Identifying progressive gene network perturbation from single-cell RNA-seq data

AbstractIdentifying the gene regulatory networks that control development or disease is one of the most important problems in biology. Here, we introduce a computational approach, called PIPER (ProgressIve network PERturbation), to identify the perturbed genes that drive differences in the gene regulatory network across different points in a biological progression. PIPER employs algorithms tailor-made for single cell RNA sequencing (scRNA-seq) data to jointly identify gene networks for multiple progressive conditions. It then performs differential network analysis along the identified gene networks to identify master regulators. We demonstrate that PIPER outperforms state-of-the-art alternative methods on simulated data and is able to predict known key regulators of differentiation on real scRNA-Seq datasets.

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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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DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

10.1101/2021.02.03.429484 ◽

2021 ◽

Author(s):

Jiaxing Chen ◽

Chinwang Cheong ◽

Liang Lan ◽

Xin Zhou ◽

Jiming Liu ◽

...

Keyword(s):

Neural Network ◽

Single Cell ◽

Regulatory Networks ◽

Deep Neural Network ◽

Neighborhood Context ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Rna Seq ◽

Cell Type Specific ◽

Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

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INFERENCE OF LARGE-SCALE GENE REGULATORY NETWORKS USING REGRESSION-BASED NETWORK APPROACH

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720009004278 ◽

2009 ◽

Vol 07 (04) ◽

pp. 717-735 ◽

Cited By ~ 4

Author(s):

HASEONG KIM ◽

JAE K. LEE ◽

TAESUNG PARK

Keyword(s):

Gene Regulatory Networks ◽

Gene Networks ◽

Regulatory Networks ◽

Large Scale ◽

Simple Procedure ◽

Simulated Data ◽

Computation Time ◽

Network Approach ◽

Global Regulators ◽

Gene Regulatory

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crecentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.

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PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data

10.1101/2020.12.18.423482 ◽

2020 ◽

Author(s):

Ming Wu ◽

Tim Kacprowski ◽

Dietmar Zehn

Keyword(s):

Single Cell ◽

Regulatory Networks ◽

Cellular Level ◽

Rna Seq ◽

Analysis Workflow ◽

Gene Regulatory ◽

Cell Trajectory ◽

User Friendly ◽

Molecular Expression ◽

Marker Detection

AbstractSummaryThe Advanced capacities of high throughput single cell technologies have facilitated a great understanding of complex biological systems, ranging from cell heterogeneity to molecular expression kinetics. Several pipelines have been introduced to standardize the scRNA-seq analysis workflow. These include cell population identification, cell marker detection and cell trajectory reconstruction. Yet, establishing a systematized pipeline to capture regulatory relationships among transcription factors (TFs) and genes at the cellular level still remains challenging. Here we present PySCNet, a python toolkit that enables reconstructing and analyzing gene regulatory networks (GRNs) from single cell transcriptomic data. PySCNet integrates competitive gene regulatory construction methodologies for cell specific or trajectory specific GRNs and allows for gene co-expression module detection and gene importance evaluation. Moreover, PySCNet offers a user-friendly dashboard website, where GRNs can be customized in an intuitive way.AvailabilitySource code and documentation are available: https://github.com/MingBit/[email protected]

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Uncovering the Gene Regulatory Networks Underlying Macrophage Polarization Through Comparative Analysis of Bulk and Single-Cell Data

10.1101/2021.01.20.427499 ◽

2021 ◽

Author(s):

Klebea Carvalho ◽

Elisabeth Rebboah ◽

Camden Jansen ◽

Katherine Williams ◽

Andrew Dowey ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Course ◽

Macrophage Polarization ◽

Cell Polarization ◽

Rna Seq ◽

Gene Regulatory ◽

Cell Subpopulations ◽

Cell Data

SummaryGene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.

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Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model

BMC Genomics ◽

10.1186/s12864-021-08113-z ◽

2021 ◽

Vol 22 (S1) ◽

Author(s):

Yongqing Zhang ◽

Qingyuan Chen ◽

Meiqin Gong ◽

Yuanqi Zeng ◽

Dongrui Gao

Keyword(s):

Bladder Cancer ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Molecular Subtypes ◽

Receptor Interaction ◽

Muscle Invasive Bladder Cancer ◽

Differential Network Analysis ◽

Differential Network ◽

Gene Regulatory ◽

Muscle Invasive

Abstract Background Recently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC). Results In this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes. Conclusions Furthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer.

