@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{Emms2015,
abstract = {Identifying homology relationships between sequences is fundamental to biological research. Here we provide a novel orthogroup inference algorithm called OrthoFinder that solves a previously undetected gene length bias in orthogroup inference, resulting in significant improvements in accuracy. Using real benchmark datasets we demonstrate that OrthoFinder is more accurate than other orthogroup inference methods by between 8 {\%} and 33 {\%}. Furthermore, we demonstrate the utility of OrthoFinder by providing a complete classification of transcription factor gene families in plants revealing 6.9 million previously unobserved relationships. Background and rationale},
author = {Emms, David M. and Kelly, Steven},
doi = {10.1186/s13059-015-0721-2},
file = {:Users/macbook/Library/Application Support/Mendeley Desktop/Downloaded/Emms, Kelly - 2015 - OrthoFinder solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accur.pdf:pdf},
issn = {1474760X},
journal = {Genome Biology},
number = {1},
pages = {1--14},
publisher = {Genome Biology},
title = {{OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy}},
url = {http://dx.doi.org/10.1186/s13059-015-0721-2},
volume = {16},
year = {2015}
}
@article{Ritchie2016,
abstract = {Network modules—topologically distinct groups of edges and nodes—that are preserved across datasets can reveal common features of organisms, tissues, cell types, and molecules. Many statistics to identify such modules have been developed, but testing their significance requires heuristics. Here, we demonstrate that current methods for assessing module preservation are systematically biased and produce skewed p values. We introduce NetRep, a rapid and computationally efficient method that uses a permutation approach to score module preservation without assuming data are normally distributed. NetRep produces unbiased p values and can distinguish between true and false positives during multiple hypothesis testing. We use NetRep to quantify preservation of gene coexpression modules across murine brain, liver, adipose, and muscle tissues. Complex patterns of multi-tissue preservation were revealed, including a liver-derived housekeeping module that displayed adipose- and muscle-specific association with body weight. Finally, we demonstrate the broader applicability of NetRep by quantifying preservation of bacterial networks in gut microbiota between men and women.},
annote = {NetRep's main advantages: permutation-based. WGCNA's modulePreservation() assumes normality of statistics' null distributions, which is often not true.

Faster.},
author = {Ritchie, Scott C. and Watts, Stephen and Fearnley, Liam G. and Holt, Kathryn E. and Abraham, Gad and Inouye, Michael},
doi = {10.1016/j.cels.2016.06.012},
file = {:Users/macbook/Documents/Papers/PIIS2405471216302174.pdf:pdf},
issn = {24054720},
journal = {Cell Systems},
number = {1},
pages = {71--82},
pmid = {27467248},
publisher = {The Author(s)},
title = {{A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets}},
url = {http://dx.doi.org/10.1016/j.cels.2016.06.012},
volume = {3},
year = {2016}
}
@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}
}
@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}
}
