Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments

BMC Bioinformatics

Volumn 9, Issue , 2008, Pages

  Parodi, Stefano ^a   Pistoia, Vito ^a   Muselli, Marco ^b  

^a G GASLINI INSTITUTE   (Italy)

^b CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER THE ROC CURVE; CHRONIC LYMPHOCYTIC LEUKEMIAS; DIFFERENTIALLY EXPRESSED GENE; ENVIRONMENTAL STIMULI; FALSE DISCOVERY RATE; FOLLICULAR LYMPHOMA; MICROARRAY EXPERIMENTS; SELECTION PROCEDURES;

EXPERIMENTS; TOOLS;

GENE EXPRESSION;

ARTICLE; B LYMPHOCYTE; CHRONIC LYMPHATIC LEUKEMIA; CONTROLLED STUDY; DATA ANALYSIS; FOLLICULAR LYMPHOMA; GENE EXPRESSION; GENE IDENTIFICATION; GENETIC ANALYSIS; GENETIC DATABASE; GENETIC SELECTION; MICROARRAY ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; ROC CURVE; SENSITIVITY AND SPECIFICITY; ANIMAL; BIOSYNTHESIS; DNA MICROARRAY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HUMAN; LYMPHOMA; METABOLISM; METHODOLOGY; PREDICTION AND FORECASTING;

TUMOR PROTEIN;

ANIMALS; DATABASES, GENETIC; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; LYMPHOMA; NEOPLASM PROTEINS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PREDICTIVE VALUE OF TESTS; ROC CURVE;

EID: 55349084073     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-410     Document Type: Article

References (21)

1. Quackenbush J Microarray analysis and tumor classification N Engl J Med 2006, 354:2463-2472. 10.1056/NEJMra042342 16760446
2. Gusnanto A Calza S Pawitan Y Identification of differentially expressed genes and false discovery rate in microarray studies Curr Opin Lipidol 2007, 18:187-193. 10.1097/MOL.0b013e3280895d6f 17353668
3. Dudoit S Yang YH Speed TP Callow MJ Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments Statistica Sinica 2002, 12:111-139.
4. Jeffery IB Higgins DG Culhane AC Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data BMC Bioinformatics 2006, 7:359. 1544358 16872483 10.1186/1471-2105-7-359
5. Pepe MS The statistical evaluation of medical tests for classification and prediction Oxford (UK): Oxford University Press; 2003.
6. Parodi S Muselli M Fontana V Bonassi S ROC curves are a suitable and flexible tool for the analysis of gene expression profiles Cytogenet Genome Res 2003, 101:90-91. 10.1159/000074404 14571143
7. Baker SG The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer J Natl Cancer Inst 2003, 95:511-515. 12671018
8. Pepe MS Longton G Anderson GL Schummer M Selecting differentially expressed genes from microarray experiments Biometrics 2003, 59:133-142. 10.1111/1541-0420.00016 12762450
9. Metz CE Herman BA Shen JH Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data Stat Med 1998, 17:1033-1053. 10.1002/ (SICI)1097-0258(19980515)17:9<1033::AID-SIM784>3.0.CO;2-Z 9612889
10. Lee WC Hsiao CK Alternative summary indices for the receiver operating characteristic curve Epidemiology 1996, 7:605-611. 8899386
11. Alizadeh AA Eisen MB Davis RE Ma C Lossos IS Rosenwald A Boldrick JC Sabet H Tran T Yu X Powell JI Yang L Marti GE Moore T Hudson J Jr Lu L Lewis DB Tibshirani R Sherlock G Chan WC Greiner TC Weisenburger DD Armitage JO Warnke R Levy R Wilson W Grever MR Byrd JC Botstein D Brown PO et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Nature 2000, 403:503-511. 10.1038/35000501 10676951
12. Tsai CA Chen JJ Significance analysis or ROC indices for comparing diagnostic markers: Applications to gene microarray data J Biopharm Stat 2004, 14:985-1003. 10.1081/BIP-200035475 15587976
13. OMIM, Online Mendelian Inheritance in Man http://www.ncbi.nlm.nih.gov/ omim
14. Koshland ME The coming of age of the immunoglobulin J chain Annu Rev Immunol 1985, 3:425-453. 10.1146/annurev.iy.03.040185.002233 2415140
15. Karagianni P Wong J HDAC3: Taking the SMRT-N-CoRrect road to repression Oncogene 2007, 26:5439-5449. 10.1038/sj.onc.1210612 17694085
16. Baker SG Kramer BS Identifying genes that contribute most to good classification in microarrays BMC Bioinformatics 2006, 7:407. 1574352 16959042 10.1186/1471-2105-7-407
17. Troyanskaya O Cantor M Sherlock G Brown P Hastie T Tibshirani R Botstein D Altman RB Missing value estimation methods for DNA microarrays Bioinformatics 2001, 17:520-525. 10.1093/bioinformatics/17.6.520 11395428
18. Sprott JC Numerical Recipes Software Numerical recipes: Routine and examples in BASIC New York (USA): Cambridge University Press; 1998.
19. Tsai CA Hsueh H Chen JJ Estimation of false discovery rates in multiple testing: Application to gene microarray data Biometrics 2003, 59:1071-1081. 10.1111/j.0006-341X.2003.00123.x 14969487
20. Tusher VG Tibshirani R Chu G Significance analysis of microarrays applied to the ionizing radiation response Proc Natl Acad Sci USA 2001, 98:5116-5121. 33173 11309499 10.1073/pnas.091062498
21. Bamber D The Area above the Ordinal Dominance Graph and the Area below the Receiver Operating Characteristic Graph Journal of Math Psychology 1975, 12:387-415. 10.1016/0022-2496(75)90001-2