Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts

Genome Biology

Volumn 15, Issue 2, 2014, Pages

  Law, Charity W ^a,b   Chen, Yunshun ^a,b   Shi, Wei ^a,b   Smyth, Gordon K ^a,b  

^a WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; BAYES THEOREM; DATA ANALYSIS; DEVELOPMENTAL STAGE; DROSOPHILA MELANOGASTER; GENE MAPPING; GENETIC TRANSCRIPTION; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MICROARRAY ANALYSIS; MOLECULAR LIBRARY; RNA ANALYSIS; RNA SEQUENCE; SEX DIFFERENCE; SIMULATION; UPREGULATION; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; NUCLEOTIDE SEQUENCE; PROCEDURES; SEQUENCE ANALYSIS; STATISTICAL MODEL; CONTROLLED STUDY; EMBRYO; EMPIRICAL BAYES; FEMALE; GENE EXPRESSION; HUMAN; INTERMETHOD COMPARISON; LIMMA TREND; MALE; MEAN VARIANCE; MEASUREMENT PRECISION; MOUSE; NONHUMAN; OVERLAPPING GENE; STATISTICAL ANALYSIS; VARIANCE MODELING AT THE OBSERVATION LEVEL METHOD;

RNA;

ALGORITHMS; BASE SEQUENCE; BAYES THEOREM; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; LINEAR MODELS; RNA; SEQUENCE ANALYSIS, RNA;

EID: 84896735766     PISSN: None     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/gb-2014-15-2-r29     Document Type: Article

References (71)

1. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci 2001, 98:5116-5121. 10.1073/pnas.091062498, 33173, 11309499.
2. Wright GW, Simon RM. A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 2003, 19:2448-2455. 10.1093/bioinformatics/btg345, 14668230.
3. Smyth G. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3:Article 3.
4. Cui X, Hwang JG, Qiu J, Blades NJ, Churchill GA. Improved statistical tests for differential gene expression by shrinking variance components estimates. Biostatistics 2005, 6:59-75. 10.1093/biostatistics/kxh018, 15618528.
5. Smyth G, Michaud J, Scott H. Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 2005, 21:2067-2075. 10.1093/bioinformatics/bti270, 15657102.
6. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005, 102:15545-15550. 10.1073/pnas.0506580102, 1239896, 16199517.
7. Wu D, Lim E, Vaillant F, Asselin-Labat M, Visvader J, Smyth G. ROAST rotation gene set tests for complex microarray experiments. Bioinformatics 2010, 26:2176-2182. 10.1093/bioinformatics/btq401, 2922896, 20610611.
8. Wu D, Smyth G. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res 2012, 40:e133-e133. 10.1093/nar/gks461, 3458527, 22638577.
9. Smyth G. Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor 2005, 397-420. New York: Springer, Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W.
10. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57-63. 10.1038/nrg2484, 2949280, 19015660.
11. Cloonan N, Forrest ARR, Kolle G, Gardiner BBA, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, Robertson AJ, Perkins AC, Bruce SJ, Lee CC, Ranade SS, Peckham HE, Manning JM, Mckernan KJ, Grimmond SM. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature Methods 2008, 5:613-619. 10.1038/nmeth.1223, 18516046.
12. Robinson M, McCarthy D, Smyth G. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26:139-140. 10.1093/bioinformatics/btp616, 2796818, 19910308.
13. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010, 11:R106. 10.1186/gb-2010-11-10-r106, 3218662, 20979621.
14. Oshlack A, Robinson MD, Young MD. From RNA-seq reads to differential expression results. Genome Biol 2010, 11:220. 10.1186/gb-2010-11-12-220, 3046478, 21176179.
15. Perkins TT, Kingsley RA, Fookes MC, Gardner PP, James KD, Yu L, Assefa SA, He M, Croucher NJ, Pickard DJ, Maskell DJ, Parkhill J, Choudhary J, Thomson NR, Dougan G. A strand-specific RNA-seq analysis of the transcriptome of the typhoid bacillus Salmonella Typhi. PLoS Genet 2009, 5:e1000569. 10.1371/journal.pgen.1000569, 2704369, 19609351.
16. Han X, Wu X, Chung WY, Li T, Nekrutenko A, Altman NS, Chen G, Ma H. Transcriptome of embryonic and neonatal mouse cortex by high-throughput RNA sequencing. Proc Natl Acad Sci 1274, 106:1-12746.
17. Parikh A, Miranda ER, Katoh-Kurasawa M, Fuller D, Rot G, Zagar L, Curk T, Sucgang R, Chen R, Zupan B, Loomis WF, Kuspa A, Shaulsky G. Conserved developmental transcriptomes in evolutionarily divergent species. Genome Biol 2010, 11:R35. 10.1186/gb-2010-11-3-r35, 2864575, 20236529.
18. Robinson MD, Smyth GK. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 2008, 9:321-332.
19. Zhou YH, Xia K, Wright FA. A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 2011, 27:2672-2678. 10.1093/bioinformatics/btr449, 3179656, 21810900.
20. Robinson MD, Smyth GK. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 2007, 23:2881-2887. 10.1093/bioinformatics/btm453, 17881408.
21. Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 2010, 11:422. 10.1186/1471-2105-11-422, 2928208, 20698981.
22. Wu H, Wang C, Wu Z. A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics 2013, 14:232-243. 10.1093/biostatistics/kxs033, 3590927, 23001152.
23. Lund S, Nettleton D, McCarthy D, Smyth G. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat Appl Genet Mol Biol 2012, 11:Article 8.
24. Srivastava S, Chen L. A two-parameter generalized Poisson model to improve the analysis of RNA-seq data. Nucleic Acids Res 2010, 38:e170. 10.1093/nar/gkq670, 2943596, 20671027.
25. Auer PL, Doerge RW. A two-stage Poisson model for testing RNA-seq data. Stat Appl Genet Mol Biol 2011, 10:Article 26.
26. Li J, Witten D, Johnstone I, Tibshirani R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 2012, 13:523-538. 10.1093/biostatistics/kxr031, 3372940, 22003245.
27. Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics 2012, 13:484. 10.1186/1471-2164-13-484, 3560154, 22985019.
28. 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.
29. Ritchie M, Diyagama D, Neilson J, Van Laar R, Dobrovic A, Holloway A, Smyth G. Empirical array quality weights in the analysis of microarray data. BMC Bioinformatics 2006, 7:261. 10.1186/1471-2105-7-261, 1564422, 16712727.
30. McCullagh P, Nelder JA. Generalized Linear Models 1989, Boca Raton: Chapman & Hall/CRC.
31. Wedderburn RWM. Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika 1974, 61:439-447.
32. Carroll RJ, Ruppert D. A comparison between maximum likelihood and generalized least squares in a heteroscedastic linear model. J Am Stat Assoc 1982, 77:878-882.
33. Nelder JA, Pregibon D. An extended quasi-likelihood function. Biometrika 1987, 74:221-232.
34. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 2012, 40:4288-4297. 10.1093/nar/gks042, 3378882, 22287627.
35. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 2008, 5:621-628. 10.1038/nmeth.1226, 18516045.
36. Sartor MA, Tomlinson CR, Wesselkamper SC, Sivaganesan S, Leikauf GD, Medvedovic M. Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments. BMC Bioinformatics 2006, 7:538. 10.1186/1471-2105-7-538, 1781470, 17177995.
37. Sequencing Quality Control (SEQC) Project. [http://www.fda.gov/MicroArrayQC].
38. Ambion FirstChoice Human Brain Reference RNA. [http://products.invitrogen.com/ivgn/product/AM6050].
