Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
By Aditya Pratapa, A. Jalihal, Jeffrey N. Law, Aditya Bharadwaj, T. Murali. Nature Methods, 2019
707 citations recorded by the source corpus.
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 than Boolean models. The algorithms with the best early precision values for Boolean models also perform well on experimental datasets. Techniques that do not require pseudotime-ordered cells are generally more accurate. Based on these results, we present recommendations to end users. BEELINE will aid the development of gene regulatory network inference algorithms. Comprehensive evaluation of algorithms for inferring gene regulatory networks using synthetic and experimental single-cell RNA-seq datasets finds heterogeneous performance and suggests recommendations to users.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/450f5f482ea3fb72782a376883f3744087aa3279
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Citations in the corpus
- A scalable SCENIC workflow for single-cell gene regulatory network analysis (2020, methodology): “In a recent benchmark study for GRN inference from single-cell transcriptomics data sets, which includes assessment of the effect of dropouts, GRNBoost2 (Step 5) was among the best-performing methods 22 .”
- Predicting transcriptional outcomes of novel multigene perturbations with GEARS (2023, positive): “GEARS functionalizes this graph-based inductive bias using a graph neural network (GNN) architecture 33 .”
- Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “One approach to benchmarking is to build in silico GRNs that allow us to assess GRN reconstruction against a known ground truth 34 , yet one that might not well reflect true biological GRNs.”