getIgraph

[a miic graph object]
The graph object returned by the miic execution.

miic.res

This functions returns an igraph object built from the result
returned by <code>miic</code>.

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>.

Vincent Cabeli

miic

Learning Causal or Non-Causal Graphical Models Using Information
Theory

Honghao Li

Marcel Ribeiro Dantas

Nadir Sella

Louis Verny

Severine Affeldt

Hervé Isambert

getIgraph function

<dl><dt>miic.res</dt>
<dd>[a miic graph object]
The graph object returned by the miic execution.</dd></dl>

Arguments

Igraph plotting function for miic — getIgraph

<dl>

<dt>miic.res</dt>
<dd>[a miic graph object]
The graph object returned by the miic execution.</dd>

</dl>

getIgraph: Igraph plotting function for miic

Description

Usage

Value

Arguments

Details

See Also