Description Details Author(s) References
Causal network analysis methods for regulator prediction and network reconstruction from genome scale data.
The most important functions are:
CreateCCG: read a computational causal graph from a .sif file
ReadExperimentalData: read a experimental data from a .txt file
MakePredictions: make causal reasoning predictions from a CCG
ScoreHypothesis: score causal reasoning predictions
CalculateSignificance: calculate statisitical significance of a result
RankTheHypotheses: compare different possible regulatory hypotheses on a single CCG
runSCANR: reduce false positives by selecting common hypotheses across pathlengths
WriteExplainedNodesToSifFile: reconstruct hypothesis specific regulatory network
Glyn Bradley, Steven J. Barrett, Chirag Mistry, Mark Pipe, David Riley, David Wille, Bhushan Bonde, Peter Woollard
"CausalR - extracting mechanistic sense from genome scale data", Bradley, G. and Barrett, S.J., Application note, Bioinformatics (submitted)
"Causal reasoning on biological networks: interpreting transcriptional changes", Chindelevitch et al., Bioinformatics 28 1114 (2012). doi:10.1093/bioinformatics/bts090
"Assessing statistical significance in causal graphs", Chindelevitch et al., BMC Bioinformatics 13 35 (2012). doi:10.1186/1471-2105-13-35
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