SIMoNe: Statistical Inference for Modular Networks
The goals of SIMoNe is to implement methods for the inference of co-expression networks based on partial correlation coefficients from either steady-state or time-course transcriptomic data. Note that with both type of data this package can deal with samples collected in different experimental conditions and therefore not identically distributed. In this particular case, multiple but related networks are inferred on one simone run.
To install CRAN version,
To install dev version,
The available inference methods for edges selection include
- neighborhood selection as in Meinshausen and Buhlman (2006), steady-state data only;
- graphical Lasso as in Banerjee et al, 2008 and Friedman et al (2008), steady-state data only;
- VAR(1) inference as in Charbonnier, Chiquet and Ambroise (2010), time-course data only;
- multitask learning as in Chiquet, Grandvalet and Ambroise (2011), both time-course and steady-state data.
All the listed methods are based l1-norm penalization, with an additional grouping effect for multitask learning (including three variants: “intertwined”, “group-Lasso” and “cooperative-Lasso”).
The penalization of each individual edge may be weighted according to a
latent clustering of the network, thus adapting the inference of the
network to a particular topology. The clustering algorithm is performed
Since the choice of the network sparsity level remains a current issue in the framework of sparse Gaussian network inference, the algorithm provides a full path of estimators starting from an empty network and adding edges as the penalty level progressively decreases. Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC) are adapted to the GGM context in order to help to choose one particular network among this path of solutions.
Graphical tools are provided to summarize the results of a SIMoNe run and offer various representations for network plotting.
Have a look at the documentation. You may also check the demos:
demo(cancer_pooled) demo(cancer_multitask) demo(check_glasso, echo=FALSE) demo(simone_steadyState) demo(simone_timeCourse) demo(simone_multitask)
J. Chiquet, Y. Grandvalet, and C. Ambroise (2011). Inferring multiple graphical structures, Statistics and Computing. http://dx.doi.org/10.1007/s11222-010-9191-2
C. Charbonnier, J. Chiquet, and C. Ambroise (2010). Weighted-Lasso for Structured Network Inference from Time Course Data. Statistical Applications in Genetics and Molecular Biology, vol. 9, iss. 1, article 15. http://www.bepress.com/sagmb/vol9/iss1/art15/
C. Ambroise, J. Chiquet, and C. Matias (2009). Inferring sparse Gaussian graphical models with latent structure. Electronic Journal of Statistics, vol. 3, pp. 205–238. http://dx.doi.org/10.1214/08-EJS314
Leger, Jean-Benoist. “Blockmodels: A R-package for estimating in Latent Block Model and Stochastic Block Model, with various probability functions, with or without covariates.” arXiv preprint arXiv:1602.07587 (2016).