A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research

Pharmacogenomics

Volumn 10, Issue 3, 2009, Pages 345-358

  Zhang, Xiaohua Douglas ^a  

^a MERCK RESEARCH LABORATORIES   (United States)

Author keywords

c+ probability; Comparing gene effects; Contrast variable; Expression profiling; High throughput; RNAi; Standardized mean of contrast; Statistical analysis

Indexed keywords

ARTICLE; BIOSTATISTICS; C PLUS PROBABILITY; CONTRAST ANALYSIS; CONTRAST VARIABLE; CONTROLLED STUDY; DATA ANALYSIS; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE FUNCTION; GENETIC ANALYSIS; HIGH THROUGHPUT SCREENING; INTERMETHOD COMPARISON; PROBABILITY; RNA INTERFERENCE; STANDARDIZED MEAN OF CONTRAST; STATISTICAL ANALYSIS; STATISTICAL CONCEPTS;

GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; MICROARRAY ANALYSIS; RNA INTERFERENCE;

EID: 66849087837     PISSN: 14622416     EISSN: 17448042     Source Type: Journal    
DOI: 10.2217/14622416.10.3.345     Document Type: Article

References (29)

1. Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207-210 (2002).
2. Howe D, Costanzo, M, Fey P et al.: Big data: The future of biocuration. Nature 455(7209), 47-50 (2008).
3. Paddison PJ, Silva JM, Conklin DS et al.: A resource for large-scale RNA-interfefence-based screens in mammals. Nature 428(6981), 427-431 (2004).
4. Zhang XHD, Ferrer M, Espeseth AS et al.: The use of strictly standardized mean difference for hit selection in primary RNA interference high-throughput screening experiments. J. Biomol. Screen. 12(4), 497-509 (2007).
5. Zhang XHD, Espeseth AS, Johnson EN et al.: Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens. J. Biomol. Screen. 130), 378-389 (2008).
6. Shao Y, Tseng CH: Sample size calculation with dependence adjustment for FDR-control in microarray studies. Stat. Med. 26(23), 4219-4237 (2007).
7. Wu CC, Wang SJ, Zhang HB: Interactions among genomic structure, function, and evolution revealed by comprehensive analysis of the Arabidopsis thaliana genome. Genomics 88(4), 394-406 (2006).
8. Wu XD, Cooper RS, Borecki I et al.: A combined analysis of genomewide linkage scans for body mass index, from the National Heart, Lung, and Blood Institute Family Blood Pressure Program. Am. J. Hum. Genet. 70(5), 1247-1256 (2002).
9. Wu XD, Cooper RS, Boerwinkle E et al.: Combined analysis of genomewide scans for adult height: from the NHLB1 family blood pressure program. Eur. J. Hum. Genet. 11(3), 271-274 (2003).
10. Wu XD, Luke A, Cooper RS et al.:A genome scan among Nigerians linking resting energy expenditure to chromosome 16. Obes. Res. 12(4), 577-581 (2004).
11. Barkerand GA, Diamond SL: RNA interference screen to identify pathways that enhance or reduce nonviral gene transfer during lipofection. Mol. Ther. 16(9), 1602-1608 (2008).
12. Hannon Gi, Zamore PD: Small RNAs, big biology: biochemical studies of RNA interference. In: A Guide to Gene Silencing. Hannon GJ (Ed.). Cold Spring Harbor Laboratory Press, NY, USA, 87-108 (2003)
13. Willingham AT, Deveraux QL, Hampton GM, Aza-Blanc P: RNAi and HTS: exploring cancer by systematic loss-of-function. Oncogene 23(51), 8392-8400 (2004).
14. Zhang XHD, Yang XC, Chung NJ et al.: Robust statistical methods for hit selection in RNA interference high-throughput screening experiments. Pharmacogenomics 7(3), 299-309 (2006).
15. Zhang XHD, Kuan PF, Ferrer M et al.: Hit selection with false discovery rate control in genome-scale RNAi screens. Nucleic Acids Res. 36(14), 4667-4679 (2008).
16. Barrett T, Troup DB, Wilhite SE et al.: NCBI GEO: mining tens of millions of expression profiles - database and tools update. Nucleic Acids Res. 35, D760-D765 (2007).
17. Eisenstein M: Quality control. Nature 442(7106), 1067-1070 (2006).
18. Zhang XHD: A new method with flexible and balanced control of false negatives and false positives for hit selection in RNA interference high-throughput screening assays. J. Biomol. Screen. 12(5), 645-655 (2007).
19. Shoemaker J: Statistical challenges with gene expression studies. Pharmacogenomics 7(3), 511-519 (2006).
20. Barrios-Rodiles M, Brown KR, Czdamar B et al.: High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307(5715), 1621-1625(2005).
21. Ioannidis JPA: Microarrays and molecular research: noise discovery? Lancet 365(9458), 454-455 (2005).
22. Ioannidis JPA: Why most published research findings are false. PLoS Med. 2(8), 696-701 (2005).
23. Sterne JAC, Smith GD: Sifting the evidence - what's wrong with significance tests? BMJ 322(7280), 226-231 (2001).
24. Zhang XHD: Genome-wide screens for effective siRNAs through assessing the size of siRNA effects. BMC Res. Notes 1(1), 33 (2008).
25. Zhang XHD: Novel analytic criteria and effective plate designs for quality control in genome-scale RNAi screens. J. Biomol. Screen. 13(5), 363-377 (2008).
26. Zhan FH, Barlogie B, Arzoumanian V et al.: Gene-expression signature of benign monoclonal gammopathy evident in multiple myeloma is linked to good prognosis. Blood 109(4), 1692-1700 (2007).
27. Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98(9), 5116-5121 (2001).
28. Vysochanskij DF, Petunin YI: Justification of the 3σ-a rule for unimodal distribution. Theor. Probability Mathem. Statistics 21, 25-36 (1980).
29. Zhang XHD: A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays. Genomics 89(4), 552-561 (2007).