How high is the level of technical noise in microarray data?

Biology Direct

Volumn 2, Issue , 2007, Pages

  Klebanov, Lev ^a,b   Yakovlev, Andrei ^a,b  

^a CHARLES UNIVERSITY   (Czech Republic)

^b UNIVERSITY OF ROCHESTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 34248177279     PISSN: None     EISSN: 17456150     Source Type: Journal    
DOI: 10.1186/1745-6150-2-9     Document Type: Review

References (17)

1. Marshall E Getting the noise out of gene arrays Science 2004 306 630-631 10.1126/science.306.5696.630 15499004
2. Cobb K Microarrays: The search for meaning in a vast sea of data Biomed Comput Rev 2006 2 4 16-23
3. Shi L Reid LH Jones WD Shippy R Warrington JA Baker SC Collins PJ de Longueville F Kawasaki ES Lee KY Lou Y Sun YA Willey JC Setterquist RA Fischer GM Tong W Dragan YP Dix DJ Frueh FW Goodsaid FM Herman D Jensen RV Johnson CD Lobenhofer EK Puri RK Sherf U Thierry-Mieg J Wang C Wilson M Wolber PK The microarray quality control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements Nat Biotechnol 2006 24 9 1151-1161 10.1038/nbt1239 16964229
4. Klebanov L Yakovlev A Treating expression levels of different genes as a sample in microarray data analysis: Is it worth a risk? Statist Appl Genet Mol Biol 2006 5 9
5. Qiu X Klebanov L Yakovlev AY Correlation between gene expression levels and limitations of the empirical Bayes methodology for finding differentially expressed genes Statist Appl Genet Mol Biol 2005 4 34
6. Klebanov L Qiu X Welle S Yakovlev A Statistical methods and microarray data Nat Biotechnol 2007 25 1 25-26 10.1038/nbt0107-25 17211383
7. Yeoh EJ Ross ME Shurtleff SA Williams WK Patel D Mahfouz R Behm FG Raimondi SC Relling MV Patel A Cheng C Campana D Wilkins D Zhou X Li J Liu H Pui CH Evans WE Naeve C Wong L Downing JR Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling Cancer Cell 2002 1 2 133-143 10.1016/S1535-6108(02)00032-6 12086872
8. Dai M Wang P Boyd AD Kostov G Athey B Jones EG Bunney WR Myers RM Speed TP Akil H Watson SJ Meng F Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data Nucl Acids Res 2005 33 20 e175 1283542 16284200 10.1093/nar/gni179
9. Qiu X Brooks AI Klebanov L Yakovlev A The effects of normalization on the correlation structure of microarray data BMC Bioinformatics 2005 6 120 1156869 15904488 10.1186/1471-2105-6-120
10. Almudevar A Klebanov LB Qiu X Salzman P Yakovlev AY Utility of correlation measures in analysis of gene expression NeuroRx 2006 3 3 384-395 10.1016/j.nurx.2006.05.037 16815221
11. Qiu X Yakovlev A Some comments on instability of false discovery rate estimation J Bioinformatics and Comput Biol 2006 4 5 1057-1068 10.1142/ S0219720006002338
12. Chen L Klebanov L Yakovlev AY Normality of gene expression revisited J Biol Syst 2007
13. Lander EC Array of hope Nat Genetics 1999 21 3-4 10.1038/4427
14. Frantz S An array of problems Nat Rev Drug Discov 2005 4 362-363 10.1038/ nrd1746 15902768
15. Klebanov L Yakovlev A Is there an alternative to increasing the sample size in microarray studies? Bioinformation 2007
16. Parmigiani G Garrett ES Irizarry RL Zeger SL The analysis of gene expression data: An overview of methods and software The Analysis of Gene Expression Data Springer, New York Parmigiani G, Garrett ES, Irizarry RL, Zeger SL 2003
17. Zakharkin SO Kim K Mehta T Chen L Barnes S Scheirer E Parrish RS Allison DB Page GP Sources of variation in Affymetrix microarray experiments BMC Bioinformatics 2005 6 214 1232851 16124883 10.1186/1471-2105-6-214