Skip to main content
SpringerLink
Log in
Book cover

Statistical Analysis of Next Generation Sequencing Data pp 25–49Cite as

Using RNA-seq Data to Detect Differentially Expressed Genes

  • Chapter
  • First Online:

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

RNA-sequencing (RNA-seq) technology has become a major choice in detecting differentially expressed genes across different biological conditions. Although microarray technology is used for the same purpose, statistical methods available for identifying differential expression for microarray data are generally not readily applicable to the analysis of RNA-seq data, as RNA-seq data comprise discrete counts of reads mapped to particular genes. In this chapter, we review statistical methods uniquely developed for detecting differential expression among different populations of RNA-seq data as well as techniques designed originally for the analysis of microarray data that have been modified for the analysis of RNA-seq data. We include a very brief description of the normalization of RNA-seq data and then elaborate on parametric and nonparametric testing procedures, as well as empirical and fully Bayesian methods. We include a brief review of software available for the analysis of differential expression and summarize the results of a recent comprehensive simulation study comparing existing methods.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Anders, S., Huber, W.: Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

    Article  Google Scholar 

  2. Anders, S., McCarthy, D.J., Chen, Y., Okoniewski, M., Smyth, G.K., Huber, W., Robinson, M.D.: Count-based differential expression analysis of RNA sequencing data using R and bioconductor. Nat. Protocol. 8, 1765–1786 (2013)

    Article  Google Scholar 

  3. Auer, P.L., Doerge, R.W.: A two-stage poisson model for testing RNA-seq data. Stat. Appl. Genet. Mol. Biol. 10(1), 26 (2011)

    MathSciNet  Google Scholar 

  4. Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. Ser. B 57, 289–300 (1995)

    MATH  MathSciNet  Google Scholar 

  5. Bottomly, D., Walter, N.A., Hunter, J.E., Darakjian, P., Kawane, S., Buck, K.J., Searles, R.P., Mooney, M., McWeeney, S.K., Hitzermann, R.: Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-seq and microarrays. PLoS One 6(3), e17820 (2011)

    Article  Google Scholar 

  6. Bullard, J.H., Purdom, E., Hansen, K.D., Dudoit, S.: Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMC Bioinform. 11, 94 (2010)

    Article  Google Scholar 

  7. Canales, R.D., Luo, Y., Willey, J.C., Austermiller, B., Barbacioru, C.C., Boysen, C., Hunkapiller, K., Jensen, R.V., Knight, C.R., Lee, K.Y., et al.: Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotech. 24(9), 1115–1122 (2006)

    Article  Google Scholar 

  8. Cloonan, N., Forrest, A.R.R., Kolle, G., Gardiner, B.B.A., Faulkner, G.J., Brown, M.K., Taylor, D.F., Steptoe, A.L., Wani, S., Bethel, G., et al.: Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Meth. 5, 613–619 (2008)

    Article  Google Scholar 

  9. Di, Y., Schafer, D.W., Cumbie, J.S., Chang, J.H.: The NBP negative binomial model for assessing differential gene expression from RNA-seq. Stat. Appl. Genet. Mol. Biol. 10(1), 24 (2011)

    MathSciNet  Google Scholar 

  10. Di, Y., Schafer, D.W, Cumbie, J.S., Chang, J.H. NBPSeq: negative binomial models for RNA-sequencing data. R Package Version 0.1.8. (2012). http://CRAN.R-project.org/package=NBPSeq

  11. Dillies, M.A., Rau, A., Aubert, J., Hennequet-Antier, C., Jeanmougin, M., Servant, N., Keime, C., Marot, G., Castel, D., Estelle, J., et al.: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. (2012). doi:10.1093/bib/bbs046

    MATH  Google Scholar 

  12. Gentleman R., Carey V.J., Bates D.M., Bolstad B., Dettling M., Dudoit S., Ellis B., Gautier L., Ge Y., Others: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 5, R80 (2004)

    Article  Google Scholar 

  13. Hardcastle, T.J.: baySeq: empirical Bayesian analysis of patterns of differential expression in count data. R Package Version 1.16.0. (2012)

    Google Scholar 

  14. Hardcastle, T.J., Kelly, K.A.: baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinform. 11, 422 (2010)

    Google Scholar 

  15. Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26, 147–159 (2004)

