scholarly journals Single-cell gene regulatory network inference at scale: The Inferelator 3.0

2021 ◽  
Author(s):  
Claudia Skok Gibbs ◽  
Christopher A Jackson ◽  
Giuseppe-Antonio Saldi ◽  
Aashna Shah ◽  
Andreas Tj&aumlrnberg ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Cell Types ◽  
Cellular Growth ◽  
Model Organisms ◽  
Gene Regulatory Network Inference ◽  
Data Set ◽  
Gene Regulatory ◽  
Cell Gene

Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator 3.0 reliably learns informative networks from the model organisms Bacillus subtilis and Saccharomyces cerevisiae. We demonstrate its capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression data set with paired single-cell chromatin accessibility data.

scholarly journals scSGL: Signed Graph Learning for Single-Cell Gene Regulatory Network Inference

2021 ◽  
Author(s):  
Abdullah Karaaslanli ◽  
Satabdi Saha ◽  
Selin Aviyente ◽  
Tapabrata Maiti
Keyword(s):  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Networks ◽  
Signed Graph ◽  
Reconstruction Algorithms ◽  
Gene Regulatory Network Inference ◽  
Graph Learning ◽  
Gene Regulatory ◽  
Cell Gene

Elucidating the topology of gene regulatory networks (GRN) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, which represent a characteristic feature of GRNs, as they account for both activating and inhibitory relationships between genes. To this end, we propose a novel signed GL approach, scSGL, that incorporates the similarity and dissimilarity between observed gene expression data to construct gene networks. The proposed approach is formulated as a non-convex optimization problem and solved using an efficient ADMM framework. In our experiments on simulated and real single cell datasets, scSGL compares favorably with other single cell gene regulatory network reconstruction algorithms.

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

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.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

scholarly journals Single cell and single nucleus RNA-Seq reveal cellular heterogeneity and homeostatic regulatory networks in adult mouse stria vascularis

2019 ◽  
Author(s):  
Soumya Korrapati ◽  
Ian Taukulis ◽  
Rafal Olszewski ◽  
Madeline Pyle ◽  
Shoujun Gu ◽  
...  
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Stria Vascularis ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Genetic Mechanisms ◽  
Single Nucleus ◽  
Gene Regulatory

AbstractThe stria vascularis (SV) generates the endocochlear potential (EP) in the inner ear and is necessary for proper hair cell mechanotransduction and hearing. While channels belonging to SV cell types are known to play crucial roles in EP generation, relatively little is known about gene regulatory networks that underlie the ability of the SV to generate and maintain the EP. Using single cell and single nucleus RNA-sequencing, we identify and validate known and rare cell populations in the SV. Furthermore, we establish a basis for understanding molecular mechanisms underlying SV function by identifying potential gene regulatory networks as well as druggable gene targets. Finally, we associate known deafness genes with adult SV cell types. This work establishes a basis for dissecting the genetic mechanisms underlying the role of the SV in hearing and will serve as a basis for designing therapeutic approaches to hearing loss related to SV dysfunction.

scholarly journals Predicting gene regulatory networks from cell atlases

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.

scholarly journals Multiview Graph Learning for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Abdullah Karaaslanli ◽  
SATABDI SAHA ◽  
Selin Aviyente ◽  
Tapabrata Maiti
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Joint Estimation ◽  
Embryonic Stem Cell Differentiation ◽  
Graph Learning ◽  
Gene Regulatory ◽  
Cell Gene

Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

scholarly journals Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

2020 ◽  
Vol 17 (2) ◽  
pp. 147-154 ◽  
Author(s):  
Aditya Pratapa ◽  
Amogh P. Jalihal ◽  
Jeffrey N. Law ◽  
Aditya Bharadwaj ◽  
T. M. Murali
Keyword(s):  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Transcriptomic Data ◽  
Gene Regulatory

Inferring Gene Regulatory Networks from Genetical Genomics Data

2010 ◽  
pp. 79-107 ◽  
Author(s):  
Bing Liu ◽  
Ina Hoeschele ◽  
Alberto de la Fuente
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Dna Marker ◽  
Search Space ◽  
Genetical Genomics ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory

In this chapter, we review the current state of Gene Regulatory Network inference based on ‘Genetical Genomics’ experiments (Brem & Kruglyak, 2005; Brem, Yvert, Clinton & Kruglyak, 2002; Jansen, 2003; Jansen & Nap, 2001; Schadt et al., 2003) as a special case of causal network inference in ‘Systems Genetics’ (Threadgill, 2006). In a Genetical Genomics experiment, a segregating or genetically randomized population is DNA marker genotyped and gene-expression profiled on a genomewide scale. The genotypes are regarded as natural, multifactorial perturbations resulting in different gene-expression ‘phenotypes’, and causal relationships can therefore be established between the measured genotypes and the gene-expression phenotypes. In this chapter, we review different computational approaches to Gene Regulatory Network inference based on the joint analysis of DNA marker and expression data and additionally of DNA sequence information if available. This includes different methods for expression QTL mapping, selection of regulator-target pairs, construction of an encompassing network, which strongly constrains the network search space, and pairwise and multivariate methods for Gene Regulatory Network inference, such as Bayesian Networks and Structural Equation Modeling.

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.

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