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CausalR: extracting mechanistic sense from genome scale data.

ABSTRACT:

Summary

Utilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network and annotation files created for visualization in Cytoscape. False positives are limited using the introduced Sequential Causal Analysis of Networks approach.

Availability and implementation

CausalR is implemented in R, parallelized, and is available from Bioconductor.

Contact

glyn.x.bradley@gsk.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Bradley G

PROVIDER: S-EPMC5870775 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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CausalR: extracting mechanistic sense from genome scale data.

Bradley Glyn G Barrett Steven J SJ

Bioinformatics (Oxford, England) 20171101 22

<h4>Summary</h4>Utilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network and annotation files created for visualization in Cytoscape. False positives are limited using the introduced Sequential Causal Analysis of Networks approach.<h4>Availability and implementatio ...[more]

PMID: 28666369

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