DOI: 10.18129/B9.bioc.diffuStats  

This package is for version 3.17 of Bioconductor; for the stable, up-to-date release version, see diffuStats.

Diffusion scores on biological networks

Bioconductor version: 3.17

Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking.

Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut]

Maintainer: Sergio Picart-Armada <sergi.picart at upc.edu>

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biocViews GeneExpression, Genetics, GenomeWideAssociation, GraphAndNetwork, Metabolomics, Network, Normalization, Proteomics, Software, Transcriptomics
Version 1.20.0
In Bioconductor since BioC 3.6 (R-3.4) (6 years)
License GPL-3
Depends R (>= 3.4)
Imports grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec
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Suggests testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils
SystemRequirements GNU make
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