Differential meta-analysis of RNA-seq data from multiple studies

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Rau, Andrea ^a   Marot, Guillemette ^b,c   Jaffrézic, Florence ^a  

^a UMR1313 GÉNÉTIQUE ANIMALE ET BIOLOGIE INTÉGRATIVE   (France)

^b UNIV LILLE   (France)

^c INRIA   (France)

Author keywords

Differential expression; Meta analysis; p value combination; RNA seq

Indexed keywords

CELL CULTURE;

DIFFERENTIAL ANALYSIS; DIFFERENTIAL EXPRESSIONS; EXPERIMENTAL CONDITIONS; GENERALIZED LINEAR MODEL; HIGH-THROUGHPUT SEQUENCING; META-ANALYSIS; P-VALUES; RNA-SEQ;

RNA;

ARTICLE; GENE EXPRESSION PROFILING; HIGH THROUGHPUT SEQUENCING; HUMAN; META ANALYSIS; METHODOLOGY; SEQUENCE ANALYSIS; TUMOR CELL LINE;

CELL LINE, TUMOR; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; SEQUENCE ANALYSIS, RNA;

EID: 84899073808     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-91     Document Type: Article

References (25)

1. Marot G, Foulley JL, Mayer CD, Jaffrézic F. Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics 2009, 25(20):2692-2699. doi:10.1093/bioinformatics/btp444, 10.1093/bioinformatics/btp444, 19628502.
2. Choi JK, Yu U, Kim S, Yoo OJ. Combining multiple microarray studies and model interstudy variation. Bioinformatics 2003, 19(Suppl 1):84-90.
3. Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple yet powerful new method to detect differential regulated genes in replicated microarray experiments. FEBS Lett 2004, 573:83-92. 10.1016/j.febslet.2004.07.055, 15327980.
4. Hu P, Greenwood CM, Beyene J. Statistical methods for meta-analysis of microarray data: a comparative study. Inf Syst Front 2006, 8:9-20.
5. Hong F, Breitling R. A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments. Bioinformatics 2008, 24(3):374-382. 10.1093/bioinformatics/btm620, 18204063.
6. Tseng GC, Ghosh D, Feingold E. Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res 2012, 40(9):3785-3799. 10.1093/nar/gkr1265, 3351145, 22262733.
7. Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3(Article 3). http://dx.doi.org/10.2202/1544-6115.1027.
8. Jaffrézic F, Marot G, Degrelle S, Hue I, Foulley JL. A structural mixed model for variances in differential gene expression studies. Genet Res 2007, 89:19-25. 10.1017/S0016672307008646, 17517156.
9. Auer P, Doerge R. A two-stage Poisson model for testing RNA-seq data. Stat Appl Genet Mol Biol 2011, 10(26):1-26.
10. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010, 11(R106). doi:10.1186/gb-2010-11-10-r106.
11. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26:139-140. 10.1093/bioinformatics/btp616, 2796818, 19910308.
12. Kulinskaya E, Morgenthaler S, Staudte RG. Meta Analysis: a guide to calibrating and combining statistical evidence, Volume Volume 756 of Wiley Series in Probability and Statistics 2008, West Sussex, England: John Wiley & Sons.
13. Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 2013, 14:91. 10.1186/1471-2105-14-91, 3608160, 23497356.
14. Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloë D, Le Gall C, Schaëffer B, Le Crom S, Jaffrézic F. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 2012, [doi:10.1093/bib/bbs046].
15. Stouffer S, Suchman E, DeVinney L, Star S, Williams JRM. The American soldier. Adjustment during Army life 1949, Princeton, NJ: Princeton University Press.
16. Liptak T. On the combination of independent tests. Magyar Tudomanyos. Akademia Matematikai Kutato Intezetenek Kozlemenyei 1958, 3:171-197.
17. Marot G, Mayer CD. Sequential analysis for microarray data based on sensitivity and meta-analysis. Stat Appl Genet Mol Biol 2009, 8(Article 3). [doi:10.2202/1544-6115.1368].
18. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B 1995, 57:289-300.
19. Fisher RA. Statistical Methods for Research Workers 1932, Edinburgh: Oliver and Boyd.
20. Rau A, Gallopin M, Celeux G, Jaffrézic F. Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics 2013, 29(17):2146-52. 10.1093/bioinformatics/btt350, 3740625, 23821648.
21. Pearson K. On a new method of determining " goodness of fit" Biometrika 1934, 26:425-442.
22. Owen AB. Karl Pearson's meta-analysis revisited. Annals of Statistics 2009, 37(6B):3867-3892.
23. Strub T, Giuliano S, Ye T, Bonet C, Keime C, Kobi D, Gras SL, Cormont M, Ballotti R, Bertolotto C, Davidson I. Essential role of microphthalmia transcription factor for DNA replication, mitosis and genomic stability in melanoma. Oncogene 2011, 30:2319-2332. 10.1038/onc.2010.612, 21258399.
24. Wickham H. ggplot2: Elegant Graphics for Data Analysis 2009, New York: Springer.
25. Chen H, Boutros PC. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 2011, 12:35. 10.1186/1471-2105-12-35, 3041657, 21269502.