ArticlePatterns (New York, N.Y.)2021
Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- scPRINT: pre-training on 50 million cells allows robust gene network predictions.Nature communications · 2025Article
- Exploring transcription modalities from bimodal, single-cell RNA sequencing data.NAR genomics and bioinformatics · 2024Article
- Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables.PloS one · 2024Article
- Decoding Human Biology and Disease Using Single-cell Omics Technologies.Genomics, proteomics & bioinformatics · 2023Review
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
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Authors and funding
5 authors.
Funding
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Abstract
Single-cell RNA sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. To date, this aspiration remains unrealized due to technical and computational challenges. In this work we focus on the latter, which is under-represented in the literature. We took a systemic approach by subdividing the GRN inference into three fundamental components: data pre-processing, feature extraction, and inference. We observed that the regulatory signature is captured in the statistical moments of scRNA-seq data and requires computationally intensive minimization solvers to extract it. Furthermore, current data pre-processing might not conserve these statistical moments. Although our moment-based approach is a didactic tool for understanding the different compartments of GRN inference, this line of thinking-finding computationally feasible multi-dimensional statistics of data-is imperative for designing GRN inference methods.
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