Learning cell-specific networks from dynamics and geometry of single cells

Zhang, Stephen Y; Stumpf, Michael

doi:10.1101/2023.01.08.523176

Cited by 6 publications

(1 citation statement)

References 143 publications

(355 reference statements)

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“…These include methods rooted in statistical learning [23], dynamical systems theory [24], treebased approaches [25], information theory [26,27,28], and time series analysis [29]. More recently, methods also consider dynamic changes to network topology itself [30]. Methods have also been introduced that make use of chromatin accessibility in addition to gene expression [31,32,33,34,35].…”

Section: Introductionmentioning

confidence: 99%

Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging

Rommelfanger

Behrends

Chen

et al. 2023

Preprint

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Inference of gene regulatory networks (GRNs) to reveal how cell state transitions are controlled is possible with single-cell genomics data. However, obstacles to temporal inference from snapshot data are difficult to overcome. Single-nuclei multiomics data offer means to bridge this gap and derive temporal information from snapshot data using joint measurements of gene expression and chromatin accessibility in the same single cells. We developed popInfer to infer networks that characterize lineage-specific dynamic cell state transitions from joint gene expression and chromatin accessibility data. Benchmarking against alternative methods for GRN inference, we showed that popInfer achieves higher accuracy in the GRNs inferred. popInfer was applied to study single-cell multiomics data characterizing hematopoietic stem cells (HSCs) and the transition from HSC to a multipotent progenitor cell state during murine hematopoiesis across age and dietary conditions. From networks predicted by popInfer, we discovered gene interactions controlling entry to/exit from HSC quiescence that are perturbed in response to diet or aging.

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Section: Introductionmentioning

confidence: 99%

Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging

Rommelfanger

Behrends

Chen

et al. 2023

Preprint

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GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

Zinati,

Takiddeen,

Emad

2024

Nat Commun

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We introduce GRouNdGAN, a gene regulatory network (GRN)-guided reference-based causal implicit generative model for simulating single-cell RNA-seq data, in silico perturbation experiments, and benchmarking GRN inference methods. Through the imposition of a user-defined GRN in its architecture, GRouNdGAN simulates steady-state and transient-state single-cell datasets where genes are causally expressed under the control of their regulating transcription factors (TFs). Training on six experimental reference datasets, we show that our model captures non-linear TF-gene dependencies and preserves gene identities, cell trajectories, pseudo-time ordering, and technical and biological noise, with no user manipulation and only implicit parameterization. GRouNdGAN can synthesize cells under new conditions to perform in silico TF knockout experiments. Benchmarking various GRN inference algorithms reveals that GRouNdGAN effectively bridges the existing gap between simulated and biological data benchmarks of GRN inference algorithms, providing gold standard ground truth GRNs and realistic cells corresponding to the biological system of interest.

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Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

Kim,

Tran,

Kim

et al. 2023

npj Syst Biol Appl

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Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.

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Learning cell-specific networks from dynamics and geometry of single cells

Cited by 6 publications

References 143 publications

Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging

Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

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