Inferring metabolic networks from untargeted high-resolution mass spectrometry data


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Documentation for package ‘MetNet’ version 1.0.1

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MetNet-package Inferring metabolic networks from untargeted high-resolution mass spectrometry data
addToList Add adjacency matrix to list
aracne Create an adjacency matrix based on algorithm for the reconstruction of accurate cellular networks
bayes Create an adjacency matrix based on constraint-based structure learning algorithm
clr Create an adjacency matrix based on context likelihood or relatedness network
combineStructuralStatistical Combine structural and statistical adjacency matrix
consensusAdjacency Create a consensus adjacency matrix of statistical adjacency matrices
correlation Create an adjacency matrix based on correlation
createStatisticalAdjacency Create statistical adjacency matrix
createStatisticalAdjacencyList Create a list of statistical adjacency matrices
createStructuralAdjacency Create adjacency matrix based on m/z (molecular weight) difference
lasso Create a adjacency matrix based on LASSO
mat_test Example data for 'MetNet': unit tests
mat_test_z Example data for 'MetNet': unit tests
MetNet Inferring metabolic networks from untargeted high-resolution mass spectrometry data
peaklist Example data for 'MetNet': data input
randomForest Create a adjacency matrix based on random forest
rtCorrection Correct connections in the structural adjacency matrix by retention time
threeDotsCall Check if passed arguments match the function's formal arguments and call the function with the checked arguments
x_test Example data for 'MetNet': data input