We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) <doi:10.1371/journal.pcbi.1005662>.
Version: | 1.0.3 |
Imports: | MASS, igraph, bnlearn, ppcor, stats, Rcpp |
LinkingTo: | Rcpp |
Published: | 2018-02-02 |
Author: | Nadir Sella [aut, cre], Louis Verny [aut], Severine Affeldt [aut], Hervé Isambert [aut] |
Maintainer: | Nadir Sella <nadir.sella at curie.fr> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | yes |
CRAN checks: | miic results |
Reference manual: | miic.pdf |
Package source: | miic_1.0.3.tar.gz |
Windows binaries: | r-devel: miic_1.0.3.zip, r-release: miic_1.0.3.zip, r-oldrel: miic_1.0.3.zip |
OS X El Capitan binaries: | r-release: miic_1.0.3.tgz |
OS X Mavericks binaries: | r-oldrel: miic_1.0.1.tgz |
Old sources: | miic archive |
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