scholarly journals corto: a lightweight R package for gene network inference and master regulator analysis

2020 ◽  
Vol 36 (12) ◽  
pp. 3916-3917 ◽  
Author(s):  
Daniele Mercatelli ◽  
Gonzalo Lopez-Garcia ◽  
Federico M Giorgi
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Network Inference ◽  
Human Tumor ◽  
R Package ◽  
Supplementary Information ◽  
Specific Gene ◽  
Master Regulator ◽  
Gene Network Inference

Abstract Motivation Gene network inference and master regulator analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses but most require a computer cluster or large amounts of RAM to be executed. Results We developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for copy-number variations and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets. Availability and implementation Cross-platform and multi-threaded R package on CRAN (stable version) https://cran.r-project.org/package=corto and Github (development release) https://github.com/federicogiorgi/corto. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals corto: a lightweight R package for Gene Network Inference and Master Regulator Analysis

2020 ◽  
Author(s):  
Daniele Mercatelli ◽  
Gonzalo Lopez-Garcia ◽  
Federico M. Giorgi
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Network Inference ◽  
Human Tumor ◽  
R Package ◽  
Specific Gene ◽  
Master Regulator ◽  
Gene Network Inference ◽  
Link Type

AbstractMotivationGene Network Inference and Master Regulator Analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses, but most require a computer cluster or large amounts of RAM to be executed.ResultsWe developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for Copy Number Variations (CNVs) and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.AvailabilityCross-platform and multi-threaded R package on CRAN (stable version) https://cran.rproject.org/package=corto and Github (development release) https://github.com/federicogiorgi/[email protected]

Complex Networks, Gene Expression and Cancer Complexity: A Brief Review of Methodology and Applications

2020 ◽  
Vol 15 (6) ◽  
pp. 629-655
Author(s):  
A.C. Iliopoulos ◽  
G. Beis ◽  
P. Apostolou ◽  
I. Papasotiriou
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Complex Networks ◽  
Gene Network ◽  
Network Inference ◽  
Mathematical Framework ◽  
Cancer Gene ◽  
Expression Data ◽  
Gene Network Inference ◽  
Basic Features

In this brief survey, various aspects of cancer complexity and how this complexity can be confronted using modern complex networks’ theory and gene expression datasets, are described. In particular, the causes and the basic features of cancer complexity, as well as the challenges it brought are underlined, while the importance of gene expression data in cancer research and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction to the corresponding theoretical and mathematical framework of graph theory and complex networks is provided. The basics of network reconstruction along with the limitations of gene network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades in complex networks, are described. Finally, an indicative and suggestive example of a cancer gene co-expression network inference and analysis is given.

IMPROVING GENE NETWORK INFERENCE BY COMPARING EXPRESSION TIME-SERIES ACROSS SPECIES, DEVELOPMENTAL STAGES OR TISSUES

2004 ◽  
Vol 02 (04) ◽  
pp. 765-783 ◽  
Author(s):  
GUILLAUME BOURQUE ◽  
DAVID SANKOFF
Keyword(s):  
Time Series ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Interaction Coefficients ◽  
Gene Network Inference ◽  
Linear Differential

We present a method for gene network inference and revision based on time-series data. Gene networks are modeled using linear differential equations and a generalized stepwise multiple linear regression procedure is used to recover the interaction coefficients. Our system is designed for the recovery of gene interactions concurrently in many gene regulatory networks related by a tree or a more general graph. We show how this comparative framework can facilitate the recovery of the networks and improve the quality of the solutions inferred.

