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Combining Not-Proper ROC Curves and Hierarchical Clustering to Detect Differentially Expressed Genes in Microarray Experiments

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8452))

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Abstract

TNRC (Test for Not Proper ROC Curve) is a statistical tool recently developed to identify differently expressed genes in microarray studies. In previous investigations it was demonstrated to be able to separate hidden subgroups in a two-class experiment, but being a univariate technique it could not exploit the complex multivariate correlation naturally occurring in gene expression data. In this study we show as the combination of TNRC with a standard technique of hierarchical clustering may provide useful biological insights. An example is provided using data from a publicly available data set of 4026 gene expression profiles in 42 samples of lymphomas and 14 samples of normal B cells.

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References

  1. Kampfrath, T., Levinson, S.S.: Brief critical review: statistical assessment of biomarker performance. Clin. Chim. Acta 419, 102–107 (2013)

    Article  Google Scholar 

  2. Alemayehu, D., Zou, H.: Applications of ROC analysis in medical research: recent developments and future directions. Acad. Radiol. 19, 1457–1464 (2012)

    Article  Google Scholar 

  3. Parodi, S., Muselli, M., Carlini, B., Fontana, V., Haupt, R., Pistoia, V., Corrias, M.V.: Restricted ROC curves are useful tools to evaluate the performance of tumour markers. Stat. Methods Med. Res. 26 Jun 2012 [epub ahead of print]

    Google Scholar 

  4. Pepe, M.: The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, Oxford (2003)

    MATH  Google Scholar 

  5. Silva-Fortes, C., Turkman, M.A., Sousa, L.: Arrow plot: a new graphical tool for selecting up and down regulated genes and genes differentially expressed on sample subgroups. BMC Bioinf. 13, 147 (2012)

    Article  Google Scholar 

  6. Pepe, M., et al.: Selecting differentially expressed genes from microarray experiments. Biometrics 59, 133–142 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  7. Lee, W., Hsiao, C.: Alternative summary indices for the receiver operating characteristic curve. Epidemiology 7, 605–611 (1996)

    Article  Google Scholar 

  8. Lee, W.: Probabilistic analysis of global performances of diagnostic tests: interpreting the Lorentz curve-based summary measures. Stat. Med. 18, 455–471 (1999)

    Article  Google Scholar 

  9. Kagaris, D., Yiannoutsos, C.: A multi-index ROC based methodology for high throughput experiments in gene discovery. Int. J. Data Min. Bioinf. 8, 42–65 (2013)

    Article  Google Scholar 

  10. Parodi, S., Pistoia, V., Muselli, M.: Not proper ROC curves as new tool for the analysis of differentially expressed genes in microarray experiments. BMC Bioinf. 9, 410 (2008)

    Article  Google Scholar 

  11. Alizadeh, A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene. Nature 403, 503–511 (2000)

    Article  Google Scholar 

  12. Michiels, S., Kramar, A., Koscielny, S.: Multidimensionality of microarrays: statistical challenges and (im)possible solutions. Mol. Oncol. 5, 190–196 (2011)

    Article  Google Scholar 

  13. Bamber, D.: The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. J. Math. Psychol. 12, 387–415 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  14. 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. 101, 90–91 (2003)

    Article  Google Scholar 

  15. Gibbons, J.D., Chakraborti, S.: Nonparametric Statistical Inference, 4th edn. Marcel Dekker Inc, New York (2003)

    MATH  Google Scholar 

  16. Tusher, V., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Nat. Acad. Sci. USA 98, 5116–5121 (2001)

    Article  MATH  Google Scholar 

  17. Hastie, T., Tibshirani, R., Friedman, J.: Hierarchical clustering. The Elements of Statistical Learning, 2nd edn, pp. 520–528. Springer, New York (2009)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Via De Marini 6, 16149, Genoa, Italy

    Stefano Parodi & Marco Muselli

  2. Laboratory of Oncology, G. Gaslini Children’s Hospital, Largo G. Gaslini, 16147, Genoa, Italy

    Vito Pistoia

Authors
  1. Stefano Parodi
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  2. Vito Pistoia
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  3. Marco Muselli
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Corresponding author

Correspondence to Marco Muselli .

Editor information

Editors and Affiliations

  1. University Nice Sophia Antipolis, Sophia Antipolis, France

    Enrico Formenti

  2. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

  3. University of Groningen, AG Groningen, The Netherlands

    Ernst Wit

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Parodi, S., Pistoia, V., Muselli, M. (2014). Combining Not-Proper ROC Curves and Hierarchical Clustering to Detect Differentially Expressed Genes in Microarray Experiments. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-09042-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09041-2

  • Online ISBN: 978-3-319-09042-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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