@article{Parsana2019,
abstract = {Background: Gene co-expression networks capture diverse biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanisms. Functional interactions between genes have not been fully characterized for most organisms, and therefore reconstruction of gene co-expression networks has been of common interest in a variety of settings. However, methods routinely used for reconstruction of gene co-expression networks do not account for confounding artifacts known to affect high dimensional gene expression measurements. Results: In this study, we show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. Both theoretically and empirically, we demonstrate that removing the effects of top principal components from gene expression measurements prior to network inference can reduce false discoveries, especially when well annotated technical covariates are not available. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, we show that this latent factor residualization approach often reduces false discoveries in the reconstructed networks. Conclusion: Network reconstruction is susceptible to confounders that affect measurements of gene expression. Even controlling for major individual known technical covariates fails to fully eliminate confounding variation from the data. In studies where a wide range of annotated technical factors are measured and available, correcting gene expression data with multiple covariates can also improve network reconstruction, but such extensive annotations are not always available. Our study shows that principal component correction, which does not depend on study design or annotation of all relevant confounders, removes patterns of artifactual variation and improves network reconstruction in both simulated data, and gene expression data from GTEx project. We have implemented our PC correction approach in the Bioconductor package sva which can be used prior to network reconstruction with a range of methods.},
author = {Parsana, Princy and Ruberman, Claire and Jaffe, Andrew E. and Schatz, Michael C. and Battle, Alexis and Leek, Jeffrey T.},
doi = {10.1186/s13059-019-1700-9},
file = {:Users/macbook/Library/Application Support/Mendeley Desktop/Downloaded/Parsana et al. - 2019 - Addressing confounding artifacts in reconstruction of gene co-expression networks.pdf:pdf},
issn = {1474-760X},
journal = {Genome Biology},
number = {1},
pages = {94},
publisher = {Genome Biology},
title = {{Addressing confounding artifacts in reconstruction of gene co-expression networks}},
url = {https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1700-9},
volume = {20},
year = {2019}
}
@article{Langfelder2008,
abstract = {BACKGROUND: Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. RESULTS: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. CONCLUSION: The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.},
author = {Langfelder, Peter and Horvath, Steve},
doi = {10.1186/1471-2105-9-559},
file = {:Users/macbook/Library/Application Support/Mendeley Desktop/Downloaded/Langfelder, Horvath - 2008 - WGCNA an R package for weighted correlation network analysis.pdf:pdf},
isbn = {1471-2105},
issn = {1471-2105},
journal = {BMC Bioinformatics},
number = {1},
pages = {559},
pmid = {19114008},
title = {{WGCNA: an R package for weighted correlation network analysis}},
url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559},
volume = {9},
year = {2008}
}
@article{Russo2018,
abstract = {The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.},
author = {Russo, Pedro S. T. and Ferreira, Gustavo R. and Cardozo, Lucas E. and B{\"{u}}rger, Matheus C. and Arias-Carrasco, Raul and Maruyama, Sandra R. and Hirata, Thiago D. C. and Lima, Di{\'{o}}genes S. and Passos, Fernando M. and Fukutani, Kiyoshi F. and Lever, Melissa and Silva, Jo{\~{a}}o S. and Maracaja-Coutinho, Vinicius and Nakaya, Helder I.},
doi = {10.1186/s12859-018-2053-1},
file = {:Users/macbook/Library/Application Support/Mendeley Desktop/Downloaded/Russo et al. - 2018 - CEMiTool a Bioconductor package for performing comprehensive modular co-expression analyses.pdf:pdf},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Algorithms,Bioinformatics,Computational Biology/Bioinformatics,Computer Appl. in Life Sciences,Microarrays},
number = {1},
pages = {56},
publisher = {BMC Bioinformatics},
title = {{CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses}},
url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2053-1},
volume = {19},
year = {2018}
}
@article{Petereit2016,
