Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.

ABSTRACT: Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have

SUBMITTER: McCalla SG 

PROVIDER: S-EPMC9997554 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature