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Discriminant Analysis and Normalization Methods for Next-Generation Sequencing Data

  • Yan Zhou
  • Junhui Wang
  • Yichuan Zhao
  • Tiejun Tong
Chapter
Part of the ICSA Book Series in Statistics book series (ICSABSS)

Abstract

Next-generation sequencing has become a powerful tool for gene expression analysis with the development of high-throughput techniques. Discriminating which type of diseases a new sample belongs to is a fundamental issue in medical and biological studies. Different from continuous microarray data, next-generation sequencing reads are mapped onto the reference genome and are discrete data. Consequently, existing discriminant analysis methods for microarray data may not be readily applicable for next-generation sequencing data. In recent years, a number of new discriminant analysis methods have been proposed to discriminate next-generation sequencing data. In this chapter, we introduce three such methods including the Poisson linear discriminant analysis, the zero-inflated Poisson logistic discriminant analysis, and the negative binomial linear discriminant analysis. In view of the importance, we further introduce several normalization methods for processing next-generation sequencing data. Simulation studies and two real datasets are also carried out to demonstrate the usefulness of the newly developed methods.

Notes

Acknowledgements

The authors thank the editor and two referees for their helpful comments that have led to some significant improvements of this chapter. Yan Zhou’s research was supported by the National Natural Science Foundation of China (Grant No. 11701385), National Statistical Research Project (Grant No. 2017LY56), the Doctor Start Fund of Guangdong Province (Grant No. 2016A030310062), and the National Social Science Foundation of China (Grant No. 15CTJ008). Junhui Wang’s research was supported by HK RGC grants GRF-11302615 and GRF-11331016. Yichuan Zhao’s research was partially supported by the NSF Grant DMS-1406163 and NSA Grant H98230-12-1-0209. Tiejun Tong’s research was supported by the Health and Medical Research Fund (Grant No. 04150476) and the National Natural Science Foundation of China (Grant No. 11671338).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yan Zhou
    • 1
  • Junhui Wang
    • 2
  • Yichuan Zhao
    • 3
  • Tiejun Tong
    • 4
  1. 1.College of Mathematics and Statistics, Institute of Statistical SciencesShenzhen UniversityShenzhenChina
  2. 2.School of Data ScienceCity University of Hong KongKowloonHong Kong
  3. 3.Department of Mathematics and StatisticsGeorgia State UniversityAtlantaUSA
  4. 4.Department of MathematicsHong Kong Baptist UniversityKowloonHong Kong

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