Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data.

ABSTRACT: We present a systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data. As the ground truth for assessing accuracy, we use synthetic networks with predictable trajectories, literature-curated Boolean models and diverse transcriptional regulatory networks. We develop a strategy to simulate single-cell transcriptional data from synthetic and Boolean networks that avoids pitfalls of previously used methods. Furthermore, we collect networks from multiple experimental single-cell RNA-seq datasets. We develop an evaluation framework called BEELINE. We find that the area under the precision-recall curve and early precision of the algorithms are moderate. The methods are better in recovering interactions in synthetic networks

SUBMITTER: Pratapa A 

PROVIDER: S-EPMC7098173 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature