Gene regulatory network inference in single-cell biology

Akers, Kyle; Murali, T. M.

doi:10.1016/j.coisb.2021.04.007

Cited by 28 publications

(22 citation statements)

References 64 publications

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“…The cell type‐specific gene networks in the developing fetal brain and in autistic patient samples has been developed, relying on a local independent test, which is carried out on a cell type‐basis (Wang et al , 2021); GRNs of distinct cell types have been constructed by integrating prior knowledge and gene activity (Gibbs et al , 2022); a fine‐grained method has been developed to infer cell‐specific networks (CSN) and predict important genes that are neglected by traditional differential gene expression analysis (Dai et al , 2019). One common trait of these methods is to adjust the global GRN inference strategies, such as correlation, mutual information, and regression, and apply them to specific cells or subpopulations of cells (Akers & Murali, 2021). In the present work, the terms “cell state” and “cell type” are largely considered as synonymous.…”

Section: Discussionmentioning

confidence: 99%

“…Ó 2022 The Authors Molecular Systems Biology 18: e11176 | 2022 et al, 2019). One common trait of these methods is to adjust the global GRN inference strategies, such as correlation, mutual information, and regression, and apply them to specific cells or subpopulations of cells (Akers & Murali, 2021). In the present work, the terms "cell state" and "cell type" are largely considered as synonymous.…”

Section: Discussionmentioning

confidence: 99%

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spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

Bocci

Zhou

Nie

2022

Molecular Systems Biology

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Extracting dynamical information from single‐cell transcriptomics is a novel task with the promise to advance our understanding of cell state transition and interactions between genes. Yet, theory‐oriented, bottom‐up approaches that consider differences among cell states are largely lacking. Here, we present spliceJAC, a method to quantify the multivariate mRNA splicing from single‐cell RNA sequencing (scRNA‐seq). spliceJAC utilizes the unspliced and spliced mRNA count matrices to constructs cell state‐specific gene–gene regulatory interactions and applies stability analysis to predict putative driver genes critical to the transitions between cell states. By applying spliceJAC to biological systems including pancreas endothelium development and epithelial–mesenchymal transition (EMT) in A549 lung cancer cells, we predict genes that serve specific signaling roles in different cell states, recover important differentially expressed genes in agreement with pre‐existing analysis, and predict new transition genes that are either exclusive or shared between different cell state transitions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

Bocci

Zhou

Nie

2022

Molecular Systems Biology

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“…The six algorithms that we are going to use for the benchmark represent together the main categories of GRN inference methods presented in [7]:…”

Section: Tested Algorithmsmentioning

confidence: 99%

One model fits all: combining inference and simulation of gene regulatory networks

Ventre

Herbach

Espinasse

et al. 2022

Preprint

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The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions. We verify that CARDAMOM quantitatively reconstructs causal links when the data is simulated from HARISSA, and demonstrate its performance on experimental data collected on in vitro differentiating mouse embryonic stem cells. Overall, this integrated strategy largely overcomes the limitations of disconnected inference and simulation.

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“…In biology, a domain characterised by enormous complexity of the systems studied, establishing causality frequently involves experimentation in controlled in-vitro lab conditions using appropriate technologies to observe response to intervention, such as for example high-content microscopy (Bray et al (2016)) and multivariate omics measurements (Bock et al (2016)). Highthroughput single-cell methods for observing whole transcriptomics measurements in individual cells under genetic perturbations (Dixit et al (2016); Datlinger et al (2017;) has recently emerged as a promising technology that could theoretically support performing causal inference in cellular systems at the scale of thousands of perturbations per experiment, and therefore holds enormous promise in potentially enabling researchers to uncover the intricate wiring diagrams of cellular biology (Yu et al (2004); Chai et al (2014); Akers & Murali (2021); Hu et al (2020)).…”

Section: Introductionmentioning

confidence: 99%

CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data

Chevalley¹,

Roohani²,

Mehrjou³

et al. 2022

Preprint

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Mapping biological mechanisms in cellular systems is a fundamental step in earlystage drug discovery that serves to generate hypotheses on what disease-relevant molecular targets may effectively be modulated by pharmacological interventions. With the advent of high-throughput methods for measuring single-cell gene expression under genetic perturbations, we now have effective means for generating evidence for causal gene-gene interactions at scale. However, inferring graphical networks of the size typically encountered in real-world gene-gene interaction networks is difficult in terms of both achieving and evaluating faithfulness to the true underlying causal graph. Moreover, standardised benchmarks for comparing methods for causal discovery in perturbational single-cell data do not yet exist. Here, we introduce CausalBench -a comprehensive benchmark suite for evaluating network inference methods on large-scale perturbational single-cell gene expression data. CausalBench introduces several biologically meaningful performance metrics and operates on two large, curated and openly available benchmark data sets for evaluating methods on the inference of gene regulatory networks from single-cell data generated under perturbations. With real-world datasets consisting of over 200 000 training samples under interventions, CausalBench could potentially help facilitate advances in causal network inference by providing what is -to the best of our knowledge -the largest openly available test bed for causal discovery from real-world perturbation data to date.

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Gene regulatory network inference in single-cell biology

Cited by 28 publications

References 64 publications

spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

spliceJAC : transition genes and state‐specific gene regulation from single‐cell transcriptome data

One model fits all: combining inference and simulation of gene regulatory networks

CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data

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