A single-cell expression simulator guided by gene regulatory networks

Dibaeinia, Payam; Sinha, Saurabh

doi:10.1101/716811

Cited by 14 publications

(23 citation statements)

References 73 publications

(83 reference statements)

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“…Recovery Metrics: Following the metrics used in (Dibaeinia and Sinha, 2019;Chen and Mar, 2018), we also use AUROC (Area Under the Receiver Operating Characteristics) and AUPRC (Area Under the Precision Recall Curve) values for our evaluation.…”

Section: Description Of Evaluation Metrics and Methodsmentioning

confidence: 99%

“…(3) The datasets DS2, DS3 are completely different from train data (& DS1) in terms of the underlying GRN as well as the corresponding SERGIO parameters are sampled from different range of values. For details, refer to Table1 in Dibaeinia and Sinha (2019) & supplementary Tables S1, S3. GRNUlar settings: We used the parameter settings as mentioned in Section 3.1.…”

Section: Realistic Data From Sergio: Ecoli and Yeastmentioning

confidence: 99%

“…While methods for GRN inference from scRNA-Seq data are improving, simulators which generate synthetic scRNA-Seq data guided by GRNs are also progressing (Dibaeinia and Sinha, 2019;Pratapa et al, 2020). These scRNA-Seq simulators can generate realistic looking data, and have modeled sources of variation in single cell RNA-Seq data, such as noise intrinsic to the process of transcription, extrinsic variation indicative of different cell states, technical variation and measurement noise and Shrivastava et al Fig.…”

Section: Introductionmentioning

confidence: 99%

“…bias. The primary application of these realistic simulators is to benchmark the performance of GRN inference methods (Dibaeinia and Sinha, 2019;Chen and Mar, 2018;Pratapa et al, 2020). These evaluations show that the performance of current method for GRN inference is not satisfying even with synthetic data.…”

Section: Introductionmentioning

confidence: 99%

“…It has been argued in the recent works by (Belilovsky et al, 2017;Shrivastava et al, 2020) that a data-driven neural algorithm may be able to leverage this distribution of problem instances, and learn an algorithm which performs better than traditional manually designed algorithms. SERGIO (Dibaeinia and Sinha, 2019) is the most realistic GRN-guided simulator for scRNA-Seq data to date, and is the main simulator we use for training and evaluation of our models. SERGIO generates realistic gene expression data by incorporating known principals of TF-gene regulatory interactions that underlie expression dynamics, and it models the stochastic nature of transcription as well as simulates the non-linear influences of multiple TFs.…”

Section: Introductionmentioning

confidence: 99%

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GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data

Shrivastava

Zhang

Aluru

et al. 2020

Preprint

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Motivation: Gene regulatory networks (GRNs) are graphs that specify the interactions between transcription factors (TFs) and their target genes. Understanding these interactions is crucial for studying the mechanisms in cell differentiation, growth and development. Computational methods are needed to infer these networks from measured data. Although the availability of single cell RNA-Sequencing (scRNA-Seq) data provides unprecedented scale and resolution of gene-expression data, the inference of GRNs remains a challenge, mainly due to the complexity of the regulatory relationships and the noise in the data. Results: We propose GRNUlar, a novel deep learning architecture based on the unrolled algorithms idea for GRN inference from scRNA-Seq data. Like some existing methods which use prior information of which genes are TFs, GRNUlar also incorporates this TF information using a sparse multi-task deep learning architecture. We also demonstrate the application of a recently developed unrolled architecture GLAD to recover undirected GRNs in the absence of TF information. These unrolled architectures require supervision to train, for which we leverage the existing synthetic data simulators which generate scRNA-Seq data guided by a GRN. We show that unrolled algorithms outperform the state-of-the-art methods on synthetic data as well as real datasets in both the settings of TF information being absent or available.

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Section: Description Of Evaluation Metrics and Methodsmentioning

confidence: 99%

Section: Realistic Data From Sergio: Ecoli and Yeastmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data

Shrivastava

Zhang

Aluru

et al. 2020

Preprint

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scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data

Osorio

Zhong

et al. 2020

Preprint

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Constructing and comparing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNAseq) data present formidable computational challenges. Many existing solutions lack effectiveness or efficiency, due to technical and analytical issues of scRNAseq such as random dropout, uncharacterized cell states, and heterogeneous samples. Here, we present a robust, unsupervised machine learning workflow, called scTenifoldNet, to improve on existing solutions. The scTenifoldNet workflow combines principal component regression, low-rank tensor approximation, and manifold alignment. It constructs and compares transcriptome-wide single-cell GRNs (scGRNs) from different samples to identify gene expression signatures shifting with cellular activity changes such as pathophysiological processes and responses to environmental perturbations. We used simulated data to benchmark the performance of scTenifoldNet. Application of scTenifoldNet on three real data sets shows it to be a powerful tool to reveal biological insights, which are otherwise difficult to obtain. In particular, scTenifoldNet identified highly specific shifts in transcriptional regulation associated with aging and acute morphine responses in mouse cortical neurons, as well as those associated with doublestranded RNA-induced immune responses in human dermal fibroblasts.

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graphsim: An R package for simulating gene expression data from graph structures of biological pathways

Kelly

Black²

2020

Preprint

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SummaryTranscriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.

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A single-cell expression simulator guided by gene regulatory networks

Cited by 14 publications

References 73 publications

GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data

GRNUlar: Gene Regulatory Network reconstruction using Unrolled algorithm from Single Cell RNA-Sequencing data

scTenifoldNet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data

graphsim: An R package for simulating gene expression data from graph structures of biological pathways

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