Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses

Nucleic Acids Research

Volumn 43, Issue 15, 2015, Pages

  Liu, Ruijie ^a   Holik, Aliaksei Z ^a,b   Su, Shian ^a   Jansz, Natasha ^a,b   Chen, Kelan ^a,b   Leong, Huei San ^a,b   Blewitt, Marnie E ^a,b   Asselin Labat, Marie Liesse ^a,b   Smyth, Gordon K ^a,c   Ritchie, Matthew E ^a,b,c  

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

^b UNIVERSITY OF MELBOURNE   (Australia)

^c UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CONTROLLED STUDY; GENE EXPRESSION; HUMAN; HUMAN CELL; INTERMETHOD COMPARISON; LOGLINEAR MODEL; OBSERVATIONAL METHOD; PRIORITY JOURNAL; RNA SEQUENCE; SAMPLE; SEQUENCE ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; VARIANCE; WEIGHT; ANIMAL; GENE EXPRESSION PROFILING; GENETICS; MOUSE; PROCEDURES; REPRODUCIBILITY; TUMOR CELL LINE;

NONHISTONE PROTEIN; SMCHD1 PROTEIN, MOUSE;

ANIMALS; CELL LINE, TUMOR; CHROMOSOMAL PROTEINS, NON-HISTONE; GENE EXPRESSION PROFILING; HUMANS; LINEAR MODELS; MICE; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, RNA;

EID: 84936076693     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkv412     Document Type: Article

References (34)

1. Oshlack, A., Robinson, M. D. and Young, M. D. (2010) From RNA-seq reads to differential expression results. Genome Biol., 11, 220.
2. Hansen, K. D., Brenner, S. E. and Dudoit, S. (2010) Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res., 38, e131.
3. Robinson, M. D. and Oshlack, A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25.
4. Risso, D., Ngai, J., Speed, T. P. and Dudoit, S. (2014) Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol., 32, 896-902.
5. Leek, J. T. (2014) svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res., doi:10. 1093/nar/gku864.
6. Hardcastle, T. J. and Kelly, K. A. (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422.
7. McCarthy, D. J., Chen, Y. and Smyth, G. K. (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res., 40, 4288-4297.
8. Kristiansson, E., Sj ögren, A., Rudemo, M. and Nerman, O. (2005) Weighted analysis of paired microarray experiments. Stat. Appl. Genet. Mol. Biol., 4, Article 30.
9. Ritchie, M. E., Diyagama, D., Neilson, J., van Laar, R., Dobrovic, A., Holloway, A. and Smyth, G. K. (2006) Empirical array quality weights in the analysis of microarray data. BMC Bioinformatics, 7, 261.
10. Sj ögren, A., Kristiansson, E., Rudemo, M. and Nerman, O. (2007) Weighted analysis of general microarray experiments. BMC Bioinformatics, 8, 387.
11. Asselin-Labat, M., Vaillant, F., Sheridan, J. M., Pal, B., Wu, D., Simpson, E. R., Yasuda, H., Smyth, G. K., Martin, T., Lindeman, G. J. et al. (2010) Control of mammary stem cell function by steroid hormone signalling. Nature, 465, 798-802.
12. Ellis, L., Pan, Y., Smyth, G. K., George, D. J., McCormack, C., Williams-Truax, R., Mita, M., Beck, J., Burris, H., Ryan, G. et al. (2008) Histone deacetylase inhibitor panobinostat induces clinical responses with associated alterations in gene expression profiles in cutaneous T-cell lymphoma. Clin. Cancer Res., 14, 4500-4510.
13. Law, C. W., Chen, Y., Shi, W. and Smyth, G. K. (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol., 15, R29.
14. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. and Smyth, G. K. (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., doi:10. 1093/nar/gkv007.
15. Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol., 3, Article 3.
16. McCarthy, D. J. and Smyth, G. K. (2009) Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics, 25, 765-771.
17. Giardine, B., Riemer, C., Hardison, R. C., Burhans, R., Elnitski, L., Shah, P., Zhang, Y., Blankenberg, D., Albert, I., Taylor, J. et al. (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res., 15, 1451-1455.
18. Goecks, J., Nekrutenko, A., Taylor, J. and The Galaxy Team (2010) Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol., 11, R86.
19. Blankenberg, D., Kuster, G. V., Coraor, N., Ananda, G., Lazarus, R., Mangan, M., Nekrutenko, A. and Taylor, J. (2010) Galaxy: a web-based genome analysis tool for experimentalists. Curr. Protoc. Mol. Biol., 89, 19-21.
20. Blankenberg, D., Von Kuster, G., Bouvier, E., Baker, D., Afgan, E., Stoler, N., Taylor, J., Nekrutenko, A. and The Galaxy Team (2014) Dissemination of scientific software with Galaxy ToolShed. Genom Biol., 15, 403.
21. R Development Core Team. (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
22. Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. : Ser. B, 57, 289-300.
23. Holloway, A. J., Oshlack, A., Diyagama, D. S., Bowtell, D. D. and Smyth, G. K. (2006) Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis. BMC Bioinformatics, 7, 511.
24. SEQC/MAQC-III Consortium. (2014) A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol., 32, 903-914.
25. Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D. et al. (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Genome Biol., 483, 603-607.
26. Liao, Y., Smyth, G. K. and Shi, W. (2013) The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res., 41, e108.
27. Liao, Y., Smyth, G. K. and Shi, W. (2014) featureCounts: an efficient general-purpose read summarization program. Bioinformatics, 30, 923-930.
28. Robinson, M. D., McCarthy, D. J. and Smyth, G. K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
29. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J. et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol., 5, R80.
30. Leong, H. S., Chen, K., Hu, Y., Lee, S., Corbin, J., Pakusch, M., Murphy, J. M., Majewski, I. J., Smyth, G. K., Alexander, W. S. et al. (2013) Epigenetic regulator Smchd1 functions as a tumor suppressor. Cancer Res., 73, 1591-1599.
31. Gendrel, A. V., Tang, Y. A., Suzuki, M., Godwin, J., Nesterova, T. B., Greally, J. M., Heard, E. and Brockdorff, N. (2013) Epigenetic functions of Smchd1 repress gene clusters on the inactive X chromosome and on autosomes. Mol. Cell. Biol., 33, 3150-3165.
32. Mould, A. W., Pang, Z., Pakusch, M., Tonks, I. D., Stark, M., Carrie, D., Mukhopadhyay, P., Seidel, A., Ellis, J. J., Deakin, J. et al. (2013) Smchd1 regulates a subset of autosomal genes subject to monoallelic expression in addition to being critical for X inactivation. Epigenetics Chromatin, 6, 19.
33. Wu, D., Lim, E., Vaillant, F., Asselin-Labat, M., Visvader, J. E. and Smyth, G. K. (2010) ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics, 26, 2176-2182.
34. Zhou, X., Lindsay, H. and Robinson, M. D. (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res., 42, e91.