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Package: causalCCC
Title: Learning Causal intracellular causal pathways enabling cell-cell communication
Version: 2.0.0
Authors@R:
c(person(given = "Louise",
family = "Dupuis",
role = "aut",
email = "louise.dupuis@curie.fr"),
person(given = "Orianne",
family = "Debeaupuis",
role = "aut",
email = "orianne.debeaupuis@curie.fr"),
person(given = "Franck",
family = "Simon",
role = c("aut", "cre"),
email = "franck.simon@curie.fr"),
person(given = "Hervé",
family = "Isambert",
role = "aut",
email = "herve.isambert@curie.fr"))
Description: This repository contains the source code for CausalCCC, a method based on MIIC
(Multivariate Information based Inductive Causation) that learns a large class of causal or
non-causal graphical models from purely observational data while including the effects of
unobserved latent variables. Cell-Cell Communication (CCC) methods recover Ligand-Receptor
interactions between single cell populations, but do not usually integrate intracellular causal
pathways upstream and downstream of these L-R interactions. CausalCCC fills this gap by
providing an integrative online server to reconstruct gene-gene interaction pathways across
interacting cell types from single-cell transcriptomic data.
For more information, you can refer to:
Simon et al. eLife, reviewed preprint <doi:10.1101/2024.02.06.579177>,
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>.
License: GPL (>= 2)
URL: https://github.com/miicTeam/CausalCCC
BugReports: https://github.com/miicTeam/CausalCCC/issues
Remotes: github::miicTeam/miic_R_package@causalccc
Imports:
ppcor,
Rcpp,
scales,
stats,
miic,
Suggests:
Seurat,
liana,
igraph,
grDevices,
ggplot2 (>= 3.3.0),
gridExtra
LinkingTo:
Rcpp
SystemRequirements: C++14
LazyData: true
Encoding: UTF-8
RoxygenNote: 7.3.1