Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models

Bioinformatics

Volumn 31, Issue 9, 2015, Pages 1420-1427

  Rau, Andrea ^a   Maugis Rabusseau, Cathy ^b   Martin Magniette, Marie Laure ^c,d,e   Celeux, Gilles ^f  

^a UMR1313 GÉNÉTIQUE ANIMALE ET BIOLOGIE INTÉGRATIVE   (France)

^b INSA   (France)

^c AgroParisTech   (France)

^d INSTITUTE OF PLANT SCIENCES PARIS SACLAY IPS2   (France)

^e CNRS   (France)

^f INRIA SACLAY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL; CELL LINE; CLUSTER ANALYSIS; DROSOPHILA MELANOGASTER; EMBRYOLOGY; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; LIVER; METABOLISM; POISSON DISTRIBUTION; PROCEDURES; SEQUENCE ANALYSIS; STATISTICAL MODEL;

ANIMALS; CELL LINE; CLUSTER ANALYSIS; DROSOPHILA MELANOGASTER; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; LIVER; MODELS, STATISTICAL; POISSON DISTRIBUTION; SEQUENCE ANALYSIS, RNA;

EID: 84941280707     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu845     Document Type: Article

References (46)

1. Anders, S. and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol., 11, 1-28.
2. Auer, P.L. and Doerge, R.W. (2010). Statistical design and analysis of RNASeq data. Genetics, 185, 1-12.
3. Auer, P.L. et al. (2012). Differential expression-the next generation and beyond. Brief. Funct. Genomics, 11, 57-62.
4. Baudry, J.-P. et al. (2012). Slope heuristics: overview and implementation. Stat. Comp., 22, 455-470.
5. Biernacki, C. et al. (2003). Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Comp. Stat. Data Anal., 41, 561-575.
6. Birgé, L. and Massart, P. (2001). Gaussian model selection. J. Eur. Math. Soc., 3, 203-268.
7. Birgé, L. and Massart, P. (2006). Minimal penalties for Gaussian model selection. Probab. Thoery Relat. Fields, 138, 33-73.
8. Cai, L. et al. (2004). Clustering analysis of SAGE data using a Poisson approach. Genome Biol., 5, R51.
9. Calinski, T. and Harabasz, J. (1974). A dendrite method for cluster analysis. Commun. Stat. Theory Methods, 3, 1-27.
10. Celeux, G. and Govaert, G. (1992). A classification EMalgorithm for clustering and two stochastic versions. Comp. Stat. Data Anal., 14, 315-332.
11. Dempster, A. P. et al. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. Series B (Methodological), 39, 1-38.
12. Dillies, M.-A. et al. (2013). A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform., 14, 671-83.
13. Eisen, M. B. et al. (1998). Cluster analysis and display of genome-wide expression patterns. PNAS, 95, 14863-14868.
14. Frazee, A.C. et al. (2011). ReCount: A multi-experiment resource of analysisready RNA-seq gene count datasets. BMC Bioinformatics, 12.
15. Graveley, B.R. et al. (2011). The development transcriptome of Drosophila melanogaster. Nature, 471, 473-479.
16. Hubert, L. and Arabie, P. (1985). Comparing partitions. J. Classif., 2, 193-218.
17. Jiang, D. et al. (2004). Cluster analysis for gene expression data: A survey. IEEE Trans. Knowl. Data Eng., 16, 1370-1386.
18. Karlis, D. (2003). An EM algorithm for multivariate Poisson distribution and related models. J. Appl. Stat., 30, 63-77.
19. Abaj, P. P. et al. (2011). Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics, 27(ISMB), i383-i391.
20. Law, C. et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol., 15.
21. Li, J. et al. (2012). Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics, 13, 523-538.
22. MacQueen, J.B. (1967). Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, p. 281-297. University of California Press, Berkeley.
23. McCarthy, D. et al. (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res., 40, 4288-4297.
24. McCutcheon, A.C. (1987). Latent Class Analysis. Sage Publications, Beverly Hills.
25. McIntyre, L.M. et al. (2011). RNA-seq: technical variability and sampling. BMC Genomics, 12.
26. McLachlan, G. et al. (2004). Analyzing Microarray Gene Expression Data. Wiley-Interscience, Hoboken.
27. McLachlan, G. and Peel, D. (2000). Finite Mixture Models. Wiley-Interscience, New York.
28. Naghavachari, N. et al. (2012). A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease. BMC Medical Genomics, 5, 28.
29. Oshlack, A. and Wakefield, M.J. (2009). Transcript length bias in RNA-seq data confounds systems biology. Biol. Direct, 4.
30. Papastamoulis, P. et al. (2014). On the estimation of mixtures of Poisson regression models with large numbers of components. Comp. Stat. Data Anal., doi:10.1016/j.csda.2014.07.005.
31. Rau, A. et al. (2013). Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics, 29, 2146-2152.
32. Robinson, M. D. et al. (2010). edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
33. Robinson, M. D. and Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25.
34. Robinson, M. D. and Smyth, G. K. (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 23, 2881-2887.
35. Schwarz, G. (1978). Estimating the dimension of a model. Ann. Stat., 6, 461-464.
36. SEQC/MAQC-III Consortium (2014). A comprehensive assessment of RNAseq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol., 32, 903-914.
37. Severin, A. J. et al. (2010). RNA-Seq Atlas of Glycine max: A guide to the soybean transcriptome. BMC Plant Biol., 10, 160.
38. Si, Y. et al. (2014). Model-based clustering for RNA-seq data. Bioinformatics, 30, 197-205.
39. Soneson, C. and Delorenzi, M. (2013). A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics, 14, 91.
40. Sultan, M. et al. (2008). A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science, 15, 956-60.
41. Trapnell, C. et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol., 28, 511-518.
42. Wang, C. et al. (2014). The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol., 32, 926-932.
43. Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 58, 236-244.
44. Witten, D. M. (2011). Classification and clustering of sequencing data using a Poisson model. Ann. Appl. Stat., 5, 2493-2518.
45. Yeung, K. Y. et al. (2001). Model-based clustering and data transformations for gene expression data. Bioinformatics, 17, 977-987.
46. Zhou, X. et al. (2014). Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res., 42, e91.