Editorial: Machine Learning and Mathematical Models for Single-Cell Data Analysis.

Abstract

Understanding how individual cells communicate with each other and respond to evolution and perturbations is a central challenge of biology (Altschuler and Wu, 2010). Due to the heterogeneity of cells, studying a bulk population of cells may confound the variability of cell-type compositions, single cell analysis has the potential to enable a more systematic study of the inner workings of biological systems, and allows us to uncover the underlying mechanisms for cellular functions and biological processes such as cell differentiation and disease development. In the past decade, advances in single-cell isolation and sequencing technologies have enabled the assay of DNA, mRNA, and protein abundances at single-cell resolution, which promote the study of genomics, transcriptomics, proteomics and metabolomics at the sinlge cell level. For example, single-cell genomic analysis can shed light to the genomic variability of individual cells, while single-cell transcriptomic and proteomic analysis can help to reveal the types and functional states of individual cells (Shapiro et al., 2013). However, processing single-cell data of high dimensionality and scale is inherently difficult, especially considering the degree of noise, sparsity, batch effects and heterogeneity in the data (Amodio et al., 2019). Thus, there is an urgent need for developing computational models which can handle the size, dimensionality, and various