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Diffusion scores on biological networks

Bioconductor version: Release (3.19)

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>

Citation (from within R, enter citation("diffuStats")):


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Case study: predicting protein function PDF R Script
Quick start HTML R Script
Reference Manual PDF


biocViews GeneExpression, Genetics, GenomeWideAssociation, GraphAndNetwork, Metabolomics, Network, Normalization, Proteomics, Software, Transcriptomics
Version 1.24.0
In Bioconductor since BioC 3.6 (R-3.4) (6.5 years)
License GPL-3
Depends R (>= 3.4)
Imports grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec
System Requirements GNU make
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Suggests testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils
Linking To Rcpp, RcppArmadillo, RcppParallel
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Source Package diffuStats_1.24.0.tar.gz
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