Prediction of survival risks with adjusted gene expression through risk-gene networks

2019 ◽  
Vol 35 (23) ◽  
pp. 4898-4906
Author(s):  
Minhyeok Lee ◽  
Sung Won Han ◽  
Junhee Seok
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Prior Knowledge ◽  
Gene Networks ◽  
Gene Network ◽  
Penalized Regression ◽  
Supplementary Information ◽  
Risk Gene ◽  
Survival Risk ◽  
The Relationship

Abstract Motivation Network-based analysis of biomedical data has been extensively studied over the last decades. As a successful application, gene networks have been used to illustrate interactions among genes and explain the associated phenotypes. However, the gene network approaches have not been actively applied for survival analysis, which is one of the main interests of biomedical research. In addition, a few previous studies using gene networks for survival analysis construct networks mainly from prior knowledge, such as pathways, regulations and gene sets, while the performance considerably depends on the selection of prior knowledge. Results In this paper, we propose a data-driven construction method for survival risk-gene networks as well as a survival risk prediction method using the network structure. The proposed method constructs risk-gene networks with survival-associated genes using penalized regression. Then, gene expression indices are hierarchically adjusted through the networks to reduce the variance intrinsic in datasets. By illustrating risk-gene structure, the proposed method is expected to provide an intuition for the relationship between genes and survival risks. The risk-gene network is applied to a low grade glioma dataset, and produces a hypothesis of the relationship between genetic biomarkers of low and high grade glioma. Moreover, with multiple datasets, we demonstrate that the proposed method shows superior prediction performance compared to other conventional methods. Availability and implementation The R package of risk-gene networks is freely available in the web at http://cdal.korea.ac.kr/NetDA/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DepEst: an R package of important dependency estimators for gene network inference algorithms

2017 ◽  
Author(s):  
Gökmen Altay ◽  
Zeyneb Kurt ◽  
Nejla Altay ◽  
Nizamettin Aydin
Keyword(s):  
Gene Network ◽  
Network Inference ◽  
R Package ◽  
Supplementary File ◽  
Main Process ◽  
Data Sets ◽  
Gene Network Inference ◽  
Inference Algorithms ◽  
Almost All ◽  
Access Link

AbstractGene network inference algorithms (GNI) are popular in bioinformatics area. In almost all GNI algorithms, the main process is to estimate the dependency (association) scores among the genes of the dataset.We present a bioinformatics tool, DepEst (Dependency Estimators), which is a powerful and flexible R package that includes 11 important dependency score estimators that can be used in almost all GNI Algorithms. DepEst is the first bioinformatics package that includes such a large number of estimators that runs both in parallel and serial.DepEst is currently available at https://github.com/altayg/Depest. Package access link, instructions, various workflows and example data sets are provided in the supplementary file.

scholarly journals Directed Conservative Causal Core Gene Networks

2018 ◽  
Author(s):  
Gokmen Altay
Keyword(s):  
Gene Networks ◽  
Large Scale ◽  
Network Inference ◽  
R Package ◽  
Core Gene ◽  
Supplementary Information ◽  
Supplementary File ◽  
Core Network ◽  
Large Scale Networks ◽  
Direction Information

AbstractMotivation:Inferring large scale directional networks with higher accuracy has important applications such as gene regulatory network or finance.Results:We modified a well-established conservative causal core network inference algorithm, C3NET, to be able to infer very large scale networks with direction information. This advanced version is called Ac3net. We demonstrate that Ac3net outperforms C3NET and many other popular algorithms when considering the directional interaction information of gene/protein networks. We provide and R package and present performance results that are reproducible via the Supplementary file.Availability:Ac3net is available on CRAN and at github.com/altayg/Ac3netContact:[email protected] information:Supplementary file is available online.

scholarly journals SeaBase: A Multispecies Transcriptomic Resource and Platform for Gene Network Inference

2014 ◽  
Vol 54 (2) ◽  
pp. 250-263 ◽  
Author(s):  
A. H. L. Fischer ◽  
D. Mozzherin ◽  
A. M. Eren ◽  
K. D. Lans ◽  
N. Wilson ◽  
...  
Keyword(s):  
Gene Network ◽  
Network Inference ◽  
Gene Network Inference ◽  
Transcriptomic Resource

scholarly journals Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

2019 ◽  
Vol 36 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Empirical Bayes ◽  
Brain Regions ◽  
Tissue Level ◽  
Supplementary Information ◽  
Specific Gene ◽  
Expression Data ◽  
Cell Type ◽  
Multiple Measurements ◽  
Cell Type Specific

Abstract Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.

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.

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