39. Baker SC, Bauer SR, Beyer RP, Brenton JD, Bromley B, Burrill J, Causton H, Conley MP, Elespuru R, Fero M, Foy C, Fuscoe J, Gao X, Gerhold DL, Gilles P, Goodsaid F, Guo X, Hackett J, Hockett RD, Ikonomi P, Irizarry RA, Kawasaki ES, Kaysser-Kranich T, Kerr K, Kiser G, Koch WH, Lee KY, Liu C, Liu ZL, Lucas A, et al. The external RNA controls consortium: a progress report. Nature Methods 2005, 2:731-734. 10.1038/nmeth1005-731, 16179916.
40. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, Veyrieras JB, Stephens M, Gilad Y, Pritchard JK. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 2010, 464:768-772. 10.1038/nature08872, 3089435, 20220758.
41. Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics 2013, 14:254. 10.1186/1471-2105-14-254, 3849762, 23965047.
42. Skaletsky H, Kuroda-Kawaguchi T, Minx PJ, Cordum HS, Hillier L, Brown LG, Repping S, Pyntikova T, Ali J, Bieri T, Chinwalla A, Delehaunty A, Delehaunty K, Du H, Fewell G, Fulton L, Fulton R, Graves T, Hou S-F, Latrielle P, Leonard S, Mardis E, Maupin R, McPherson J, Miner T, Nash W, Nguyen C, Ozersky P, Pepin K, Rock S, et al. The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes. Nature 2003, 423:825-837. 10.1038/nature01722, 12815422.
43. Gonzalez JR, Esnaola M. tweeDEseqCountData: RNA-seq count data employed in the vignette of the tweeDEseq package. [http://www.bioconductor.org].
44. Carrel L, Willard HF. X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 2005, 434:400-404. 10.1038/nature03479, 15772666.
45. Graveley BR, Brooks N, Carlson JW, Duff MO, Landolin JM, Yang L, Artieri G, van Baren MJ, Boley N, Booth BW, Brown JB, Cherbas L, Davis CA, Dobin A, Li R, Lin W, Malone JH, Mattiuzzo NR, Miller D, Sturgill D, Tuch BB, Zaleski C, Zhang D, Blanchette M, Dudoit S. The developmental transcriptome of Drosophila melanogaster. Nature 2011, 471:473-479. 10.1038/nature09715, 3075879, 21179090.
46. Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F, Xue C, Marinov GK, Khatun J, Williams BA, Zaleski C, Rozowsky J, Röder M, Kokocinski F, Abdelhamid RF, Alioto T, Antoshechkin I, Baer MT, Bar NS, Batut P, Bell K, Bell I, Chakrabortty S, Chen X, Chrast J, Curado J, et al. Landscape of transcription in human cells. Nature 2012, 489:101-108. 10.1038/nature11233, 3684276, 22955620.
47. Gonzalez-Porta M, Frankish A, Rung J, Harrow J, Brazma A. Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene. Genome Biol 2013, 14:R70. 10.1186/gb-2013-14-7-r70, 23815980.
48. Bera AK, Bilias Y. Rao's score, Neyman's C α and Silvey's LM tests: an essay on historical developments and some new results. J Stat Plann Inference 2001, 97:9-44.
49. Pregibon D. Score tests in GLIM with applications. GLIM 82 Proceedings of the International Conference on Generalised Linear Models 1982, 87-97. New York: Springer, Gilchrist R.
50. Phipson B, Lee S, Majewski IJ, Alexander WS, Smyth GK. Empirical Bayes in the presence of exceptional cases, with application to microarray data. 2013, [http://www.statsci.org/smyth/pubs/RobustEBayesPreprint.pdf].
51. Oehlert GW. A note on the delta method. Am Statistician 1992, 46:27-29.
52. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979, 74:829-836.
53. Oshlack A, Emslie D, Corcoran L, Smyth G. Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes. Genome Biol 2007, 8:R2. 10.1186/gb-2007-8-1-r2, 1839120, 17204140.
54. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 2010, 11:R25. 10.1186/gb-2010-11-3-r25, 2864565, 20196867.
55. Gale WA, Sampson G. Good-Turing frequency estimation without tears. J Quant Linguist 1995, 2:217-237.
56. Law CW, Chen Y, Shi W, Smyth GK. Supplementary information for 'Voom: precision weights unlock linear model analysis tools for RNA-seq read counts'. [http://bioinf.wehi.edu.au/voom].
57. Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, Luo Y, Sun YA, Willey JC, Setterquist RA, Fischer GM, Tong W, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Scherf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK, et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotechnol 2006, 24:1151-1161.
58. Liao Y, Smyth GK, Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 2013, 41:e108. 10.1093/nar/gkt214, 3664803, 23558742.
59. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general-purpose read summarization program. Bioinformatics 2013, [http://bioinformatics.oxfordjournals.org/content/early/2013/11/30/bioinformatics.btt656].
60. Shi W, Liao Y. Rsubread: a super fast, sensitive and accurate read aligner for mapping next-generation sequencing reads. [http://www.bioconductor.org].
61. Bolstad BM, Irizarry RA, Åstrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19:185-193. 10.1093/bioinformatics/19.2.185, 12538238.
62. Frazee AC, Langmead B, Leek JT. ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics 2011, 12:449. 10.1186/1471-2105-12-449, 3229291, 22087737.
63. Frazee A, Langmead B, Leek J. ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. [http://bowtie-bio.sourceforge.net/recount].
64. Falcon S, Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics 2007, 23:257-258. 10.1093/bioinformatics/btl567, 17098774.
65. Carlson M. org.Dm.eg.db: Genome wide annotation for Fly. [http://www.bioconductor.org].
66. Bottomly D, Walter NA, Hunter JE, Darakjian P, Kawane S, Buck KJ, Searles RP, Mooney M, McWeeney S K H. Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays. PLoS One 2011, 6:e17820. 10.1371/journal.pone.0017820, 3063777, 21455293.
67. Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth GK, Tierney L, Yang JY, Zhang J. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004, 5:R80. 10.1186/gb-2004-5-10-r80, 545600, 15461798.
68. Bioconductor: open source software for bioinformatics. [http://www.bioconductor.org].
69. Comprehensive R Archive Network. [http://www.r-project.org].
70. Auer P, Doerge RW. TSPM.R: R code for a two-stage Poisson model for testing RNA-seq data. 2011, [http://www.stat.purdue.edu/~doerge/software/TSPM.R].
71. Smyth GK. limma: Linear Models for Microarray Data. [http://www.bioconductor.org/packages/release/bioc/html/limma.html].