SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks

Dibaeinia, Payam; Sinha, Saurabh

doi:10.1016/j.cels.2020.08.003

Cited by 83 publications

(153 citation statements)

References 94 publications

(143 reference statements)

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“…BEELINE [126] optimized SINGE's hyperparameters and constructed much smaller ensembles than we do in this study. SER-GIO's ensembling [130] was much closer to ours except for differences in the treatment of the λ hyperparameter. In the SERGIO evaluation, SINGE was the best GRN method when inferring networks from simulated datasets with added technical noise.…”

Section: Benchmarking and Evaluationsupporting

confidence: 51%

“…One evaluation of network inference algorithms on simulated single-cell datasets reported that their performances were only slightly better than a random edge ordering [129]. New single-cell gene expression simulators designed specifically for simulating GRNs [126,130] can help inform which GRN inference methods are best for different types of biological trajectories. Both of these studies evaluated SINGE performance with their GRN simulators but with different ensembling strategies.…”

Section: Benchmarking and Evaluationmentioning

confidence: 99%

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Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data

Deshpande

Chu

Stewart

et al. 2019

Preprint

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Advances in single-cell transcriptomics enable measuring the gene expression of individual cells, allowing cells to be ordered by their state in a dynamic biological process. Many algorithms assign 'pseudotimes' to each cell, representing the progress along the biological process. Ordering the expression data according to such pseudotimes can be valuable for understanding the underlying regulator-gene interactions in a biological process, such as differentiation. However, the distribution of cells sampled along a transitional process, and hence that of the pseudotimes assigned to them, is not uniform. This prevents using many standard mathematical methods for analyzing the ordered gene expression states. We present Single-Cell Inference of Networks using Granger Ensembles (SCINGE), an algorithm for gene regulatory network inference from single-cell gene expression data. Given ordered singlecell data, SCINGE uses kernel-based Granger Causality regression, which smooths the irregular pseudotimes and missing expression values. It then aggregates the predictions from an ensemble of regression analyses with a modified Borda method to compile a ranked list of candidate interactions between transcriptional regulators and their target genes. In two mouse embryonic stem cell differentiation case studies, SCINGE outperforms other contemporary algorithms for gene network reconstruction. However, a more detailed examination reveals caveats about transcriptional network reconstruction with single-cell RNA-seq data. Network inference methods, including SCINGE, may have near random performance for predicting the targets of many individual regulators even if the aggregate performance is good. In

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Section: Benchmarking and Evaluationsupporting

confidence: 51%

Section: Benchmarking and Evaluationmentioning

confidence: 99%

Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data

Deshpande

Chu

Stewart

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…We also simulated scRNA-seq data using a parametric method with a predefined scGRN model (see Experimental Procedures for details). 28 With the simulated data, which contain 100 genes and up to 3,000 cells, we compared constructed scGRNs against the ground truth (i.e., the simulated scGRN) to estimate the accuracy of reconstruction. We tested the accuracy of scTenifoldNet/PC regression against methods based on Spearman’s correlation coefficient (SCC) and mutual information (MI) 1 and on GENIE3.…”

Section: Resultsmentioning

confidence: 99%

“…We generated our own synthetic datasets using SERGIO, a single-cell expression simulator guided by GRNs. 28 SERGIO allows for the simulation of scRNA-seq data while considering the linear and non-linear influences of regulatory interactions between genes. SERGIO takes a user-provided GRN to define the interactions and generates expression profiles of genes in steady state using systems of stochastic differential equations derived from the chemical Langevin equation.…”

Section: Methodsmentioning

confidence: 99%

scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

Osorio

Zhong

et al. 2020

Patterns

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“…We first used the simulated data to validate the relevance of our method. We generated a synthetic scRNA-seq data set using the simulator SERGIO-a single-cell expression simulator guided by GRNs [10]. To simulate the data, we supplied SERGIO with a predefined GRN with five co-expression modules of different sizes (containing 5, 10, 25, 40, and 20 genes, respectively).…”

Section: Substep 13 Denoising Adjacency Matrices To Obtain the Finamentioning

confidence: 99%

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

Osorio

Zhong

et al. 2021

Preprint

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Gene knockout (KO) experiments are a proven approach for studying gene function. A typical KO experiment usually involves the phenotypic characterization of KO organisms. The recent advent of single-cell technology has greatly boosted the resolution of cellular phenotyping, providing unprecedented insights into cell-type-specific gene function. However, the use of single-cell technology in large-scale, systematic KO experiments is prohibitive due to the vast resources required. Here we present scTenifoldKnk, a machine learning workflow that performs virtual KO experiments using single-cell RNA sequencing (scRNA-seq) data. scTenifoldKnk first uses data from wild-type (WT) samples to construct a single-cell gene regulatory network (scGRN). Then, a gene is knocked out from the constructed scGRN by setting weights of the gene's outward edges to zeros. ScTenifoldKnk then compares this "pseudo-KO" scGRN with the original scGRN to identify differentially regulated (DR) genes. These DR genes, also called virtual-KO perturbed genes, are used to assess the impact of the gene KO and reveal the gene's function in analyzed cells. Using existing data sets, we demonstrate that the scTenifoldKnk analysis recapitulates the main findings of three real-animal KO experiments and confirms the functions of genes underlying three Mendelian diseases. We show the power of scTenifoldKnk as a predictive method to successfully predict the outcomes of two KO experiments that involve intestinal enterocytes in Ahr-/- mice and pancreatic islet cells in Malat1-/- mice, respectively. Finally, we demonstrate the use of scTenifoldKnk to perform systematic KO analyses, in which a large number of genes are virtually deleted, allowing gene functions to be revealed in a cell type-specific manner.

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SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks

Cited by 83 publications

References 94 publications

Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data

Network Inference with Granger Causality Ensembles on Single-Cell Transcriptomic Data

scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

scTenifoldKnk: an efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation

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