Differential expression-the next generation and beyond

Briefings in Functional Genomics

Volumn 11, Issue 1, 2012, Pages 57-62

  Auer, Paul L ^a   Srivastava, Sanvesh ^a   Doerge, R W ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

Curse of dimensionality; Differential expression; Dimension reduction; Experimental design; RNA seq; Statistical bioinformatics

Indexed keywords

ARTICLE; EXON; GENE EXPRESSION; GENE TECHNOLOGY; GENETIC VARIABILITY; HIGH THROUGHPUT SEQUENCING; MACHINE LEARNING; MICROARRAY ANALYSIS; RNA SEQUENCE;

ARTIFICIAL INTELLIGENCE; GENE EXPRESSION PROFILING; MULTIFACTOR DIMENSIONALITY REDUCTION; RESEARCH DESIGN; STATISTICS AS TOPIC;

EID: 84857218267     PISSN: 20412649     EISSN: 20412657     Source Type: Journal    
DOI: 10.1093/bfgp/elr041     Document Type: Article

References (46)

1. Hillier LW, Marth GT, Quinlan AR, et al. Whole-genome sequencing and variant discovery in C. elegans. NatMethods 2008;5:183-8.
2. Mikkelsen TS, et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 2007; 448:553-60.
3. Valouev A, Johnson DS, Sundquis A, et al. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. NatMethods 2008;5:829-34.
4. Bentley DR. Whole-genome re-sequencing. Curr Opin Genet Dev 2006;16:545-52.
5. Futschik A, Schlötterer C. The next generation of molecular markers from massively parallel sequencing of pooled DNA samples. Genetics 2010;186:207-18.
6. Lynch M. Estimation of allele frequencies from highcoverage genome-sequencing projects. Genetics 2009;182: 295-301.
7. Blumenstiel JP, Noll AC, Griffiths JA, et al. Identification of EMS-induced mutations in drosophila melanogaster by whole-genome sequencing. Genetics 2009;182:25-32.
8. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008;5:621-8.
9. Marioni JC, Mason CE, Mane SM, et al. RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008;18:1509-17.
10. Larkin JE, Frank BC, Gavras H, et al. Independence and reproducibility across microarray platforms. Nat Methods 2005;2:337-44.
11. Nettleton D. A discussion of statistical methods for design and analysis of microarray experiments for plant scientists. Plant Cell 2006;18:2112-21.
12. Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003;4:249.
13. Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics 2010;185:405-16.
14. Salzman J, Jiang H, Wong WH. Statistical modeling of RNA-seq data. Statistical Science 2011;26(1):62-83.
15. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002;32:490-5.
16. Kerr MK, Churchill GA. Statistical design and the analysis of gene expression microarray data. Genetics Res 2001;77: 123-8.
17. Kerr MK, Churchill GA. Experimental design for gene expression microarrays. Biostatistics 2001;2:183-201.
18. Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet 2010;11:31-46.
19. Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 2008;18:1851-8.
20. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25: 1754-60.
21. Trapnell C, Williams BA, Pertea A, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010;28:511-5.
22. Jiang H, Wong WH. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics 2009;25:1026-32.
23. Degner JF, Marioni JC, Pai AA, et al. Effect of read-mapping biases on detecting allele-specific expression from RNAsequencing data. Bioinformatics 2009;25:3207-12.
24. Hutchison III CA. DNA sequencing: bench to bedside and beyond. Nucleic Acids Res 2007;35:6227-37.
25. Bullard JH, Purdom E, Hansen K, et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010; 11:94.
26. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-Seq data. Genome Biol 2010;11:R25.
27. Robinson MD, Smyth GK. Moderated statistical test for assessing differences in tag abundance. Bioinformatics 2007; 23:2881-87.
28. Langmead B, Hansen KD, Leek JT. Cloud-scale RNA sequencing differential expression analysis with Myrna. Genome Biol 2010;11:R83.
29. Auer PL, Doerge RW. A two-stage Poisson model for testing RNA-seq data. Stat Appl Genet Mol Biol 2011;10: 1-26.
30. 't Hoen PAC, Ariyurek Y, Thygesen HH, et al. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res 2008;36: e141.
31. McIntyre LM, Lopiano KK, Morse AM, et al. RNA-seq: technical variability and sampling. BMC Genomics 2011;12: 293.
32. Fisher RA. TheDesign of Experiments, 1935. 3rd edn. London: Oliver & Boyd Ltd.
33. Jones B, Goos P. A candidate-set-free algorithm for generating D-optimal split-plot designs. J RStat Soc Ser C 2007; 56:347-64.
34. Wit E, Nobile A, Khanin R. Simulated annealing for near-optimal dual-channel microarray designs. University of Glasgow, 2004.
35. Wit E, McClure J. Statistics for Microarrays; Design, Analysis and Inference. Chichester: John Wiley & Sons, 2004.
36. Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome Biol 2010; 11(12):220.
37. Efron B. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press, 2010.
38. Efron B, Tibshirani R, Storey JD, et al. Empirical Bayes analysis of a microarray experiment. J Am Stat Assoc 2001; 96(456):1151-60.
39. Efron B. Large-scale simultaneous hypothesis testing. JAm Stat Assoc 2004;99(465):96-104.
40. Efron B. Size, power and false discovery rates. Ann Stat 2007;35(4):1351-77.
41. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softwr 2010;33(1):1.
42. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. JRStat Soc Ser B 1995;57(1):289-300.
43. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001;29:1165-88.
44. Efron B. Microarrays, empirical Bayes and the two-groups model. Stat Sci 2008;23(1):1-22.
45. Srivastava S, Doerge RW. Latent process decomposition of high-dimensional count data. Technical Report 11-03, Purdue University, 2011.
46. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10: 57-63.