GNET2: an R package for constructing gene regulatory networks from transcriptomic data

2020 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A Birchler ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Data Exchange ◽  
Graphical Model ◽  
R Package ◽  
Supplementary Information ◽  
Original Algorithm ◽  
Transcriptomic Data ◽  
Regulatory Module ◽  
Gene Regulatory

Abstract Motivation The Gene Network Estimation Tool (GNET) is designed to build gene regulatory networks (GRNs) from transcriptomic gene expression data with a probabilistic graphical model. The data preprocessing, model construction and visualization modules of the original GNET software were developed on different programming platforms, which were inconvenient for users to deploy and use. Results Here, we present GNET2, an improved implementation of GNET as an integrated R package. GNET2 provides more flexibility for parameter initialization and regulatory module construction based on the core iterative modeling process of the original algorithm. The data exchange interface of GNET2 is handled within an R session automatically. Given the growing demand for regulatory network reconstruction from transcriptomic data, GNET2 offers a convenient option for GRN inference on large datasets. Availability and implementation The source code of GNET2 is available at https://github.com/jianlin-cheng/GNET2. Supplementary information Supplementary data are available at Bioinformatics online.

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

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.

scholarly journals Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

2019 ◽  
Vol 36 (1) ◽  
pp. 197-204 ◽  
Author(s):  
Xin Zhou ◽  
Xiaodong Cai
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Structural Equation ◽  
Regulatory Networks ◽  
Supplementary Information ◽  
Specific Gene ◽  
Joint Inference ◽  
Perturbation Data ◽  
Gene Regulatory ◽  
The Difference

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DCI: learning causal differences between gene regulatory networks

2021 ◽  
Author(s):  
Anastasiya Belyaeva ◽  
Chandler Squires ◽  
Caroline Uhler
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Developmental Time ◽  
Supplementary Information ◽  
Causal Graph ◽  
Causal Graphs ◽  
Gene Regulatory

Abstract Summary Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Availability and implementation Python package freely available at http://uhlerlab.github.io/causaldag/dci. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals IReNA: integrated regulatory network analysis of single-cell transcriptomes

2021 ◽  
Author(s):  
junyao jiang ◽  
Seth Blackshaw ◽  
Jiang Qian ◽  
Jie Wang
Keyword(s):  
Network Analysis ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
R Package ◽  
Integrated Network ◽  
Binding Motifs ◽  
Gene Regulatory ◽  
Public Datasets

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.

scholarly journals Programmatic access to bacterial regulatory networks with regutools

2020 ◽  
Vol 36 (16) ◽  
pp. 4532-4534
Author(s):  
Joselyn Chávez ◽  
Carmina Barberena-Jonas ◽  
Jesus E Sotelo-Fonseca ◽  
José Alquicira-Hernández ◽  
Heladia Salgado ◽  
...  
Keyword(s):  
Data Structures ◽  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
R Package ◽  
Transcriptional Regulatory Networks ◽  
Dna Binding Sites ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
Programmatic Access

Abstract Summary RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools gives researchers the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. Availability and implementation regutools is an R package available through Bioconductor at bioconductor.org/packages/regutools.

PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data

2020 ◽  
Author(s):  
Gourab Ghosh Roy ◽  
Nicholas Geard ◽  
Karin Verspoor ◽  
Shan He
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Network Inference ◽  
State Of The Art ◽  
Supplementary Information ◽  
Expression Data ◽  
Gene Regulatory Network Inference ◽  
Regulatory Interactions ◽  
Inference Algorithms ◽  
Gene Regulatory

Abstract Motivation Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles. Results To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets. Availability and implementation Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Reconstructing gene regulatory networks in single-cell transcriptomic data analysis

2020 ◽  
Vol 41 (6) ◽  
pp. 599-604
Author(s):  
Hao Dai ◽  
◽  
◽  
Qi-Qi Jin ◽  
Lin Li ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Transcriptomic Data ◽  
Gene Regulatory

scholarly journals Crosslink: An R Package for Network Visualization of Grouped Nodes

2021 ◽  
Vol 12 ◽  
Author(s):  
Di Liu ◽  
Zhijie Bai ◽  
Bing Liu ◽  
Zongcheng Li
Keyword(s):  
Gene Regulatory Networks ◽  
Drug Target ◽  
Regulatory Networks ◽  
R Package ◽  
Network Visualization ◽  
Receptor Interactions ◽  
Gene Regulatory ◽  
And Function ◽  
User Friendly ◽  
Coordinates Transformation

The demand for network visualization of relationships between nodes attributed to different categories grows in various biomedical research scenarios, such as gene regulatory networks, drug-target networks, ligand-receptor interactions and association networks of multi-omics elements. Elegantly visualizing the relationships between nodes with complex metadata of nodes and edges appended may inspire new insights. Here, we developed the crosslink R package, tailored for network visualization of grouped nodes, to provide a series of flexible functions for generating network diagrams. We first designed a CrossLink class for storage of metadata about nodes and edges and manipulation of node coordinates. Then affine transformation and function mapping transformation are implemented to perform fundamental node coordinates transformation by groups, based on which various network layouts can be defined easily. For convenience, we predefined several commonly used layouts, including row, column, arc, polygon and hive, which also can be combined in one layout. Finally, we designed a user-friendly wrapper function to draw network connections, aesthetic mappings of metadata and decoration with related annotation graphs in one interface by taking advantage of the powerful ggplot2 system. Overall, the crosslink R package is easy-to-use for achieving complex visualization of a network diagram of grouped nodes surrounded by associated annotation graphs.Availability and ImplementationCosslink is an open-source R package, freely available from github: https://github.com/zzwch/crosslink; A detailed user documentation can be found in https://zzwch.github.io/crosslink/.

scholarly journals ELMER v.2: an R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

2018 ◽  
Vol 35 (11) ◽  
pp. 1974-1977 ◽  
Author(s):  
Tiago C Silva ◽  
Simon G Coetzee ◽  
Nicole Gull ◽  
Lijing Yao ◽  
Dennis J Hazelett ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Gene Regulatory Networks ◽  
Statistical Power ◽  
Regulatory Networks ◽  
Supplementary Information ◽  
Bioconductor Package ◽  
Functional Changes ◽  
Basal Like Breast Cancer ◽  
Gene Regulatory ◽  
Altered Gene

Abstract Motivation DNA methylation has been used to identify functional changes at transcriptional enhancers and other cis-regulatory modules (CRMs) in tumors and other disease tissues. Our R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships) provides a systematic approach that reconstructs altered gene regulatory networks (GRNs) by combining enhancer methylation and gene expression data derived from the same sample set. Results We present a completely revised version 2 of ELMER that provides numerous new features including an optional web-based interface and a new Supervised Analysis mode to use pre-defined sample groupings. We show that Supervised mode significantly increases statistical power and identifies additional GRNs and associated Master Regulators, such as SOX11 and KLF5 in Basal-like breast cancer. Availability and implementation ELMER v.2 is available as an R/Bioconductor package at http://bioconductor.org/packages/ELMER/. Supplementary information Supplementary data are available at Bioinformatics online.

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