    Article  Google Scholar 

  16. Kvam, V.M., Liu, P., Si, Y.: A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am. J. Botany 99(2), 248–256 (2012)

    Article  Google Scholar 

  17. Lee, J., Ji, Y., Liang, S., Cai, G., Muller, P.: On differential gene expression using RNA-seq data. Cancer Inform. 10, 205–215 (2011)

    Google Scholar 

  18. Leng, N.: EBSeq: an R package for gene and isoform differential expression analysis of RNA-seq data. R Package Version 1.2.0 (2013)

    Google Scholar 

  19. Leng, N., Dawson, J., Thomson, J., Ruotti, V., Rissman, A., Smits, B., Haag, J., Gould, M., Stewart, R., Kendziorski, C.: EBSeq: an empirical bayes hierarchical model for inference in RNA-seq experiments. Technical Report 226. Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison (2012). http://www.biostat.wisc.edu/Tech-Reports/pdf/tr_226.pdf

  20. Li, J., Tibshirani, R.: Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-seq data. Stat. Meth. Med. Res. 22(5), 519–536 (2011)

    Article  MathSciNet  Google Scholar 

  21. Li, P., Ponnala, L., Gandotra, N., Wang, L., Si, Y. Tausta, S.L., Kebrom, T.H., et al. The developmental dynamics of the maize leaf transcriptome. Nat. Genet. 42, 1060–1067 (2010)

    Article  Google Scholar 

  22. Lister, R., O’Malley, R.C., Tonti-Filippini, J., Gregory, B.D., Berry, C.C., Millar, A.H., Ecker, J.R.: Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008)

    Article  Google Scholar 

  23. Lund, S.P., Nettleton, D., McCarthy, D.J., Smyth, G.K.: Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat. Appl. Genet. Mol. Biol. 11(5), Article 8 (2012)

    Google Scholar 

  24. Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M., Gilad, Y.: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008)

    Article  Google Scholar 

  25. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., Wold, B.: Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Meth. 5, 621–628 (2008)

    Article  Google Scholar 

  26. Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M., Snyder, M.: The transcriptional language of the yeast genome defined by RNA sequencing. Science 320(5881), 1344–1349 (2008)

    Article  Google Scholar 

  27. Obayashi, T., Kinoshuta, K.: Coxpresdb: a database to compare gene coexpression in seven model animals. Nucleic Acids Res. 39, D1016–D1022 (2011)

    Article  Google Scholar 

  28. Pan, Q., Shai, O., Lee, L.J., Frey, B.J., Blencowe, B.J.: Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008)

    Article  Google Scholar 

  29. Pickrell, J.K., Marioni, J.C., Pai, A.A., Degner, J.F., Engelhardt B.E., Nkadori, E., Veyrieras, J.B., et al.: Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010)

    Article  Google Scholar 

  30. Pounds, S.B., Gao, C.L., Zhang, H.: Empirical Bayesian selection of hypothesis testing procedures for analysis of sequence count expression data. Stat. Appl. Genet. Mol. Biol. 11(5), Article 7 (2012)

    Google Scholar 

  31. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2013). http://www.R-project.org/

  32. Robinson, M.D., Oshlack, A.: A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010)

    Article  Google Scholar 

  33. Robinson, M.D., Smyth, G.K.: Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881–2887 (2007)

    Article  Google Scholar 

  34. Robinson, M.D., Smyth, G.K.: Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9, 321–332 (2008)

    Article  MATH  Google Scholar 

  35. Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010)

    Article  Google Scholar 

  36. Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). JRSSB 71(2), 319–392 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  37. Shi, L., Reid, L.H., Jones, W.D., Shippy, R., Warrington, J.A., Baker, S.C., Collins, P.J., de Longueville, F., Kawasaki, E.S., Lee, K.Y., et al.: The microarray quality control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotech. 24, 1151–1161 (2006)

    Article  Google Scholar 

  38. Smyth, G.K.: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, Article 3 (2004)

    Google Scholar 

  39. Smyth, G.K.: Limma: linear models for microarray data. In: Gentleman, R., Carey, V., Dudoit, S., Irizarry, R., Huber, W. (eds.) Bioinformatics and Computational Biology Solutions Using R and Bioconductor, pp. 397–420. Springer, New York (2005)

