References, resources and related tools
- Agrawal, R., Imielinski, T. & Swami, A. "Mining Association Rules between Sets of Items in Large Databases" in ACM SIGMOD Conference (eds. Buneman, P. & Jajodia, S.) 207-216 (ACM Press, 1993).
- Aitchison, J., "A Concise Guide to Compositional Data Analysis" in CDA Workshop Girona (2003).
- Brown, M.B., "A Method for Combining Non-Independent, One-Sided Tests of Significance." Biometrics 31, 987-992 (1975).
- Costello, E.K. et al. "Bacterial Community Variation in Human Body Habitats Across Space and Time." Science 326, 1694-1697 (2009).
- Ellson, J., Gansner, E.R., Koutsofios, E., North, S.C. & Woodhull, G. in GRAPH DRAWING SOFTWARE (Springer-Verlag, 2003).
- Faith J.J., et al. "Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles." PLoS Biol, 5:54-66 (2007).
- Hu, Z. et al. "VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology." Nucleic Acids Research 37, W115-W121 (2009).
- Lallich S., Teytaud O., & Prudhomme E. "Statistical inference and data mining: false discoveries control." 17th Compstat Symposium of the IASC:325-336 (2006).
- Legendre, P. & Legendre, L. "Numerical ecology" (Elsevier Science B.V., Amsterdam, 1983).
- Margolin, A.A. et al. "ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context." BMC Bioinformatics, 1-15 (2006)
- Meyer, P.E., Lafitte, F. & Bontempi, G. "minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information." BMC Bioinformatics, 1-10 (2009)
- Meyer P.E., et al. "Information-theoretic inference of large transcriptional regulatory networks." EUROSIP J. Bioinform. Syst. Biol. 79879 (2007).
Below, resources are listed of which CoNet makes use.
Tools are listed in alphabetical order by their name.
-
ARACNE (standalone)
http://wiki.c2b2.columbia.edu/califanolab/index.php/Software/ARACNE
Gene regulatory network inference algorithm, Califano lab
-
BANJO (standalone and Java library)
http://www.cs.duke.edu/~amink/software/banjo/
Banjo is a software application and framework for structure learning of static and dynamic Bayesian networks,
Alexander J. Hartemink and colleagues
-
BAnOCC (R package)
http://huttenhower.sph.harvard.edu/banocc
BAnOCC: Bayesian Analysis of Compositional Correlations. BAnOCC is a package for analyzing compositional covariance
while accounting for the compositional structure, Huttenhower lab
-
bnlearn
http://www.bnlearn.com
An R package for Bayesian network learning and inference, by Marco Scutari
-
BNW (web server)
http://compbio.uthsc.edu/BNW/sourcecodes/home.php
Bayesian Network Webserver for Biological Network Modeling, by Jesse D. Ziebarth, Anindya Bhattacharya and Yan Cui
-
CCLasso (R package)
https://github.com/huayingfang/CCLasso
Correlation Inference for Compositional Data through Lasso, by Fang Huaying
Includes an implementation of SparCC
-
ccrepe (R package)
http://huttenhower.sph.harvard.edu/ccrepe
R package designed to detect significant correlations in compositional data, Huttenhower lab
CoNet also implements the ccrepe approach to p-value computation (combining permutation with renormalization and bootstrap)
-
CGBayesNets (Matlab)
http://www.cgbayesnets.com
CGBayesNets builds and predicts with conditional Gaussian Bayesian networks, by Michael McGeachie & Hsun-Hsien Chang
-
compare-profiles (RSAT tool suite, command line tool)
http://rsat.ulb.ac.be/rsat
Compares all pairs of rows in a matrix using one of several measures, by Jacques van Helden
-
Community Analyzer (standalone)
http://metagenomics.atc.tcs.com/Community_Analyzer/
Inter-microbial interactions across metagenomes, TATA Consultancy Services, Bio-Sciences Division
-
corpcor (R package)
http://strimmerlab.org/software/corpcor/
R package with shrinkage estimator for covariance matrix, Strimmer lab
-
Cyni Toolbox (Cytoscape plugin)
http://apps.cytoscape.org/apps/cynitoolbox
Cytoscape Network Inference Toolbox puts together several tools that allow inferring networks from bio data, Benno Schwikowski and Oriol Guitart Pla
-
EcoSimR (R package)
https://cran.r-project.org/web/packages/EcoSimR/
Given a site by species interaction matrix, users can make inferences about species interactions, by Nick Gotelli, Edmund Hart and Aaron Ellison
-
ExpressionCorrelation (Cytoscape plugin)
