Package: diffuStats 1.25.0

Sergio Picart-Armada

diffuStats: Diffusion scores on biological networks

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.

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

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diffuStats.pdf |diffuStats.html✨
diffuStats/json (API)
NEWS

# Install 'diffuStats' in R:
install.packages('diffuStats', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On BioConductor:diffuStats-1.25.0(bioc 3.20)diffuStats-1.24.0(bioc 3.19)

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

bioconductor-package

24 exports 0.71 score 41 dependencies 3 mentions

Last updated 2 months agofrom:d3fbdfcf49

Exports:commuteTimeKerneldiffusediffuse_griddiffuse_mcdiffuse_rawdiffusionKernelgenerate_graphgenerate_inputget_covarget_muget_mu_referenceget_var_referenceinverseCosineKernelis_kernellargest_ccmetric_aucmetric_funperfperf_evalperf_wilcoxpStepKernelregularisedLaplacianKernelscores2coloursscores2shapes

Dependencies:assertthatbackportscheckmateclicolorspacecpp11data.tableexpmfansifarverggplot2gluegridExtragtableigraphisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrprecrecR6RColorBrewerRcppRcppArmadilloRcppParallelrlangscalestibbleutf8vctrsviridisLitewithr

Case study: predicting protein function

Rendered fromdiffuStats.Rnwusingutils::Sweaveon Jun 17 2024.

Last update: 2019-03-28
Started: 2017-06-27

Diffusion using diffuStats in a nutshell

Sergio Picart-Armada, Alexandre Perera-Lluna

Rendered fromintro.Rmdusingknitr::rmarkdownon Jun 17 2024.

Last update: 2017-07-13
Started: 2017-02-28

Readme and manuals

Help Manual

Help pageTopics
Sanity checks for input.available_methods .check_graph .check_K .check_method .check_metric .check_scores
Function to connect a non connected graph.connect_undirected_graph
Generate data.frame with default vertex attributes.default_graph_param .default_prop
S4 sparse matrix to arma::sp_matconvertSparse
Diffuse scores on a networkdiffuse diffuse_grid
Compute the heatrank using permutationsdiffuse_mc
Diffuse scores on a networkdiffuse_raw
diffuStats: an R package to compute and benchmark diffusion scoresdiffuStats
Generate a random graphgenerate_graph
Generate a random input for graph diffusiongenerate_input
Toy graph to play with diffusiongraph_toy
Check if a matrix is a valid kernelis_kernel
Compute graph kernelscommuteTimeKernel diffusionKernel inverseCosineKernel kernels pStepKernel regularisedLaplacianKernel
Largest connected componentlargest_cc
Compute the area under the curves (ROC, PRC)metric_auc metric_fun
Compute exact statistical momentsget_covar get_mu get_mu_reference get_var_reference moments
Create a named listnamed.list
Compute heatrank in parallelParallelHeatrank
Compare diffusions to a target score on a grid of parametersperf
Compute performance of diffusion scores on a single caseperf_eval
Compute column-wise statistics in a performance matrixperf_wilcox
Translate values into coloursscores2colours
Translate values into shapesscores2shapes
Compute heatrank for a single caseserialHeatrank
Sparsify arma::mat into arma::sp_matsparsify2
Convert input to list formatto_list
Convert list format to desired formatto_x_from_list
In which format is the input?which_format