Detecting differential expression from RNA-seq data with expression measurement uncertainty

doi:10.1007/s11704-015-4308-6

Frontiers of Computer Science

2015, Vol. 9

Issue (4): 652-663 https://doi.org/10.1007/s11704-015-4308-6

本期目录

Detecting differential expression from RNA-seq data with expression measurement uncertainty

Li ZHANG,Songcan CHEN,Xuejun LIU(

)

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

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Abstract：

High-throughput RNA sequencing (RNA-seq) has emerged as a revolutionary and powerful technology for expression profiling. Most proposed methods for detecting differentially expressed (DE) genes from RNA-seq are based on statistics that compare normalized read counts between conditions. However, there are few methods considering the expression measurement uncertainty into DE detection. Moreover, most methods are only capable of detecting DE genes, and few methods are available for detecting DE isoforms. In this paper, a Bayesian framework (BDSeq) is proposed to detect DE genes and isoforms with consideration of expression measurement uncertainty. This expression measurement uncertainty provides useful information which can help to improve the performance of DE detection. Three real RAN-seq data sets are used to evaluate the performance of BDSeq and results show that the inclusion of expression measurement uncertainty improves accuracy in detection of DE genes and isoforms. Finally, we develop a GamSeq-BDSeq RNA-seq analysis pipeline to facilitate users.

Key words： RNA-seq Bayesian method differentially expressed genes/isoforms expression measurement uncertainty analysis pipeline

收稿日期: 2014-07-11 出版日期: 2015-09-07

Corresponding Author(s): Xuejun LIU

引用本文:

. [J]. Frontiers of Computer Science, 2015, 9(4): 652-663.
Li ZHANG,Songcan CHEN,Xuejun LIU. Detecting differential expression from RNA-seq data with expression measurement uncertainty. Front. Comput. Sci., 2015, 9(4): 652-663.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-015-4308-6
https://academic.hep.com.cn/fcs/CN/Y2015/V9/I4/652

