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Review
. 2018 Jul 1;17(4):246-254.
doi: 10.1093/bfgp/elx046.

Mapping gene regulatory networks from single-cell omics data

Mark W E J Fiers  1 , Liesbeth Minnoye  1   2 , Sara Aibar  1   2 , Carmen Bravo González-Blas  1   2 , Zeynep Kalender Atak  1   2 , Stein Aerts  1   2
Affiliations
Review

Mapping gene regulatory networks from single-cell omics data

Mark W E J Fiers et al. Brief Funct Genomics. .

Abstract

Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.

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Figures

Figure 1.
Figure 1.
Single-cell GRNs. The goal of many single-cell studies is to understand which cell states are present in a heterogeneous sample; how these states differ from each other; how (and if) cells can switch from one state to another; and which states are relevant to the biological process under study. Cell states can be defined by GRNs, which can be inferred from scRNA-seq and scEpigenomics methods such as scATAC-seq and scMethyl-seq data. The two main classes of GRN inference methods are dynamic GRN methods that predict trajectories; and static GRN methods that can be used to predict cell states. Perturbation experiments can be used to confirm regulatory relationships.
Figure 2.
Figure 2.
Tools for GRN inference from scRNA-seq data.
See this image and copyright information in PMC

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