Assessing significance in High-Throughput experiments by sequential goodness of fit and Q-Value estimation

PLoS ONE

Volumn 6, Issue 9, 2011, Pages

  Carvajal Rodriguez, Antonio ^a   de Uña Alvarez, Jacobo ^a  

^a UNIVERSITY OF VIGO   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; FALSE POSITIVE RESULT; HIGH THROUGHPUT SCREENING; INFORMATION; METHODOLOGY; SIMULATION; STATISTICAL ANALYSIS; TECHNIQUE; BIOLOGY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; SOFTWARE;

EID: 80052595934     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0024700     Document Type: Article

References (22)

1. Benjamini Y, Hochberg Y, (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57: 289-300.
2. Storey J, (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. The Annals of Statistics 31: 2013-2035.
3. Pounds SB, (2006) Estimation and control of multiple testing error rates for microarray studies. Brief Bioinform 7: 25-36.
4. Storey JD, Tibshirani R, (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: 9440-9445.
5. Zaykin DV, Young SS, Westfall PH, (2000) Using the false discovery rate approach in the genetic dissection of complex traits: a response to Weller et al. Genetics 154: 1917-1918.
6. Carvajal-Rodriguez A, de Uña-Alvarez J, Rolan-Alvarez E, (2009) A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics 10: 209.
7. de Uña-Alvarez J, Carvajal-Rodriguez A, (2010) 'SGoFicance Trace': assessing significance in high dimensional testing problems. PLoS One 5: e15930.
8. Diz AP, Dudley E, MacDonald BW, Pina B, Kenchington EL, et al. (2009) Genetic variation underlying protein expression in eggs of the marine mussel Mytilus edulis. Mol Cell Proteomics 8: 132-144.
9. Carvajal-Rodriguez A, Uña-Álvarez J, Rolán-Álvarez E, (2008) SGoF: Sequential Goodness of Fit Multitest Correction:http://webs.uvigo.es/acraaj/SGoF.htm.
10. de Uña-Alvarez J, (2011) On the statistical properties of SGoF multitesting method. Statistical Applications in Genetics and Molecular Biology 10.
11. Storey JD, (2002) A Direct Approach to False Discovery Rates. Journal of the Royal Statistical Society Series B (Statistical Methodology) 64: 479.
12. Benjamini Y, Yekutieli D, (2005) Quantitative trait loci analysis using the false discovery rate. Genetics 171: 783-790.
13. Farcomeni A, (2008) A review of modern multiple hypothesis testing, with particular attention to the false discovery proportion. Stat Methods Med Res 17: 347-388.
14. Storey JD, Taylor JE, Siegmund D, (2004) Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach. Journal of the Royal Statistical Society Series B-Statistical Methodology 66: 187-205.
15. Carvajal-Rodriguez A, de Uña-Alvarez J, (2011) A simulation study on the impact of strong dependence in high-dimensional multiple-testing I: The case without effects. Salamanca Springer Berlin/Heidelberg pp. 241-246 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011).
16. Efron B, (2007) Correlation and Large-Scale Simultaneous Significance Testing. Journal of the American Statistical Association 102: 93-103.
17. Kim KI, van de Wiel M, (2008) Effects of dependence in high-dimensional multiple testing problems. BMC Bioinformatics 9: 114.
18. Benjamini Y, Yekutieli D, (2001) The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics 29: 1165-1188.
19. Dalmasso C, Broet P, Moreau T, (2005) A simple procedure for estimating the false discovery rate. Bioinformatics 21: 660-668.
20. Meinshausen N, Rice J, (2006) Estimating the Proportion of False Null Hypotheses among a Large Number of Independently Tested Hypotheses. The Annals of Statistics 34: 373.
21. Barry W, Brown KR, (1997) Methods correcting for multiple testing: operating characteristics. Statistics in Medicine 16: 2511-2528.
22. Press WH, (2002) Numerical recipes in C++: the art of scientific computing. Cambridge Cambridge University Press XXVIII, 1002 s. p.