Apply Partial Correlation coefficient with Information Theory (PCIT) to a correlation matrix. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network. The algorithm can be applied to any correlation-based network including but not limited to gene co-expression networks. To reduce compute time by making use of multiple compute cores, simply run PCIT on a computer with has multiple cores and also has the Rmpi package installed. PCIT will then auto-detect the multicore environment and run in parallel mode without the need to rewrite your scripts. This makes scripts, using PCIT, portable across single core (or no Rmpi package installed) computers which will run in serial mode and multicore (with Rmpi package installed) computers which will run in parallel mode.
|Author||Nathan S. Watson-Haigh|
|Date of publication||2015-02-16 17:39:23|
|Maintainer||Nathan S. Watson-Haigh <firstname.lastname@example.org>|
clusteringCoefficient: Calculate the clustering coefficient
clusteringCoefficientPercent: Calculate the clustering coefficient as a percentage
data: Demo Data
defineTasks: Define a list of tasks for slave CPUs
getEdgeList: Converts an adjacency matrix into edge list representation
idx: Get indicies for significant edges
idxInvert: Invert linear indices from a matrix
localClusteringCoefficient: Calculate the local clustering coefficient
maxMatrixSize: Calculate the maximum correlation matrix size which PCIT can...
pcit: Apply the PCIT algorithm
pcitMemoryRequirements: Calculate the memory requirement for running PCIT
PCIT-package: Partial Correlation coefficient with Information Theory...
plotCorCoeff: Plot superimposed histograms of correlation coefficients