Description: 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>.
person("Vincent", "Cabeli", role = c("aut", "cre"), email="vincent.cabeli@curie.fr"),
person("Honghao", "Li", role = "aut", email="honghao.li@curie.fr"),
person("Marcel", "Ribeiro Dantas", role = "aut", email = "marcel.ribeiro-dantas@curie.fr"),
person("Nadir", "Sella", role = "aut", email = "nadir.sella@curie.fr"),
Imports: MASS, igraph, ppcor, scales, stats, Rcpp

Imports: igraph, ppcor, scales, stats, Rcpp

Description: 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.
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>.
Suggests: igraph, grDevices, ggplot2 (>= 3.3.0), gridExtra