abstract = {BACKGROUND: Networks provide effective models to study complex biological systems, such as gene and protein interaction networks. With the advent of new sequencing technologies, many life scientists are grasping for user-friendly methods and tools to examine biological components at the whole-systems level. Gene co-expression network analysis approaches are frequently used to successfully associate genes with biological processes and demonstrate great potential to gain further insights into the functionality of genes, thus becoming a standard approach in Systems Biology. Here the objective is to construct biologically meaningful and statistically strong co-expression networks, the identification of research dependent subnetworks, and the presentation of self-contained results.$\backslash$n$\backslash$nRESULTS: We introduce petal, a novel approach to generate gene co-expression network models based on experimental gene expression measures. petal focuses on statistical, mathematical, and biological characteristics of both, input data and output network models. Often over-looked issues of current co-expression analysis tools include the assumption of data normality, which is seldom the case for hight-throughput expression data obtained from RNA-seq technologies. petal does not assume data normality, making it a statistically appropriate method for RNA-seq data. Also, network models are rarely tested for their known typical architecture: scale-free and small-world. petal explicitly constructs networks based on both these characteristics, thereby generating biologically meaningful models. Furthermore, many network analysis tools require a number of user-defined input variables, these often require tuning and/or an understanding of the underlying algorithm; petal requires no user input other than experimental data. This allows for reproducible results, and simplifies the use of petal. Lastly, this approach is specifically designed for very large high-throughput datasets; this way, petal's network models represent as much of the entire system as possible to provide a whole-system approach.$\backslash$n$\backslash$nCONCLUSION: petal is a novel tool for generating co-expression network models of whole-genomics experiments. It is implemented in R and available as a library. Its application to several whole-genome experiments has generated novel meaningful results and has lead the way to new testing hypothesizes for further biological investigation.},
author = {Petereit, Juli and Smith, Sebastian and Harris, Frederick C. and Schlauch, Karen A.},
doi = {10.1186/s12918-016-0298-8},
file = {:Users/macbook/Library/Application Support/Mendeley Desktop/Downloaded/Petereit et al. - 2016 - petal Co-expression network modelling in R.pdf:pdf},
isbn = {17520509 (Electronic)},
issn = {1752-0509},
journal = {BMC Systems Biology},
keywords = {0,1664 n,2016 the author,89557 reno,com,commons attribution 4,correspondence,gmail,is distributed under the,julipetereit,open access this article,parameter-free algorithm,r,reno,s,scale-free,small-world,terms of the creative,university of nevada,usa,virginia street,whole omics-approach},
number = {S2},
pages = {51},
pmid = {27490697},
title = {{petal: Co-expression network modelling in R}},
url = {http://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-016-0298-8},
volume = {10},
year = {2016}
}
@article{VanBel2018,
abstract = {PLAZA (https://bioinformatics.psb.ugent.be/plaza) is a plant-oriented online resource for comparative, evolutionary and functional genomics. The PLAZA platform consists of multiple independent instances focusing on different plant clades, while also providing access to a consistent set of reference species. Each PLAZA instance contains structural and functional gene annotations, gene family data and phylogenetic trees and detailed gene colinearity information. A user-friendly web interface makes the necessary tools and visualizations accessible, specific for each data type. Here we present PLAZA 4.0, the latest iteration of the PLAZA framework. This version consists of two new instances (Dicots 4.0 and Monocots 4.0) providing a large increase in newly available species, and offers access to updated and newly implemented tools and visualizations, helping users with the ever-increasing demands for complex and in-depth analyzes. The total number of species across both instances nearly doubles from 37 species in PLAZA 3.0 to 71 species in PLAZA 4.0, with a much broader coverage of crop species (e.g. wheat, palm oil) and species of evolutionary interest (e.g. spruce, Marchantia). The new PLAZA instances can also be accessed by a programming interface through a RESTful web service, thus allowing bioinformaticians to optimally leverage the power of the PLAZA platform.},
