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>.
c(
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"),
person("Louis", "Verny",role = "aut"),
person("Severine", "Affeldt", role = "aut"),
person("Hervé", "Isambert", role = c("aut"), email = "Herve.Isambert@curie.fr"))
MASS, igraph, ppcor, scales, stats, Rcpp
Vincent Cabeli [aut, cre],
Honghao Li [aut],
Marcel Ribeiro Dantas [aut],
Nadir Sella [aut],
Louis Verny [aut],
Severine Affeldt [aut],
Hervé Isambert [aut]

c(person(given = "Vincent",
family = "Cabeli",
role = c("aut", "cre"),
email = "vincent.cabeli@curie.fr"),
person(given = "Honghao",
family = "Li",
role = "aut",
email = "honghao.li@curie.fr"),
person(given = "Marcel",
family = "Ribeiro Dantas",
role = "aut",
email = "marcel.ribeiro-dantas@curie.fr"),
person(given = "Nadir",
family = "Sella",
role = "aut",
email = "nadir.sella@curie.fr"),
person(given = "Louis",
family = "Verny",
role = "aut"),
person(given = "Severine",
family = "Affeldt",
role = "aut"),
person(given = "Hervé",
family = "Isambert",
role = "aut",
email = "Herve.Isambert@curie.fr"))
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>.
igraph, grDevices, ggplot2 (>= 3.3.0), gridExtra