Abstract
A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1. Relevant scientific contributions concerning the use of ROC curves for microarray data are briefly reviewed in Subheading 2. The special case with covariates is considered in Subheading 3. Two relevant aspects are reviewed in this section: the use of LASSO techniques for selecting and combining relevant markers and how to correct for multiple testing when a large number of markers are available. Finally, some conclusions are included.
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References
Green DM, Swets JA (1966) Signal detection theory and psychophysics. Wiley, New York
Swets JA, Pickett RM (1982) Evaluation of diagnostic systems: methods from signal detection theory. Academic Press, New York
Hanley JA (1989) Receiver operating characteristic (ROC) methodology: the state of the art. Crit Rev Diagn Imaging 29(2):307–335
Pepe MS (1997) A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing. Biometrika 84:595–608
Metz CE, Herman BA, Shen JH (1998) Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. Stat Med 17:1033–1053
Alonzo TA, Pepe MS (2002) Distribution-free ROC analysis using binary regression techniques. Biostatistics 3:421–432
Pepe MS, Longton G (2005) Standardizing markers to evaluate and compare their performances. Epidemiology 16:598–603
Gu W, Pepe MS (2009) Estimating the capacity for improvement in risk prediction with a marker. Biostatistics 10:172–186
Huang Y, Pepe MS (2009) Biomarker evaluation and comparison using the controls as a reference population. Biostatistics 10:228–244
Pepe MS, Longton G, Janes H (2009) Estimation and comparison of receiver operating characteristic curves. Stata J 9:1–16
Mari JD, Williams P (1985) A comparison of the validity of 2 psychiatric screening questionnaires (GHQ-12 and SRQ-20) in Brazil, using relative operating characteristic (ROC) analysis. Psychol Med 15(3):651–659
Greiner M, Sohr D, Gobel P (1995) A modified ROC analysis for the selection of cutoff values and the definition of intermediate results of serodiagnostic tests. J Immunol Methods 185(1):123–132
Rankinen T, Kim SY, Perusse L, Despres JP, Bouchard C (1999) The prediction of abdominal visceral fat level from body composition and anthropometry: ROC analysis. Int J Obes 23(8):801–809
Chan HP, Sahiner B, Helvie MA, Petrick N, Roubidoux MA, Wilson TE, Adler DD, Paramagul C, Newman JS, Sanjay-Gopal S (1999) Improvement of radiologists’ characterization of mammographic masses by using computer-aided diagnosis: an ROC study. Radiology 212(3):817–827
Baker SG (2003) The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. J Natl Cancer Inst 95:511–515
Pepe MS, Longton G, Anderson GL, Schummer M (2003) Selecting differentially expressed genes from microarray experiments. Biometrics 59:133–142
Tsai CA, Chen JJ (2004) Significance analysis of ROC indices for comparing diagnostic markers: applications to gene microarray data. J Biopharm Stat 14(4):985–1003
Berrar D, Flach P (2012) Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them). Brief Bioinform 13(1):83–97
Swamidass SJ, Azencott CA, Daily K, Baldi P (2010) A CROC stronger than ROC: measuring, visualizing and optimizing early retrieval. Bioinformatics 26(10):1348–1356
Pepe MS, Thompson ML (2000) Combining diagnostic test results to increase accuracy. Biostatistics 1(2):123–140
Pepe MS, Cai T, Longton G (2006) Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 62(1):221–229
Liu C, Liu A, Halabi S (2011) A min-max combination of biomarkers to improve diagnostic accuracy. Stat Med 30(16):2005–2014
Kang L, Liu A, Tian L (2016) Linear combination methods to improve diagnostic/prognostic accuracy on future observations. Stat Methods Med Res 25(4):1359–1380
Tibshirani R (1996) Regression shrinkage and selection via the LASSO. J R Stat Soc Ser B 58(1):267–288
Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89(428):1255–1270
Hastie T, Buja A, Tibshirani R (1995) Penalized discriminant analysis. Ann Stat 23(1):73–102
Ghosh D, Chinnaiyan AM (2005) Classification and selection of biomarkers in genomic data using LASSO. J Biomed Biotechnol 2:147–154
Hochberg Y, Tamhane AC (1987) Multiple comparison procedures. Wiley, New York
Westfall PH, Young SS (1993) Resampling-based multiple testing. Wiley, New York
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B 57:289–300
Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29(4):1165–1188
Zaykin DV, Young SS, Westfall PH (2000) Letter to editor. using the false discovery rate in the genetic dissection of complex traits. Genetics 154:1917–1918
Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98:5116–5121
Hsueh HM, Chen JJ, Kodell RL (2003) Comparison of methods for estimating the number of true null hypotheses in multiplicity testing. J Biopharm Stat 13(4):675–689
Tsai CA, Hsueh HM, Chen JJ (2003) Estimation of false discovery rates in multiple testing: application to gene microarray data. Biometrics 59(4):1071–1081
Delongchamp RR, Bowyer JF, Chen JJ, Kodell RL (2004) Multiple-testing strategy for analyzing cDNA array data on gene expression. Biometrics 60(3):774–782
Chen JJ, Wang SJ, Tsai CA, Lin CJ (2007) Selection of differentially expressed genes in microarray data analysis. Pharmacogenomics J 7:212–220
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Cao, R., López-de-Ullibarri, I. (2019). ROC Curves for the Statistical Analysis of Microarray Data. In: Bolón-Canedo, V., Alonso-Betanzos, A. (eds) Microarray Bioinformatics. Methods in Molecular Biology, vol 1986. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9442-7_11
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DOI: https://doi.org/10.1007/978-1-4939-9442-7_11
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