1	Mortazavi A, Williams A, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 2008, 5(7): 621&horbar;628 https://doi.org/10.1038/nmeth.1226
2	Marioni J, Mason C, Mane S, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 2008, 18: 1509&horbar;1517 https://doi.org/10.1101/gr.079558.108
3	Marguerat S, B?hler J. RNA-seq: from technology to biology. Cellular and Molecular Life Sciences, 2010, 67(4): 569&horbar;579 https://doi.org/10.1007/s00018-009-0180-6
4	Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason C E, Socci N D, Betel D. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biology, 2013, 14(9): R95 https://doi.org/10.1186/gb-2013-14-9-r95
5	Zhang Z H, Jhaveri D J, Marshall VM, Bauer D C, Edson J, Narayanan R K, Zhao Q. A comparative study of techniques for differential expression analysis on RNA-Seq data. PLoS ONE, 2014, 9: e103207 https://doi.org/10.1371/journal.pone.0103207
6	Ozsolak F, Milos P. RNA sequencing: advances, challenges and opportunities. Nature Reviews Genetics, 2011, 12(2): 87&horbar;98 https://doi.org/10.1038/nrg2934
7	Soneson C, Delorenzi M. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics, 2013, 14(1): 9 https://doi.org/10.1186/1471-2105-14-91
8	Kvam V, Lu P, Si Y. A comparison of statistical methods for detecting differentially expressed genes from Rna-Seq data. American Journal of Botany, 2012, 99(2): 248&horbar;256 https://doi.org/10.3732/ajb.1100340
9	Seyednasrollah F, Laiho A, Elo L L. Comparison of software packages for detecting differential expression in RNA-seq studies. Briefings in bioinformatics, 2013, bbt086
10	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. Nature Protocols, 2013, 8(9): 1765&horbar;1786 https://doi.org/10.1038/nprot.2013.099
11	Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology, 2010, 11(10): R106 https://doi.org/10.1186/gb-2010-11-10-r106
12	Hardcastle T, Kelly K. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 2010, 11(1): 422 https://doi.org/10.1186/1471-2105-11-422
13	Di Y, Schafer D, Cumbie J, Chang J. The NBP negative binomial model for assessing differential gene expression from RNA-Seq. Statistical Applications in Genetics and Molecular Biology, 2011, 10(1): 1&horbar;28 https://doi.org/10.2202/1544-6115.1637
14	Yu D, Huber W, Vitek O. Shrinkage estimation of dispersion in negative binomial models for RNA-seq experiments with small sample size. Bioinformatics, 2013, 29(10): 1275&horbar;1282 https://doi.org/10.1093/bioinformatics/btt143
15	Robinson M, Smyth G. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 2007, 23(21): 2881&horbar;2887 https://doi.org/10.1093/bioinformatics/btm453
16	Wu H, Wang C, Wu Z. A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics, 2013, 14(2): 232&horbar;243 https://doi.org/10.1093/biostatistics/kxs033
17	Law CW, Chen Y, Shi W, Smyth G K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, 2014, 15: R29 https://doi.org/10.1186/gb-2014-15-2-r29
18	Bi Y, Davuluri R V. NPEBseq: nonparametric empirical bayesianbased procedure for differential expression analysis of RNA-seq data. BMC bioinformatics, 2013, 14(1): 262 https://doi.org/10.1186/1471-2105-14-262
19	Sandmann T, Vogg M, Owlarn S, Boutros M, Bartscherer K. The headregeneration transcriptome of the planarian Schmidtea mediterranea. Genome Biol, 2011, 12(8): R76 https://doi.org/10.1186/gb-2011-12-8-r76
20	Jiang H, Wong W. Statistical inferences for isoform expression in RNA-Seq. Bioinformatics, 2009, 25(8): 1026&horbar;1032 https://doi.org/10.1093/bioinformatics/btp113
21	Li B, Dewey C. RSEM: accurate transcript quantification from RNASeq data with or without a reference genome. BMC Bioinformatics, 2011, 12(1): 323 https://doi.org/10.1186/1471-2105-12-323
22	Trapnell C, Williams B, Pertea G, Mortazavi A, Kwan G, Baren M, Salzberg S, Wold B, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology, 2010, 28(5): 211&horbar;215 https://doi.org/10.1038/nbt.1621
23	Glaus P, Honkela A, Rattray M. Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics, 2011, 28(13): 1721&horbar;1728 https://doi.org/10.1093/bioinformatics/bts260
24	Leng N, Dawson J, Thomson A, Ruotti V, Rissman A, Smits B M G, Haag J D, Gould M N, Stewart R M, Kendziorski C. EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics, 2013, 29(8): 1035&horbar;1043 https://doi.org/10.1093/bioinformatics/btt087
25	Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley D, Pimentel H, Salzberg S L, Rinn J L, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols, 2012, 7(3): 562&horbar;578 https://doi.org/10.1038/nprot.2012.016
26	Hein A, Richardson S, Causton H, Ambler G, Green P. BGX: a fully Bayesian integrated approach to the analysis of Affymetrix GeneChip data. Biostatistics, 2005, 6(3): 349&horbar;373 https://doi.org/10.1093/biostatistics/kxi016
27	Liu X, Milo M, Lawrence D, Rattray M. Probe-level measurement error improv<?Pub Caret?>es accuracy in detecting differential gene expression. Bioinformatics, 2006, 22(17): 2107&horbar;2113 https://doi.org/10.1093/bioinformatics/btl361
28	Zhang L, Liu X. An improved probabilistic model for finding differential gene expression. In: Proceedings of the 2nd International Conference on Biomedical Engineering and Informatics. 2009, 1-4: 1566&horbar;1571
29	Zhang L, Liu X. A Gamma-based method of RNA-seq analysis. Journal of Nanjing University (Natural Sciences), 2013, 49: 465&horbar;474(in Chinese)
30	Jordan M, Ghahramani Z, Jaakkola T, Saul L. An introduction to variational methods for graphical models. Machine Learning, 1999, 37(2): 183&horbar;233 https://doi.org/10.1023/A:1007665907178
31	Sun J, Kaban A. A fast algorithm for robust mixtures in the presence of measurement errors. IEEE Transactions on Neural Networks, 2010, 21(8): 1206&horbar;1220 https://doi.org/10.1109/TNN.2010.2048219
32	MAQC Consortium. TheMicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotechnology, 2006, 24(9): 1151&horbar;1161 https://doi.org/10.1038/nbt1239
33	Canales R D, Luo Y L, Willey J C, Austermiller B, Barbacioru C C, Boysen C, Hunkapiller K, Jensen R V, Knight C R, Lee K Y, Ma Y Q, Maqsodi B, Papallo A, Peters E H, Poulter K, Ruppel P L, Samaha R R, Shi L M, Yang W, Zhang L, Goodsaid F M. Evaluation of DNA microarray results with quantitative gene expression platforms. Nature Biotechnology, 2006, 24(9): 1115&horbar;1122 https://doi.org/10.1038/nbt1236
34	Griffith M, Griffith OL, Mwenifumbo J, Goya R, Morrissy AS, Morin R D, Corbett R, Tang M J, Hou Y C, Pugh T J, Robertson G, Chittaranjan S, Ally A, Asano J K, Chan S Y, Li H Y I, McDonald H, Teague K, Zhao Y J, Zeng T, Delaney A, Hirst M, Morin G B, Jones S GM, Tai I T, Marra M A. Alternative expression analysis by RNA sequencing. Nature Methods, 2010, 7(10): 843&horbar;847 https://doi.org/10.1038/nmeth.1503
35	Wang E, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore S F, Schroth G P, Burge C B. Alternative isoform regulation in human tissue transcriptomes. Nature, 2008, 456(7221): 470&horbar;476 https://doi.org/10.1038/nature07509

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