cosmicCancer_stateOrder

<p>The dataset contains 807 samples without predisposing Brca1/2 germline mutations
and includes 204 somatic mutations (from whole exome sequencing) and expression
 level information for 91 genes, cathegory order file.</p>

datasets

data

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: Verny et al. Plos Comput Biol. (2017) <doi:10.1371/journal.pcbi.1005662>.

Nadir Sella

miic

Multivariate Information Inductive Causation

Louis Verny

Severine Affeldt

Herv<c3><a9> Isambert

cosmicCancer_stateOrder function

Format

Genomic and ploidy alterations in breast tumors — cosmicCancer_stateOrder

Genomic and ploidy alterations in breast tumors

cosmicCancer_stateOrder: Genomic and ploidy alterations in breast tumors

Description

Usage

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References