scholarly journals scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data

2020 ◽  
Vol 16 (7) ◽  
pp. e1007471 ◽  
Author(s):  
Xiao Zheng ◽  
Yuan Huang ◽  
Xiufen Zou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

scholarly journals A single cell RNA sequencing resource for early sea urchin development

Development ◽  
2020 ◽  
Vol 147 (17) ◽  
pp. dev191528 ◽  
Author(s):  
Stephany Foster ◽  
Nathalie Oulhen ◽  
Gary Wessel
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Sea Urchin ◽  
Germ Cells ◽  
Regulatory Network ◽  
Germ Line ◽  
Primordial Germ Cells ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

ABSTRACTIdentifying cell states during development from their mRNA profiles provides insight into their gene regulatory network. Here, we leverage the sea urchin embryo for its well-established gene regulatory network to interrogate the embryo using single cell RNA sequencing. We tested eight developmental stages in Strongylocentrotus purpuratus, from the eight-cell stage to late in gastrulation. We used these datasets to parse out 22 major cell states of the embryo, focusing on key transition stages for cell type specification of each germ layer. Subclustering of these major embryonic domains revealed over 50 cell states with distinct transcript profiles. Furthermore, we identified the transcript profile of two cell states expressing germ cell factors, one we conclude represents the primordial germ cells and the other state is transiently present during gastrulation. We hypothesize that these cells of the Veg2 tier of the early embryo represent a lineage that converts to the germ line when the primordial germ cells are deleted. This broad resource will hopefully enable the community to identify other cell states and genes of interest to expose the underpinning of developmental mechanisms.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

2019 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Reconstruction ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

AbstractUnderstanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.

scholarly journals Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

2021 ◽  
Author(s):  
Matthew Stone ◽  
Sunnie Grace McCalla ◽  
Alireza Fotuhi Siahpirani ◽  
Viswesh Periyasamy ◽  
Junha Shin ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Cost Effective ◽  
Gene Regulatory Network Inference ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory ◽  
Inference Methods

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional pro- grams of different cellular states by measuring the transcriptome of thousands individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory net- works and a number of methods with different learning frameworks have been developed. Here we present a expanded benchmarking study of eleven recent network inference methods on six published single-cell RNA-sequencing datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that while no method is a universal winner and most methods have a modest recovery of experimentally derived interactions based on global metrics such as AUPR, methods are able to capture targets of regulators that are relevant to the system under study. Based on overall performance we grouped the methods into three main categories and found a combination of information-theoretic and regression-based methods to have a generally high perfor- mance. We also evaluate the utility of imputation for gene regulatory network inference and find that a small number of methods benefit from imputation, which further depends upon the dataset. Finally, comparisons to inferred networks for comparable bulk conditions showed that networks inferred from scRNA-seq datasets are often better or at par to those from bulk suggesting that scRNA-seq datasets can be a cost-effective way for gene regulatory network inference. Our analysis should be beneficial in selecting algorithms for performing network inference but also argues for improved methods and better gold standards for accurate assessment of regulatory network inference methods for mammalian systems.

scholarly journals Regulatory network-based imputation of dropouts in single-cell RNA sequencing data

2019 ◽  
Author(s):  
Ana Carolina Leote ◽  
Xiaohui Wu ◽  
Andreas Beyer
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Regulatory Network ◽  
Missing Values ◽  
Transcriptional Regulatory Network ◽  
Imputation Method ◽  
Sequencing Data ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Baseline Approach

AbstractSingle-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information.Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Additionally, we tested a baseline approach, where we imputed missing values using the sample-wide average expression of a gene. Unexpectedly, up to 48% of the genes were better predicted using this baseline approach, suggesting negligible cell-to-cell variation of expression levels for many genes. Our work shows that there is no single best imputation method; rather, the best method depends on gene-specific features, such as expression level and expression variation across cells. We thus implemented an R-package called ADImpute (available from https://github.com/anacarolinaleote/ADImpute) that automatically determines the best imputation method for each gene in a dataset.

scholarly journals Single-Cell RNA Sequencing Analysis of the Heterogeneity in Gene Regulatory Networks in Colorectal Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Rui-Qi Wang ◽  
Wei Zhao ◽  
Hai-Kui Yang ◽  
Jia-Mei Dong ◽  
Wei-Jie Lin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Analysis ◽  
Sequencing Data ◽  
Communication Analysis ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Colorectal cancer (CRC) manifests as gastrointestinal tumors with high intratumoral heterogeneity. Recent studies have demonstrated that CRC may consist of tumor cells with different consensus molecular subtypes (CMS). The advancements in single-cell RNA sequencing have facilitated the development of gene regulatory networks to decode key regulators for specific cell types. Herein, we comprehensively analyzed the CMS of CRC patients by using single-cell RNA-sequencing data. CMS for all malignant cells were assigned using CMScaller. Gene set variation analysis showed pathway activity differences consistent with those reported in previous studies. Cell–cell communication analysis confirmed that CMS1 was more closely related to immune cells, and that monocytes and macrophages play dominant roles in the CRC tumor microenvironment. On the basis of the constructed gene regulation networks (GRNs) for each subtype, we identified that the critical transcription factor ERG is universally activated and upregulated in all CMS in comparison with normal cells, and that it performed diverse roles by regulating the expression of different downstream genes. In summary, molecular subtyping of single-cell RNA-sequencing data for colorectal cancer could elucidate the heterogeneity in gene regulatory networks and identify critical regulators of CRC.

Random Forest Factorization Reveals Latent Structure in Single Cell RNA Sequencing Data

2021 ◽  
Author(s):  
Boris M. Brenerman ◽  
Benjamin D. Shapiro ◽  
Michael C. Schatz ◽  
Alexis Battle
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Sequencing Data ◽  
Random Forest Regression ◽  
Single Cell Rna Sequencing ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractSingle-cell RNA sequencing data contain patterns of correlation that are poorly captured by techniques that rely on linear estimation or assumptions of Gaussian behavior. We apply random forest regression to scRNAseq data from mouse brains, which identifies the co-regulation of genes within specific cellular contexts. By analyzing the estimators of the random forest, we identify several novel candidate gene regulatory networks and compare these networks in aged and young mice. We demonstrate that cell populations have cell-type specific phenotypes of aging that are not detected by other methods, including the collapse of differentiating oligodendrocytes but not precursors or mature oligodendrocytes.

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

2020 ◽  
Author(s):  
Harsh Shrivastava ◽  
Xiuwei Zhang ◽  
Srinivas Aluru ◽  
Le Song
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Rna Sequencing ◽  
Regulatory Networks ◽  
Target Genes ◽  
Synthetic Data ◽  
Expression Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

AbstractMotivationGene 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.ResultsWe 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.AvailabilityGithub link to GRNUlar - https://github.com/Harshs27/[email protected]

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