scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

Journal: BMC genomics
Published Date: Jul 12, 2022

Abstract

BACKGROUND: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for example, Poisson linear discriminant analysis (PLDA), negative binomial linear discriminant analysis (NBLDA), and zero-inflated Poisson logistic discriminant analysis (ZIPLDA). Nevertheless, few existing methods perform well for large sample scRNA-seq data, in particular when the distribution assumption is also violated.

Authors

  • Yan Zhou
    Department of Computer Science, University of Texas at Dallas, Richardson, Texas 75080, United States.
  • Minjiao Peng
    College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China.
  • Bin Yang
    School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, PR China. Electronic address: yangbin@dlut.edu.cn.
  • Tiejun Tong
  • Baoxue Zhang
    School of Statistics, Capital University of Economics and Business, Beijing, China.
  • Niansheng Tang
    Department of Mathematics and Statistics, Yunnan University, China.

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