SimSeq: A nonparametric approach to simulation of RNA-sequence datasets

Bioinformatics

Volumn 31, Issue 13, 2015, Pages 2131-2140

  Benidt, Sam ^a   Nettleton, Dan ^a  

^a Department of Statistics Center for Statistics and Applications in Forensic Evidence Iowa State University 2438 Osborn Dr Ames   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BINOMIAL DISTRIBUTION; BIOLOGY; COMPUTER PROGRAM; GENE EXPRESSION PROFILING; HIGH THROUGHPUT SEQUENCING; HUMAN; NONPARAMETRIC TEST; PROCEDURES; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS;

ALGORITHMS; BINOMIAL DISTRIBUTION; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, RNA; SOFTWARE; STATISTICS, NONPARAMETRIC;

EID: 84936777677     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv124     Document Type: Article

References (28)

1. Anders,S. and Huber,W. (2010) Differential expression analysis for sequence count data. Genome Biol., 11, R106.
2. Ashburner,M. et al. (2000) Gene ontology: tool for the unification of biology. Nat. Genet., 25, 25-29.
3. Benjamini,Y. and Hochberg,Y. (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. B, 57, 289-300.
4. Bottomly,D. et al. (2011) Evaluating gene expression in c57bl/6j and dba/2j mouse striatum using rna-seq and microarrays. PLoS One, 6, e17820.
5. Bullard,J. et al. (2010) Evaluation of statisticalmethods for normalization and differential expression inmRNA-Seq experiments.BMC Bioinformatics, 11, 94.
6. Burden,C. et al. (2014) Error estimates for the analysis of differential expression from RNA-seq count data. Peer J. 2, e576.
7. Dillies,M.-A. et al. (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief. Bioinform., 14, 671-683.
8. Gadbury,G.L. et al. (2008) Evaluating statistical methods using plasmode data sets in the age of massive public databases: an illustration using false discovery rates. PLoS Genet., 4, e1000098.
9. Griebel,T. et al. (2012) Modelling and simulating generic RNA-Seq experiments with the flux simulator. Nucleic Acids Res., 40, 10073-10083.
10. Law,C.W. et al. (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol., 15, R29.
11. Li,B. and Dewey,C. (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12, 323.
12. Li,J. and Tibshirani,R. (2013) Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Stat. Methods Med. Res., 22, 519-536.
13. Liang,K. and Nettleton,D. (2010) A hidden Markov model approach to testing multiple hypotheses on a tree-transformed gene ontology graph. J. Am. Stat. Assoc., 105, 1444-1454.
14. Love,M.I. et al. (2014) Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biol., 15, 550.
15. Lund,S. et al. (2012) Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Stat. Appl. Genet. Mol. Biol., 11, 8.
16. McCarthy,D.J. et al. (2012) Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res., 40, 4288-4297.
17. Nettleton,D. et al. (2008) Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis. Bioinformatics, 24, 192-201.
18. Reeb,P.D. and Steibel,J.P. (2013) Evaluating statistical analysis models for RNA sequencing experiments. Front. Genet., 4, 178.
19. Robinson,D.G. and Storey,J.D. (2014) subSeq: Determining appropriate sequencing depth through efficient read subsampling. Bioinformatics, 30, 3424-3426.
20. Robinson,M.D. and Oshlack,A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25.
21. Robinson,M.D. and Smyth,G.K. (2007) Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 23, 2881-2887.
22. Robinson,M.D. and Smyth,G.K. (2008) Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332.
23. Robinson,M.D. et al. (2010) edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140.
24. Soneson,C. and Delorenzi,M. (2013) A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics, 14, 91.
25. Storey,J.D. (2002) A direct approach to false discovery rates. J. R. Stat. Soc. B, 64, 479-498.
26. Strimmer,K. (2008a) fdrtool: A versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics, 24, 1461-1462.
27. Strimmer,K. (2008b) A unified approach to false discovery rate estimation. BMC Bioinformatics, 9, 303.
28. The Cancer Genome Atlas Research Network (2013) Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature, 499, 43-49.