http://www.baderlab.org/Software/ExpressionCorrelation
Similarity network from either the genes or conditions in an expression matrix, Bader lab
-
Fast Local Similarity Analysis (standalone)
http://hallam.microbiology.ubc.ca/fastLSA/install/index.html
Directed similarity network from time series data, Hallam lab
-
Fast SparCC (standalone)
https://github.com/scwatts/fastspar
Fast implementation of SparCC, by Stephen Watts
-
Extended Local Similarity Analysis (LSA) (standalone and part of the Galaxy pipeline)
http://meta.usc.edu/softs/lsa/
Directed similarity network from time series data, Sun lab
-
gCoda (R script)
https://github.com/huayingfang/gCoda
Conditional dependence network inference for compositional data, by Fang Huaying
-
GeneNet (R package)
http://strimmerlab.org/software/genenet/index.html
GeneNet is an R package for learning high-dimensional dependency networks from genomic data
(e.g. gene association networks), Strimmer lab
-
LIMITS (Mathematica, R)
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0102451
LIMITS parameterizes the discrete generalized Lotka-Volterra from time series data, by Fisher & Mehta
Equivalent to a network inference, since the interaction matrix is a directed species interaction network
Originally implemented in Mathematica (see supplement), but also available in R (in seqtime)
-
MDSINE (Matlab)
https://bitbucket.org/MDSINE/mdsine/
Parameterization of the generalized Lotka Volterra, by Vanni Bucci
Equivalent to a network inference, since the interaction matrix is a directed species interaction network
-
MENA (web server)
http://ieg2.ou.edu/MENA
Molecular Ecological Network Analysis Pipeline, Zhou lab
-
MetaNet (MATLAB)
http://biostatistics.csmc.edu/MetaNet/
Software tool for network construction and biologically significant module detection, by Zhenqiu Liu, Shili Lin and Steven Piantadosi
-
MINE (Java and R)
http://www.exploredata.net
Maximal Information-based Nonparametric Exploration, by David and Yakir Reshef
-
MInt (R package)
https://cran.r-project.org/web/packages/MInt/index.html
Learns direct microbe-microbe interaction networks using a Poisson multivariate-normal hierarchical model with an L1 penalized precision matrix, by Surojit Biswas
-
MLRR (MATLAB)
https://biostatistics.csmc.edu/mlrr/
Multilevel regularized regression method that simultaneously identifies taxa and constructs networks, by Zhenqiu Liu
-
MONET (Cytoscape plugin and web service)
http://monet.kisti.re.kr
Regulatory network inference algorithm based on Bayesian network learning, by Phil Hyoun Lee and Doheon Lee
-
NetCutter (standalone)
http://muller.group.ifom-ieo-campus.it/
Co-occurrence networks identification and analysis, by Heiko Mueller and Francesco Mancuso
-
Otu.association (part of mothur)
https://www.mothur.org/wiki/Otu.association
Calculates the correlation coefficient for the OTUs, by Pat Schloss
-
Picante (R package)
http://picante.r-forge.r-project.org/
Phylogeny and trait diversity, community null models, by Peter Cowan, Matthew Helmus and Steven Kembel
-
REBACCA (R code)
http://faculty.wcas.northwestern.edu/~hji403/REBACCA.htm
Significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank, by Yuguang Ban, Lingling An and Hongmei Jiang
-
SparCC (Python standalone)
https://bitbucket.org/yonatanf/sparcc
Python module for computing correlations in compositional data, by Yonatan Friedman
-
Sparse S-map (Mathematica)
http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12814/abstract
Equation-free method to infer interaction networks (implementation in the supplement), by Kenta Suzuki Katsuhiko Yoshida, Yumiko Nakanishi and Shinji Fukuda
-
sspairs (R package)
https://github.com/mjwestgate/sppairs
Designed to calculate the degree of spatial association/co-occurrence between species from presence/absence data, by Martin Westgate and Peter Lane
-
SPIEC-EASI (R package)
http://bonneaulab.bio.nyu.edu/software.html#spieceasi
Sparse InversE Covariance estimation for Ecological Association Inference, by Zachary Kurtz, Bonneau lab
Includes an implementation of SparCC
-
WGCNA (R package)
http://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/
R package for weighted correlation network analysis, by Peter Langfelder and Steve Horvath