Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

Abstract

The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell-cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.

Keywords: CNV estimation; batch effects removal; cell cycle identification; cell type identification; cell–cell interaction; data imputation; regulatory network inference; trajectory inference.

FIGURE 1

Brief summary of computational methods for single cell data analysis.

FIGURE 2

Conceptual view of reads imputation. (A) Expected distribution of transcripts. (B) Reads notches in bulk RNA-seq. (C) Reads notches in scRNA-seq. For concise, the junction reads are not indicated.