author = {{Van Bel}, Michiel and Diels, Tim and Vancaester, Emmelien and Kreft, Lukasz and Botzki, Alexander and {Van De Peer}, Yves and Coppens, Frederik and Vandepoele, Klaas},
doi = {10.1093/nar/gkx1002},
file = {:Users/macbook/Documents/Papers/gkx1002.pdf:pdf},
issn = {13624962},
journal = {Nucleic Acids Research},
number = {D1},
pages = {D1190--D1196},
pmid = {29069403},
publisher = {Oxford University Press},
title = {{PLAZA 4.0: An integrative resource for functional, evolutionary and comparative plant genomics}},
volume = {46},
year = {2018}
}
@article{Love2014,
abstract = {In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.},
author = {Love, Michael I. and Huber, Wolfgang and Anders, Simon},
doi = {10.1186/s13059-014-0550-8},
file = {:Users/macbook/Documents/Papers/13059{\_}2014{\_}Article{\_}550.pdf:pdf},
issn = {1474760X},
journal = {Genome Biology},
number = {12},
pages = {1--21},
pmid = {25516281},
title = {{Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2}},
volume = {15},
year = {2014}
}
@article{Oldham2012,
abstract = {BACKGROUND: Genomic datasets generated by new technologies are increasingly prevalent in disparate areas of biological research. While many studies have sought to characterize relationships among genomic features, commensurate efforts to characterize relationships among biological samples have been less common. Consequently, the full extent of sample variation in genomic studies is often under-appreciated, complicating downstream analytical tasks such as gene co-expression network analysis.$\backslash$n$\backslash$nRESULTS: Here we demonstrate the use of network methods for characterizing sample relationships in microarray data generated from human brain tissue. We describe an approach for identifying outlying samples that does not depend on the choice or use of clustering algorithms. We introduce a battery of measures for quantifying the consistency and integrity of sample relationships, which can be compared across disparate studies, technology platforms, and biological systems. Among these measures, we provide evidence that the correlation between the connectivity and the clustering coefficient (two important network concepts) is a sensitive indicator of homogeneity among biological samples. We also show that this measure, which we refer to as cor(K,C), can distinguish biologically meaningful relationships among subgroups of samples. Specifically, we find that cor(K,C) reveals the profound effect of Huntington's disease on samples from the caudate nucleus relative to other brain regions. Furthermore, we find that this effect is concentrated in specific modules of genes that are naturally co-expressed in human caudate nucleus, highlighting a new strategy for exploring the effects of disease on sets of genes.$\backslash$n$\backslash$nCONCLUSIONS: These results underscore the importance of systematically exploring sample relationships in large genomic datasets before seeking to analyze genomic feature activity. We introduce a standardized platform for this purpose using freely available R software that has been designed to enable iterative and interactive exploration of sample networks.},
author = {Oldham, Michael C. and Langfelder, Peter and Horvath, Steve},
doi = {10.1186/1752-0509-6-63},
file = {:Users/macbook/Library/Application Support/Mendeley Desktop/Downloaded/Oldham, Langfelder, Horvath - 2012 - Network methods for describing sample relationships in genomic datasets application to Huntington's.pdf:pdf},
issn = {17520509},
journal = {BMC Systems Biology},
keywords = {Clustering coefficient,Data pre-processing,Gene expression,Huntington's disease,Microarrays,Sample network analysis,Sample networks,Standardized C(k) curve,cor(K,C)},
number = {1},
pages = {1},
publisher = {BMC Systems Biology},
title = {{Network methods for describing sample relationships in genomic datasets: application to Huntington's disease}},
url = {BMC Systems Biology},
volume = {6},
year = {2012}
}
@article{Shin2020,
author = {Shin, Junha and Marx, Harald and Richards, Alicia and Vaneechoutte, Dries and Jayaraman, Dhileepkumar and Maeda, Junko and Chakraborty, Sanhita and Sussman, Michael and Coon, Joshua and Roy, Sushmita and Vandepoele, Klaas and An, Jean-michel},
doi = {10.1093/nar/gkaa1041},
file = {:Users/macbook/Documents/Papers/gkaa1041.pdf:pdf},
issn = {0305-1048},
journal = {Nucleic Acids Research},
pages = {1--23},
title = {{A network-based comparative framework to study conservation and divergence of proteomes in plant phylogenies}},
year = {2020}
}
