networkABC: Network Reverse Engineering with Approximate Bayesian Computation

We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as scale-freeness and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.

Version: 0.6-0
Depends: R (≥ 3.0.0)
Imports: RColorBrewer, network, sna
Suggests: PCIT, ggplot2, knitr
Published: 2020-02-07
Author: Frederic Bertrand ORCID iD [cre, aut], Myriam Maumy-Bertrand ORCID iD [aut], Khadija Musayeva [ctb], Nicolas Jung [ctb], Université de Strasbourg [cph], CNRS [cph]
Maintainer: Frederic Bertrand <frederic.bertrand at math.unistra.fr>
BugReports: https://github.com/fbertran/networkABC/issues
License: GPL-3
URL: http://www-irma.u-strasbg.fr/~fbertran/, https://github.com/fbertran/networkABC
NeedsCompilation: yes
Classification/MSC: 62E17, 62F15, 62J07, 62P10, 92C42
Citation: networkABC citation info
Materials: NEWS
CRAN checks: networkABC results

Downloads:

Reference manual: networkABC.pdf
Vignettes: Using the networkABC package
Package source: networkABC_0.6-0.tar.gz
Windows binaries: r-devel: networkABC_0.6-0.zip, r-devel-gcc8: networkABC_0.6-0.zip, r-release: networkABC_0.6-0.zip, r-oldrel: networkABC_0.6-0.zip
OS X binaries: r-release: networkABC_0.6-0.tgz, r-oldrel: networkABC_0.6-0.tgz
Old sources: networkABC archive

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