Review of gene regulatory network inference algorithms based on single-cell RNA sequencing data
Gene regulatory networks(GRN)can be inferred from the changes of gene expression.Single-cell RNA sequencing(scRNA-seq)technologies provide new possibilities for inferring GRNs of time-dependent biological processes such as cell cycle or differentiation,and GRN inference algorithm has become a relatively active research direction.Firstly,26 inference algorithms including three algorithms based on bulk RNA sequencing data and 23 algorithms based on scRNA-seq data(two algorithms based on Boolean network,three algorithms based on differential equations,five algorithms based on pseudo-time-series gene correlation integration strategy,four algorithms based on co-expression genes,three algorithms based on cell specif-ic,six algorithms based on deep learning)are reviewed.The method principles of the algorithms are described in detail as well as advantages and disadvantages of each algorithm,and the algorithms are compared comprehensively.And then the compara-tive studies on inference algorithms are analyzed,and the performance of 26 algorithms is simply evaluated using scRNA-seq data.Finally,the opportunities and challenges faced by current GRN inference algorithms are discussed.
万方数据
维普
