Statistical and machine learning methods for immunoprofiling based on single-cell data.

Journal: Human vaccines & immunotherapeutics
Published Date:

Abstract

Immunoprofiling has become a crucial tool for understanding the complex interactions between the immune system and diseases or interventions, such as therapies and vaccinations. Immune response biomarkers are critical for understanding those relationships and potentially developing personalized intervention strategies. Single-cell data have emerged as a promising source for identifying immune response biomarkers. In this review, we discuss the current state-of-the-art methods for immunoprofiling, including those for reducing the dimensionality of high-dimensional single-cell data and methods for clustering, classification, and prediction. We also draw attention to recent developments in data integration.

Authors

  • Jingxuan Zhang
    Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
  • Jia Li
    Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan Tsuihang New District, Guangdong, 528400, PR China; School of Pharmacy, Zunyi Medical University, Zunyi, 563000, PR China; National Center for Drug Screening, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, PR China.
  • Lin Lin
    Central Laboratory, The First Affiliated Hospital of Xiamen University, Xiamen, China, zhibinli33@163.com, liusuhuan@xmu.edu.cn.