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: Cabeli et al. PLoS Comp. Bio. 2020 <doi:10.1371/journal.pcbi.1007866>, Verny et al. PLoS Comp. Bio. 2017 <doi:10.1371/journal.pcbi.1005662>.
| Version: | 1.5.3 |
| Imports: | ppcor, Rcpp, scales, stats |
| LinkingTo: | Rcpp |
| Suggests: | igraph, grDevices, ggplot2 (≥ 3.3.0), gridExtra |
| Published: | 2020-10-13 |
| Author: | Vincent Cabeli [aut, cre], Honghao Li [aut], Marcel Ribeiro Dantas [aut], Nadir Sella [aut], Louis Verny [aut], Severine Affeldt [aut], Hervé Isambert [aut] |
| Maintainer: | Vincent Cabeli <vincent.cabeli at curie.fr> |
| BugReports: | https://github.com/miicTeam/miic_R_package/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/miicTeam/miic_R_package |
| NeedsCompilation: | yes |
| SystemRequirements: | C++14 |
| CRAN checks: | miic results |
| Reference manual: | miic.pdf |
| Package source: | miic_1.5.3.tar.gz |
| Windows binaries: | r-devel: miic_1.5.3.zip, r-devel-UCRT: miic_1.5.3.zip, r-release: miic_1.5.3.zip, r-oldrel: miic_1.5.3.zip |
| macOS binaries: | r-release (arm64): miic_1.5.3.tgz, r-release (x86_64): miic_1.5.3.tgz, r-oldrel: miic_1.5.3.tgz |
| Old sources: | miic archive |
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