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Unraveling Root Development Through Single-Cell Omics and Reconstruction of Gene Regulatory Networks

Frontiers in Plant Science ◽

10.3389/fpls.2021.661361 ◽

2021 ◽

Vol 12 ◽

Author(s):

Laura Serrano-Ron ◽

Javier Cabrera ◽

Pablo Perez-Garcia ◽

Miguel A. Moreno-Risueno

Keyword(s):

Single Cell ◽

Cell Fate ◽

Gene Regulatory Networks ◽

Root Development ◽

Regulatory Networks ◽

Primary Root ◽

Cell Fate Specification ◽

Rna Seq ◽

Fate Specification ◽

Gene Regulatory

Over the last decades, research on postembryonic root development has been facilitated by “omics” technologies. Among these technologies, microarrays first, and RNA sequencing (RNA-seq) later, have provided transcriptional information on the underlying molecular processes establishing the basis of System Biology studies in roots. Cell fate specification and development have been widely studied in the primary root, which involved the identification of many cell type transcriptomes and the reconstruction of gene regulatory networks (GRN). The study of lateral root (LR) development has not been an exception. However, the molecular mechanisms regulating cell fate specification during LR formation remain largely unexplored. Recently, single-cell RNA-seq (scRNA-seq) studies have addressed the specification of tissues from stem cells in the primary root. scRNA-seq studies are anticipated to be a useful approach to decipher cell fate specification and patterning during LR formation. In this review, we address the different scRNA-seq strategies used both in plants and animals and how we could take advantage of scRNA-seq to unravel new regulatory mechanisms and reconstruct GRN. In addition, we discuss how to integrate scRNA-seq results with previous RNA-seq datasets and GRN. We also address relevant findings obtained through single-cell based studies and how LR developmental studies could be facilitated by scRNA-seq approaches and subsequent GRN inference. The use of single-cell approaches to investigate LR formation could help to decipher fundamental biological mechanisms such as cell memory, synchronization, polarization, or pluripotency.

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Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds

Future Oncology ◽

10.2217/fon-2019-0275 ◽

2019 ◽

Vol 15 (34) ◽

pp. 3917-3934

Author(s):

Min-Cheng Yu ◽

Ji-Xiang Liu ◽

Xiao-Lu Ma ◽

Bo Hu ◽

Pei-Yao Fu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Network Analysis ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Regulatory Networks ◽

Differential Network Analysis ◽

Diverse Background ◽

Differential Network ◽

Diverse Backgrounds ◽

Gene Regulatory

Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hsa-mir-19a was identified as showed different effects in the three HCC differential regulation networks, and showed vital regulatory role in HBV-related HCC. Conclusion: We describe in detail the regulatory networks involved in HCC with different backgrounds. E2F1 may serve as a universal target for HCC treatment.

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Effect of imputation on gene network reconstruction from single-cell RNA-seq data

10.1101/2021.04.13.439623 ◽

2021 ◽

Author(s):

Lam-Ha Ly ◽

Martin Vingron

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Network Reconstruction ◽

Data Sets ◽

Rna Seq ◽

Imputation Methods ◽

Before And After ◽

Gene Regulatory ◽

The Difference

AbstractDespite the advances in single-cell transcriptomics the reconstruction of gene regulatory networks remains challenging. Both the large amount of zero counts in experimental data and the lack of a consensus preprocessing pipeline for single-cell RNA-seq data make it hard to infer networks from transcriptome data. Data imputation can be applied in order to enhance gene-gene correlations and facilitate downstream data analysis. However, it is unclear what consequences imputation methods have on the reconstruction of gene regulatory networks.To study this question, we evaluate the effect of imputation methods on the performance and structure of the reconstructed networks in different experimental single-cell RNA-seq data sets. We use state-of-the-art algorithms for both imputation and network reconstruction and evaluate the difference in results before and after imputation. We observe an inflation of gene-gene correlations that affects the predicted network structures and may decrease the performance of network reconstruction in general. Yet, within the modest limits of achievable results, we also make a recommendation as to an advisable combination of algorithms, while warning against the indiscriminate use of imputation before network reconstruction in general.

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Cross-species transcriptomic and epigenomic analysis reveals key regulators of injury response and neuronal regeneration in vertebrate retinas

10.1101/717876 ◽

2019 ◽

Cited By ~ 6

Author(s):

Thanh Hoang ◽

Jie Wang ◽

Patrick Boyd ◽

Fang Wang ◽

Clayton Santiago ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Gene Networks ◽

Regulatory Networks ◽

Chromatin Accessibility ◽

Specific Gene ◽

Muller Glia ◽

Müller Glia ◽

Rna Seq ◽

Adult Mice ◽

Gene Regulatory

AbstractInjury induces retinal Müller glia of cold-blooded, but not mammalian, vertebrates to regenerate neurons. To identify gene regulatory networks that control neuronal reprogramming in retinal glia, we comprehensively profiled injury-dependent changes in gene expression and chromatin accessibility in Müller glia from zebrafish, chick and mice using bulk RNA-Seq and ATAC-Seq, as well as single-cell RNA-Seq. Cross-species integrative analysis of these data, together with functional validation, identified evolutionarily conserved and species-specific gene networks controlling glial quiescence, gliosis and neurogenesis. In zebrafish and chick, transition from the resting state to gliosis is essential for initiation of retinal regeneration, while in mice a dedicated network suppresses neurogenic competence and restores quiescence. Selective disruption of NFI family transcription factors, which maintain and restore quiescence, enables Müller glia to proliferate and generate neurons in adult mice following retinal injury. These findings may aid in the design of cell-based therapies aimed at restoring retinal neurons lost to degenerative disease.Summary sentenceThis study identifies gene regulatory networks controlling proliferative and neurogenic competence in retinal Müller glia.

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