ArticleFrontiers in genetics2021
Evaluating the Reproducibility of Single-Cell Gene Regulatory Network Inference Algorithms.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- CroCoNet: a framework for the quantitative comparison of gene regulatory networks across species.Genome biology · 2026Article
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- Improved gene regulatory network inference from single cell data with dropout augmentation.PLoS computational biology · 2025Article
- CONSTRUCTING GENE REGULATORY NETWORK USING CHATTERJEE'S RANK CORRELATION WITH SINGLE-CELL TRANSCRIPTOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- Critical assessment of the ability of Boolean threshold models to describe gene regulatory network dynamics.PNAS nexus · 2025Article
- Inferring gene regulatory networks from time-series scRNA-seq data via GRANGER causal recurrent autoencoders.Briefings in bioinformatics · 2025Article
- Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data.Molecular systems biology · 2025Review
- Exploring the interplay between circadian rhythms and obesity: A Boolean network approach to understanding metabolic dysregulation.PloS one · 2025Article
- From Noise to Knowledge: Diffusion Probabilistic Model-Based Neural Inference of Gene Regulatory Networks.Journal of computational biology : a journal of computational molecular cell biology · 2024Article
- StableMate: a statistical method to select stable predictors in omics data.NAR genomics and bioinformatics · 2024Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.Bioinformatics (Oxford, England) · 2024Article
- Stem-cell states converge in multistage cutaneous squamous cell carcinoma development.Science (New York, N.Y.) · 2024Article
- Topological benchmarking of algorithms to infer gene regulatory networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS.Bioinformatics (Oxford, England) · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- MICA: a multi-omics method to predict gene regulatory networks in early human embryos.Life science alliance · 2024Article
- GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging.Genome biology · 2023Article
- Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.NPJ systems biology and applications · 2023Review
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3 authors.
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Abstract
Networks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth. Here, we benchmark six single-cell network inference methods based on their reproducibility, i.e., their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis. Once taking into account networks with up to 100,000 links, GENIE3 results to be the most reproducible algorithm and, together with GRNBoost2, show higher intersection with ground-truth biological interactions. These results are independent from the single-cell sequencing platform, the cell type annotation system and the number of cells constituting the dataset. Finally, GRNBoost2 and CLR show more reproducible performance once a more stringent thresholding is applied to the networks (1,000-100 links). In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.
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