BMC Bioinformatics | |
More powerful significant testing for time course gene expression data using functional principal component analysis approaches | |
Shuang Wu1  Hulin Wu1  | |
[1] Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, NY, 14642, USA | |
关键词: Yeast cell cycle; Time course gene expression; One group test; Multiple group test; Functional data analysis; Differentially expressed genes; | |
Others : 1088028 DOI : 10.1186/1471-2105-14-6 |
|
received in 2012-08-07, accepted in 2012-11-07, 发布年份 2013 | |
【 摘 要 】
Background
One of the fundamental problems in time course gene expression data analysis is to identify genes associated with a biological process or a particular stimulus of interest, like a treatment or virus infection. Most of the existing methods for this problem are designed for data with longitudinal replicates. But in reality, many time course gene experiments have no replicates or only have a small number of independent replicates.
Results
We focus on the case without replicates and propose a new method for identifying differentially expressed genes by incorporating the functional principal component analysis (FPCA) into a hypothesis testing framework. The data-driven eigenfunctions allow a flexible and parsimonious representation of time course gene expression trajectories, leaving more degrees of freedom for the inference compared to that using a prespecified basis. Moreover, the information of all genes is borrowed for individual gene inferences.
Conclusion
The proposed approach turns out to be more powerful in identifying time course differentially expressed genes compared to the existing methods. The improved performance is demonstrated through simulation studies and a real data application to the Saccharomyces cerevisiae cell cycle data.
【 授权许可】
2013 Wu and Wu; licensee BioMed Central Ltd.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
20150117065417976.pdf | 793KB | download | |
Figure 7. | 64KB | Image | download |
Figure 6. | 82KB | Image | download |
Figure 5. | 40KB | Image | download |
Figure 4. | 64KB | Image | download |
Figure 3. | 65KB | Image | download |
Figure 2. | 48KB | Image | download |
Figure 1. | 29KB | Image | download |
【 图 表 】
Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.
Figure 6.
Figure 7.
【 参考文献 】
- [1]Tusher V, Tibshirani R, Chu C: Significance analysis of microarrays applied to the ionizing radiation response. Proc Nat Acad Sci USA 2001, 98:5116-5121.
- [2]Storey J, Tibshirani R: SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays. In The analysis of gene expression data: methods and software. Edited by Parmigiani G, Garrett ES, Irizarry R, Zeger S. New York: Springer; 2003:272-290.
- [3]Park T, Yi S, Lee S, Lee S, Yoo D, Ahn J, Lee Y: Statistical tests for identifying differentially expressed genes in time course microarray experiments. Bioinformatics 2003, 19:694-703.
- [4]Smyth G: Limma: linear models for microarray data. In Bioinformatics and computational biology solutions using R and Bioconductor. Edited by Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. New York: Springer; 2005:12837-12842.
- [5]Tai Y, Speed T: A multivariate empirical Bayes statistic for replicated microarray time course data. Ann Stat 2006, 34:2387-2412.
- [6]Yuan M, Kendziorski C: Hidden Markov Models for Microarray Time Course Data in Multiple Biological Conditions. J Am Stat Assoc 2006, 101(476):1323-1332.
- [7]Sun W, Wei Z: Multiple Testing for Pattern Identification, With Applications to Microarray Time-Course Experiments. J Am Stat Assocsss 2011, 106:73-88.
- [8]Ramsay JO, Silverman BW: Functional data analysis. New York: Springer; 2005. [Springer Series in Statistics]
- [9]Coffey N, Hinde J: Analysing time-course microarray data using functional data analysis - A review. BMC Bioinf 2011, 10:23.
- [10]Xu XL, Olson JM, Zhao LP: A regression-based method to identify differentially expressed genes in microarray time course studies and its application in an inducible Huntington’s disease transgenic model. Human Mol Genet 2002, 11(17):1977-1985.
- [11]Bar-Joseph Z, Gerber G, Simon I, Gifford D, Jaakkola T: Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes. Proc Nat Acad Sci USA 2003, 100:10146-10151.
- [12]Storey JD, Xiao W, Leek JT, Tompkins RG, Davis RW: Significance analysis of time course microarray experiments. Proc National Acad Sci USA 2005, 102(36):12837-12842.
- [13]Hong F, Li H: Functional hierarchical models for identifying genes with different time-course expression profiles. Biometrics 2006, 62:534-544.
- [14]Liu X, Yang M: Identifying temporally differentially expressed genes through functional principal component analysis. Biostatistics 2009, 10:667-679.
- [15]Chen K, Wang JL: Identifying differentially expressed genes for time-course microarray data through functional data analysis. Stat Biosci 2010, 2:95-119.
- [16]Ma P, Zhong W, Liu J: Identifying differentially expressed genes in time course microarray data. Stat Biosci 2009, 1:144-159.
- [17]Orlando D, Lin C, Bernard A, Wang J, Socolar J, Iversen E, Hartemink A, Haase S: Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature 2008, 453:944-947.
- [18]Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Mol Biol Cell 1998, 9(12):3273-3297.
- [19]Cicatiello L, Scafoglio C, Altucci L, Cancemi M, Natoli G, Facchiano A, Iazzetti G, Calogero R, Biglia N, De Bortoli M, Sfiligoi C, Sismondi P, Bresciani F, Weisz A: A genomic view of estrogen actions in human breast cancer cells by expression profiling of the hormone-responsive trascriptome. J Mol Endocrinol 2004, 32:719-775.
- [20]Sohn I, Owzar K, George SL, Kim S, Jung S: A permutation-based multiple testing method for time-course microarray experiments. BMC Bioinf 2009, 10:336. BioMed Central Full Text
- [21]Han X, Sung WK, Feng L: Identifying differentially expressed genes in time-course microarray experiment without replicate. J Bioinf Comput Biol 2007, 5:281-296.
- [22]Ash RB, Gardner MF: Topics in stochastic processes. New York: Academic Press; 1975. [Probability and Mathematical Statistics, Vol. 27]
- [23]Yao F, Müller HG, Wang JL: Functional data analysis for sparse longitudinal data. J Am Stat Assoc 2005, 100(470):577-590.
- [24]Shen Q, Faraway J: An F test for linear models with functional responses. Statistica Sinica 2004, 14:1239-1257.
- [25]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R S Soc: Ser B (Stat Methodology) 1995, 57:289-300.
- [26]Reiner A, Yekutieli D, Benjamini Y: Identifying diffrentially expressed genes using false discovery rate controlling procedure. Bioinformatics 2003, 19:368-375.
- [27]Hart JD: Nonparametric smoothing and lack-of-fit tests. Springer; 1997.
- [28]Benjamini Y, Yekutieli D: The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001, 29:1165-1188.
- [29]Klebanov L, Yakovlev A: Detecting intergene correlation changes in microarray analysis: A new approach to gene selection. Ann Appl Stat 2007, 1:538-559.
- [30]Hu R, Qiu X, Glazko G: A new gene selection procedure based on the covariance distance. Bioinformatics 2010, 23:348-354.