Package: MetNet 1.31.0

Thomas Naake

MetNet: Inferring metabolic networks from untargeted high-resolution mass spectrometry data

MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.

Authors:Thomas Naake [aut, cre], Liesa Salzer [ctb], Elva Maria Novoa-del-Toro [ctb]

MetNet_1.31.0.tar.gz
MetNet_1.31.0.zip(r-4.7-any)MetNet_1.31.0.zip(r-4.6-any)MetNet_1.31.0.zip(r-4.5-any)
MetNet_1.31.0.tgz(r-4.6-any)MetNet_1.31.0.tgz(r-4.5-any)
MetNet_1.31.0.tar.gz(r-4.7-any)MetNet_1.31.0.tar.gz(r-4.6-any)
MetNet_1.31.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MetNet/json (API)

# Install 'MetNet' in R:
install.packages('MetNet', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • mat_test - Example data for 'MetNet': unit tests
  • mat_test_z - Example data for 'MetNet': unit tests
  • peaklist - Example data for 'MetNet': data input
  • sps_sub - Spectra data to test addSpectralSimilarity
  • x_annotation - Example annotation for 'MetNet': data input
  • x_test - Example data for 'MetNet': data input

On BioConductor:MetNet-1.31.0(bioc 3.24)MetNet-1.30.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologymetabolomicsmassspectrometrynetworkregression

4.18 score 3 scripts 656 downloads 15 mentions 21 exports 68 dependencies

Last updated from:a6b1a68bdc. Checks:1 WARNING, 8 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING171
linux-develERROR406
source / vignettesERROR454
linux-releaseERROR430
macos-releaseERROR290
macos-oldrelERROR291
windows-develERROR286
windows-releaseERROR233
windows-oldrelERROR214
wasm-releaseOK134

Exports:addSpectralSimilarityAdjacencyMatrixaracnebayesclrcombinecorrelationdirectedgetLinkslassomz_summarymz_vispartialCorrelationrandomForestrtCorrectionshowstatisticalstructuralthresholdthresholdedtype

Dependencies:abindBHBiobaseBiocGenericsBiocParallelbnlearnclicodetoolscorpcorcpp11DelayedArraydplyrfarverfdrtoolformatRfutile.loggerfutile.optionsGeneNetgenericsGENIE3GenomicRangesggplot2glueGPArotationgtableIRangesisobandlabelinglambda.rlatticelifecyclelongitudinalmagrittrMatrixMatrixGenericsmatrixStatsmnormtnlmeparmigenepillarpkgconfigplyrpsychpurrrR6RColorBrewerRcppreshape2rlangS4ArraysS4VectorsS7scalesSeqinfosnowSparseArraystabsstringistringrSummarizedExperimenttibbletidyrtidyselectutf8vctrsviridisLitewithrXVector

Readme and manuals

Help Manual

Help pageTopics
Inferring metabolic networks from untargeted high-resolution mass spectrometry dataMetNet-package MetNet
Create S4 class AdjacencyMatrix.AdjacencyMatrix
Check if all the assays in the `AdjacencyMatrix` object have identical colnames and rownames.assays_have_identical_colnames_rownames
Check if the assays in the `AdjacencyMatrix` object have identical dimnames.assays_have_identical_dimnames
Adding a spectral similarity matrix to the "structural" `AdjacencyMatrix`addSpectralSimilarity
Add adjacency matrix to listaddToList
Wrapper to create an instance of S4 class AdjacencyMatrixAdjacencyMatrix
Methods for `AdjacencyMatrix` objectsAdjacencyMatrix-class as.data.frame as.data.frame,AdjacencyMatrix-method dim dim,AdjacencyMatrix-method directed directed,AdjacencyMatrix-method length,AdjacencyMatrix-method show show,AdjacencyMatrix-method thresholded thresholded,AdjacencyMatrix-method type type,AdjacencyMatrix-method
Placeholder for generics functions documentationAllGenerics
Create an adjacency matrix based on algorithm for the reconstruction of accurate cellular networksaracne
Create an adjacency matrix based on score-based structure learning algorithmbayes
Create an adjacency matrix based on context likelihood or relatedness networkclr
Combine structural and statistical `AdjacencyMatrix` objectscombine
Create an adjacency matrix based on correlationcorrelation
Write an adjacency matrix to a `data.frame`getLinks
Create an adjacency matrix based on LASSOlasso
Example data for 'MetNet': unit testsmat_test
Example data for 'MetNet': unit testsmat_test_z
Spectra data to test addSpectralSimilarityms2_test sps_sub
Create a summary from adjacency list containing mass differencesmz_summary
Visualize mass difference distributionmz_vis
Calculate the partial correlation and p-valuespartialCorrelation
Example data for 'MetNet': data inputpeaklist
Create an adjacency matrix based on random forestrandomForest
Correct connections in the structural adjacency matrices by retention timertCorrection
Spectra data to test addSpectralSimilarityspectra_matrix
Create an `AdjacencyMatrix` object containing assays of adjacency matrices from statistical methodsstatistical
Create adjacency matrix based on m/z (molecular weight) differencestructural
Threshold the statistical adjacency or spectral similarity matricesthreshold
Return consensus ranks from a matrix containing rankstopKnet
Example annotation for 'MetNet': data inputx_annotation
Example data for 'MetNet': data inputx_test