Statistical strategies for microRNAseq batch effect reduction

Translational Cancer Research

Volumn 3, Issue 3, 2014, Pages 260-265

  Guo, Yan ^a   Zhao, Shilin ^a   Su, Pei Fang ^b   Li, Chung I ^c   Ye, Fei ^a   Flynn, Charles R ^a   Shyr, Yu ^a  

^a VANDERBILT INGRAM CANCER CENTER   (United States)

^b NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^c NATIONAL CHIAYI UNIVERSITY   (Taiwan)

Author keywords

Batch effect removal; miRNA sequencing; Normalization

Indexed keywords

EID: 84962648175     PISSN: 2218676X     EISSN: 22196803     Source Type: Journal    
DOI: 10.3978/j.issn.2218-676X.2014.06.05     Document Type: Article

References (23)

1. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10:57-63.
2. Shendure J. The beginning of the end for microarrays? Nat Methods 2008;5:585-7.
3. Guo Y, Sheng Q, Li J, et al. Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data. PLoS One 2013;8:e71462.
4. Chen K, Rajewsky N. The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet 2007;8:93-103.
5. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009;136:215-33.
6. Kusenda B, Mraz M, Mayer J, et al. MicroRNA biogenesis, functionality and cancer relevance. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2006;150:205-15.
7. Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 2011;39:D152-7.
8. Jiang Q, Wang Y, Hao Y, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 2009;37:D98-104.
9. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008;5:621-8.
10. Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010;28:511-5.
11. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118-27.
12. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 2010;11:733-9.
13. Bullard JH, Purdom E, Hansen KD, et al. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010;11:94.
14. Li J, Witten DM, Johnstone IM, et al. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 2012;13:523-38.
15. Guo Y, Samuels DC, Li J, et al. Evaluation of allele frequency estimation using pooled sequencing data simulation. Scientific World Journal 2013;2013:895496.
16. Guo Y, Cai Q, Li C, et al. An evaluation of allele frequency estimation accuracy using pooled sequencing data. Int J Comput Biol Drug Des 2013;6:279-93.
17. Guo Y, Li CI, Ye F, et al. Evaluation of read count based RNAseq analysis methods. BMC Genomics 2013;14 Suppl 8:S2.
18. Sun Z, Chai HS, Wu Y, et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med Genomics 2011;4:84.
19. Hansen KD, Irizarry RA, Wu Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 2012;13:204-16.
20. 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.
21. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40.
22. Guo Y, Zhao S, Ye F, et al. MultiRankSeq: Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control. BioMed Research International 2014;2014:8.
23. Auer PL, Doerge RW. Statistical design and analysis of RNA sequencing data. Genetics 2010;185:405-16.