Inferring Novel Cells in Single-Cell RNA-Sequencing Data

Single-cell RNA-sequencing (scRNA-seq) is a powerful technology that allows researchers to study gene expression heterogeneity within a tissue or cell population. One of the major advantages of scRNA-seq is that it allows researchers to identify and characterize novel cell types or subpopulations within a tissue that may be missed by traditional bulk RNA-sequencing methods. Although many existing methods have been developed to recognize known cell types, inferring novel cells may still be challenging in routine scRNA-seq analysis. Here we describe three lines of methods for inferring novel cells: unsupervised and outlier-detection-based methods, supervised and semi-supervised methods, and copy number variation (CNV)-based methods, as well as the corresponding situations that each method applies. We also provide implementation code and example usages to illustrate the available methods.