Rapid Reconstruction of Time-Varying Gene Regulatory Networks


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Documentation for package ‘TGS’ version 1.0.0

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adjmxToSif Create a .sif file from given adjacency matrix
calcPerfDiNet Calculating performance metrics of the directed net 'inferredNet' w.r.t. the directed net 'targetNet'.
checkUnrolledDbn Checks whether the given unrolled DBN follows 1st Markov order or not
CompareNet Checks if 'di.net.adj.matrix' = 'cmi.net.adj.matrix'
computeCmi Compute Conditional Mutual Infortion (CMI)
ComputeCmiPcaCmi Compute Conditional Mutual Information (CMI) the way it is done in the implementation of the PCA-CMI algo
ComputEntropy Compute Entropy matrix from the input data
ConvertDinetToUndinet Given a directed network, convert it into an undirected network
CountFeedFwdEdgesUndi Count the number of feed-forward edges in a given undirected network.
discretizeData.2L.Tesla Discretize input data into 2 levels.
discretizeData.2L.wt.l Discretizes input data into two levels.
discretizeData.2L.wt.le Discretizes input data into two levels.
discretizeData.3L.wt Discretizes input data into three levels, given a tolerance.
discretizeData.5L.wt Discretizes input data into five levels.
eval.wrt.known.gene.ias Accuracy of predicted directed gene reuglatory network adjacency matrix
GenTrueAdjMatrix Generates True net adjacency matrix and save as an R object
LearnClr2NetMfi Learns CLR2 network
LearnClr3NetMfi Learn CLR3 network
LearnClrNetFromDiscrData Learns CLR network from a given discretized dataset.
LearnClrNetMfi Learns CLR network
LearnClrNetMfiVer2.1 Learn CLR2.1 network
learnCmiNetStruct Learns the CMI structure
learnDbnStructLayer3dParDeg1 Unrolled DBN structure learning with Markov Order 0 and 1.
LearnDbnStructMo1Clr3Ser Learns DBN structure of Markov order 1 where candidate parents are selected using the CLR3 algo.
learnDbnStructMo1Layer3dParDeg1 Goal: Unrolled DBN structure learning with Markov Order 1.
learnDbnStructMo1Layer3dParDeg1_v2 Goal: Unrolled DBN structure learning with Markov Order 1.
LearnMiNetStructClr Learns the CLR network Replaces all non-zero edge weights with 1.
LearnMiNetStructRowMedian Learn the mi network structure
LearnMiNetStructZstat Learn the mi network structure
LearnTgs Implementing the TGS Algorithm.
Print.common.di.edges Given two di network adjacency matrices, it prints the common edges in an output file
reachable.nodes Returns all the nodes reachable from the given node in the directed adjacency matrix
rollDbn Convert a given unrolled Dynamic Bayesian Network (DBN) into a rolled DBN using different rolling methods Rolls time-varying networks into a single time-invariant network. This function is compatible with the time-varying networks learnt through learnDbnStruct3dParDeg1.R::learnDbnStructMo1Layer3dParDeg1().
rollDbn_v2 Convert a given unrolled Dynamic Bayesian Network (DBN) into a rolled DBN using different rolling methods Rolls time-varying networks into a single time-invariant network. This function is compatible with the time-varying networks learnt through learnDbnStruct3dParDeg1.R::learnDbnStructMo1Layer3dParDeg1_v2().