IReNA: integrated regulatory network analysis of single-cell transcriptomes

While single-cell RNA sequencing (scRNA-seq) is widely used to profile gene expression, few methods are available to infer gene regulatory networks using scRNA-seq data. Here, we developed and extended IReNA (Integrated Regulatory Network Analysis) to perform regulatory network analysis using scRNA-seq profiles. Four features are developed for IReNA. First, regulatory networks are divided into different modules which represent distinct biological functions. Second, transcription factors significantly regulating each gene module can be identified. Third, regulatory relationships among modules can be inferred. Fourth, IReNA can integrate ATAC-seq data into regulatory network analysis. If ATAC-seq data is available, both transcription factor footprints and binding motifs are used to refine regulatory relationships among co-expressed genes. Using public datasets, we showed that integrated network analysis of scRNA-seq data with ATAC-seq data identified a higher fraction of known regulators than scRNA-seq data alone. Moreover, IReNA provided a better performance of network analysis than currently available methods. Beyond the reconstruction of regulatory networks, IReNA can modularize regulatory networks, and identify key regulators and significant regulatory relationships for modules, facilitating the systems-level understanding of biological regulatory mechanisms. The R package IReNA is available at https://github.com/jiang-junyao/IReNA.

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Predicting gene regulatory networks from cell atlases

10.1101/2020.08.21.261735 ◽

2020 ◽

Author(s):

Andreas Fønss Møller ◽

Kedar Nath Natarajan

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Types ◽

Mouse Cell ◽

Cell Type ◽

Integrated Network ◽

Mouse Tissues ◽

Cell Type Specific ◽

Gene Regulatory

AbstractRecent single-cell RNA-sequencing atlases have surveyed and identified major cell-types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from 3 major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences including sampled tissues, sequencing depth and author assigned cell-type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell-types from specialised cell-type specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell-types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wildtype and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.SummaryIntegrated single-cell gene regulatory network from three mouse cell atlases captures global and cell-type specific regulatory modules and crosstalk, important for cellular identity.

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

eLife ◽

10.7554/elife.51254 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 16

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.

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

10.1101/581678 ◽

2019 ◽

Cited By ~ 6

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.

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Predicting gene regulatory networks from cell atlases

Life Science Alliance ◽

10.26508/lsa.202000658 ◽

2020 ◽

Vol 3 (11) ◽

pp. e202000658 ◽

Cited By ~ 1

Author(s):

Andreas Fønss Møller ◽

Kedar Nath Natarajan

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Types ◽

Mouse Cell ◽

Cell Type ◽

Integrated Network ◽

Mouse Tissues ◽

Multiple Cell ◽

Gene Regulatory

Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

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KBoost: a new method to infer gene regulatory networks from gene expression data

Scientific Reports ◽

10.1038/s41598-021-94919-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Luis F. Iglesias-Martinez ◽

Barbara De Kegel ◽

Walter Kolch

Keyword(s):

Breast Cancer ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Bayesian Model Averaging ◽

Model Averaging ◽

R Package ◽

Breast Cancer Patients ◽

Sources Of Information ◽

Cancer Subtypes ◽

Gene Regulatory

AbstractReconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

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Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

Patterns ◽

10.1016/j.patter.2021.100332 ◽

2021 ◽

Vol 2 (9) ◽

pp. 100332

Author(s):

N. Alexia Raharinirina ◽

Felix Peppert ◽

Max von Kleist ◽

Christof Schütte ◽

Vikram Sunkara

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Higher Order ◽

Rna Seq ◽

Higher Order Moments ◽

Gene Regulatory

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Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm

RSC Advances ◽

10.1039/c7ra01557g ◽

2017 ◽

Vol 7 (37) ◽

pp. 23222-23233 ◽

Cited By ~ 6

Author(s):

Wei Liu ◽

Wen Zhu ◽

Bo Liao ◽

Haowen Chen ◽

Siqi Ren ◽

...

Keyword(s):

Systems Biology ◽

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Network Structure ◽

Regulatory Network ◽

Regulatory Networks ◽

Central Problem ◽

Expression Data ◽

Redundancy Reduction ◽

Gene Regulatory

Inferring gene regulatory networks from expression data is a central problem in systems biology.

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Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application

10.1101/2020.08.30.273839 ◽

2020 ◽

Author(s):

Turki Turki ◽

Y-h. Taguchi

Keyword(s):

Deep Learning ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Metabolic Diseases ◽

Large Data ◽

Data Repositories ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Gene

AbstractAnalyzing single-cell pancreatic data would play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, analyzing various functions related to the inference of gene regulatory networks, derived from single-cell data, remains difficult, thereby posing a barrier to the deepening of understanding of cellular metabolism. Since recent studies have led to the reliable inference of single-cell gene regulatory networks (SCGRNs), the challenge of discriminating between SCGRNs has now arisen. By accurately discriminating between SCGRNs (e.g., distinguishing SCGRNs of healthy pancreas from those of T2D pancreas), biologists would be able to annotate, organize, visualize, and identify common patterns of SCGRNs for metabolic diseases. Such annotated SCGRNs could play an important role in speeding up the process of building large data repositories. In this study, we aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked prediction based on a test set. We evaluated the DL architectures on an HP workstation platform with a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

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PAGeneRN

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch055 ◽

2020 ◽

pp. 1052-1075 ◽

Cited By ~ 1

Author(s):

Dina Elsayad ◽

A. Ali ◽

Howida A. Shedeed ◽

Mohamed F. Tolba

Keyword(s):

Gene Expression ◽

Data Analysis ◽

Network Analysis ◽

Gene Regulatory Network ◽

Gene Expression Data ◽

Regulatory Network ◽

Regulatory Networks ◽

Expression Data ◽

Gene Expression Data Analysis ◽

Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

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