3273, 25624, 9843569.
27. Luan Y, Li H. Clustering of time-course gene expression data using mixed-effects models with B-splines. Bioinformatics 2003, 19:474-482. 10.1093/bioinformatics/btg014, 12611802.
28. Beissbarth T, Speed TP. GOstat: Find Statistically Overrepresented Gene Ontologies Within a Group of Genes 2004, 1464-1465. London: Oxford Univ Press.
29. Brown PO, Botstein D, Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast saccaromyces cerevisiae by microarray hybridization. Mol Bio Cell 1998, 9:3273-3297.
30. Lindquist S, Craig EA. The heat-shock proteins. Annu Rev Genet 1998, 22:631-677.
31. Smith B, Yaffe M. A mutation in the yeast heat-shock factor gene causes temperature-sensitive defects in both mitochondrial protein import and the cell cycle. Mol Cell Biol 1991, 11:2647-2655. 360034, 2017170.
32. Wilson AG, Adams RP. Gaussian Process Kernels for Pattern Discovery and Extrapolation 2013, Atlanta: International Machine Learning Society, Proceedings of the 30th International Conference on Machine Learning.
33. Kim J, Hart JD. Test for change when the data are dependent. J Time Series Anal 1998, 19(4):399-424.
34. Eubank R, Hart JD. Testing goodness-of-fit via order selection criteria. Ann Stat 1992, 20(3):1412-1425.
35. Berk KN. A central limit theorem for m-dependent random variables with unbounded m. Anal Prob 1973, 1973(1):352-354.
36. Freedman D, Lane D. The empirical distribution of Fourier coefficients. Anal stat 1980, 8(6):1244-1251.
37. Banfield JD, Raftery AE. Model-based Gaussian and non-Gaussian clustering. Biometrics 1993, 49:803-821.
38. Hubert L. Comparing partitions. J. of Classification 1985, 2:193-218.
39. Rousseeuw PJ. Silhouettes: graphical aid to the interpretation and validation of cluster analysis. J Comp App Math 1987, 20:53-65.