An iterative leave-one-out approach to outlier detection in RNA-seq data

PLoS ONE

Volumn 10, Issue 6, 2015, Pages

  George, Nysia I ^a   Bowyer, John F ^a   Crabtree, Nathaniel M ^b   Chang, Ching Wei ^a  

^a NATIONAL CENTER FOR TOXICOLOGICAL RESEARCH   (United States)

^b UNIVERSITY OF ARKANSAS AT LITTLE ROCK   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BINOMIAL DISTRIBUTION; DATA ANALYSIS; ITERATIVE LEAVE ONE OUT ALGORITHM; OUTLIER DETECTION; RNA SEQUENCE; SAMPLE SIZE; SIMULATION; STATISTICAL ANALYSIS; DNA SEQUENCE; GENETICS; NUCLEIC ACID DATABASE; PROCEDURES; SEQUENCE ANALYSIS;

DNA; RNA;

DATABASES, NUCLEIC ACID; DNA; RNA; SEQUENCE ANALYSIS, DNA; SEQUENCE ANALYSIS, RNA;

EID: 84931265950     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0125224     Document Type: Article

References (17)

1. Li J, Tibshirani R (2013) Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Statistical methods in medical research 22: 519-536. doi: 10.1177/0962280211428386 PMID: 22127579
2. Zhou X, Lindsay H, Robinson MD (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic acids research: gku310.
3. Dillies M- A, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, et al. (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Briefings in bioinformatics 14: 671-683. doi: 10.1093/bib/bbs046 PMID: 22988256
4. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139-140. doi: 10.1093/bioinformatics/btp616 PMID: 19910308
5. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11: R25. doi: 10.1186/gb-2010-11-3-r25 PMID: 20196867
6. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome biology 15: 550. PMID: 25516281
7. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome biol 11: R106. doi: 10.1186/gb-2010-11-10-r106 PMID: 20979621
8. Huber PJ (2011) Robust statistics: Springer.
9. Cook RD (1977) Detection of influential observation in linear regression. Technometrics: 15-18.
10. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, et al. (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456: 470-476. doi: 10.1038/nature07509 PMID: 18978772
11. Frazee AC, Langmead B, Leek JT (2011) ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC bioinformatics 12: 449. doi: 10.1186/1471-2105-12-449 PMID: 22087737
12. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research 18: 1509-1517. doi: 10.1101/gr.079558.108 PMID: 18550803
13. Wu H, Wang C, Wu Z (2013) A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics 14: 232-243. doi: 10.1093/biostatistics/kxs033 PMID: 23001152
14. Hardcastle TJ, Kelly KA (2010) baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC bioinformatics 11: 422. doi: 10.1186/1471-2105-11-422 PMID: 20698981
15. Soneson C, Delorenzi M (2013) A comparison of methods for differential expression analysis of RNA-seq data. BMC bioinformatics 14: 91. doi: 10.1186/1471-2105-14-91 PMID: 23497356
16. Pieters E, Gates C, Matis J, Sterling W (1977) Small sample comparison of different estimators of negative binomial parameters. Biometrics: 718-723.
17. Landau WM, Liu P (2013) Dispersion Estimation and Its Effect on Test Performance in RNA-seq Data Analysis: A Simulation-Based Comparison of Methods. PloS one 8: e81415. doi: 10.1371/journal.pone.0081415 PMID: 24349066