miic: Learning Causal or Non-Causal Graphical Models Using Information
Theory
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 |
Documentation:
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