    Chapter  Google Scholar 

  40. Soneson, C., Delorenzi, M.: A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform. 14, 91 (2013)

    Article  Google Scholar 

  41. Srivastava, S., Chen, L.: A two-parameter generalized Poisson model to improve the analysis of RNA-seq data. Nucleic Acids Res. 38(17), e170 (2010)

    Article  Google Scholar 

  42. Srivastava, S., Chen, L.: GPseq: using the generalized Poisson distribution to model sequence read counts from high throughput sequencing experiments. R Package Version 0.5. (2011). http://CRAN.R-project.org/package=GPseq

  43. Sultan, M., Schulz, M.H., Richard, H., Magen, A., Klingenhoff, A., Scherf, M., Seifert, M., Borodina, T., Soldatov, A., Parkhomchuk, D., et al.: A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321, 956–960 (2008)

    Article  Google Scholar 

  44. Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., Conesa, A.: Differential expression in RNA-seq: a matter of depth. Genome Res. 21, 2213–2223 (2011)

    Article  Google Scholar 

  45. Tarazona, S., Furio-Tari, P., Ferrer, A., Conesa, A.: NOISeq: Exploratory analysis and differential expression for RNA-seq data. R Package Version 2.2.1 (2012)

    Google Scholar 

  46. Tibshirani, R., Chu, G., Narasimhan, B., Li, J.: samr: SAM: significance analysis of microarrays. R Package Version 2.0. (2011). http://CRAN.R-project.org/package=samr

  47. Tierney, L., Rossini, A.J., Li, N., Sevcikova, H.: snow: simple Network of Workstations. R Package Version 0.3–13 (2013). http://CRAN.R-project.org/package=snow

  48. Trapnell, C., Williams, B.A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M.J., Salzberg, S.L., Wold, B.J., Pachter, L.: Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotech. 28, 511–515 (2010)

    Article  Google Scholar 

  49. van de Wiel, M.A., Leday, G.G.R., Pardo, L., Rue, H., van der Vaart, A.W., Van Wieringen, W.N.: Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors. Biostatistics 14, 113–128 (2012)

    Article  Google Scholar 

  50. Wang, Z., Gerstein, M., Snyder, M.: RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009)

    Article  Google Scholar 

  51. Wang, L., Feng, Z., Wang, X., Wang, X., Zhang, X.: DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26, 136–138 (2010)

    Article  Google Scholar 

  52. Yang, E., Girke, T., Jiang, T.: Differential gene expression analysis using coexpression and RNA-seq data. Bioinformatics 29(17), 2153–2161 (2013). doi:10.1093/bioinformatics/btt363

    Article  Google Scholar 

  53. Yendrek, Y.R., Ainsworth, A.A., Thimmaruram, J.: The bench scientist’s guide to statistical analysis of RNA-seq data. BMC Res. Notes 5, 506 (2012)

    Article  Google Scholar 

  54. Young, M.D., Wakefield, M.J., Smyth, G.K., Oshlack, A.: Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010). doi:10.1186/gb-2010-11-2-r14

    Article  Google Scholar 

  55. Zhou, Y., Xia, K., Wright, F.A.: A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics 27(19), 2672–2678 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Bioinformatics and Biostatistics, School of Public Health and Information Science, University of Louisville, 485 E. Gray St., Louisville, KY, 40205, USA

    Douglas J. Lorenz, Ritendranath Mitra & Susmita Datta

  2. Department of Mathematics, University of Louisville, Louisville, KY, 40292, USA

    Ryan S. Gill

Authors
  1. Douglas J. Lorenz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryan S. Gill
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ritendranath Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Susmita Datta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susmita Datta .

Editor information

Editors and Affiliations

  1. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky, USA

    Somnath Datta

  2. Department of Statistics, Iowa State University, Ames, Iowa, USA

    Dan Nettleton

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Lorenz, D.J., Gill, R.S., Mitra, R., Datta, S. (2014). Using RNA-seq Data to Detect Differentially Expressed Genes. In: Datta, S., Nettleton, D. (eds) Statistical Analysis of Next Generation Sequencing Data. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-07212-8_2

Download citation

Publish with us

Policies and ethics

Search

Navigation