Package: Patterns 1.7

Patterns: Deciphering Biological Networks with Patterned Heterogeneous Measurements

A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.

Authors:Frederic Bertrand [cre, aut], Myriam Maumy-Bertrand [aut]

Patterns_1.7.tar.gz
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Patterns_1.7.tgz(r-4.6-any)Patterns_1.7.tgz(r-4.5-any)
Patterns_1.7.tar.gz(r-4.7-any)Patterns_1.7.tar.gz(r-4.6-any)
Patterns_1.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
Patterns/json (API)
NEWS

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

Bug tracker:https://github.com/fbertran/patterns/issues

Pkgdown/docs site:https://fbertran.github.io

Datasets:
  • CLL - Expression data from healthy and malignant
  • doc - Human transcription factors from HumanTFDB
  • infos - Details on some probesets of the affy_hg_u133_plus_2 platform.
  • jetsetscores - JetsetscoresHuman transcription factors from HumanTFDB
  • M - Simulated microarray.
  • Net - Simulated network for examples.
  • Net_inf_PL - Reverse-engineered network of the M and Net simulated data.
  • network - A example of an inferred network (4 groups case).
  • network2gp - A example of an inferred cascade network (2 groups case).
  • networkCascade - A example of an inferred cascade network (4 groups case).
  • Selection - Selection of genes.

On CRAN:

Conda:

6.90 score 4 stars 50 scripts 548 downloads 11 mentions 30 exports 121 dependencies

Last updated from:053e13981f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK342
source / vignettesOK360
linux-release-x86_64OK331
macos-release-arm64OK195
macos-oldrel-arm64OK168
windows-develOK171
windows-releaseOK175
windows-oldrelOK221
wasm-releaseOK201

Exports:analyze_networkas.omics_arrayCascadeFinitCascadeFshapeclustExplorationclustInferencecomparecutoffevolutiongene_expr_simulationgeneNeighborhoodgenePeakSelectiongeneSelectionheadIndicFinitIndicFshapeinferencenetwork_randomplotplotFpositionpredictprobeMergereplaceBandreplaceDownreplaceUpshowunionOmicsunsupervised_clusteringunsupervised_clustering_auto_m_c

Dependencies:abindanimationbackportsbase64encBiobaseBiocGenericsbitopsbslibcachemCascadecaToolscheckmateclasscliclueclustercodetoolscolorspacecpp11curldata.tabledeldirdigestdoParalleldynamicTreeCutDynDoce1071evaluatefarverfastclusterfastmapfontawesomeforeachforeignFormulafsgenericsggplot2glmnetgluegplotsgridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsigraphimputeinterpisobanditeratorsjpegjquerylibjsonliteKernSmoothknitrlabelinglarslatticelatticeExtralifecyclelimmamagickmagrittrMASSMatrixmatrixStatsmemoiseMfuzzmimemovMFmsgpsnnetnnlsnor1mixpkgconfigplotrixplspngpreprocessCoreproxyR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRfastrlangrmarkdownrpartrstudioapiS7sassscalesSelectBoostshapeskmeansslamsplsstatmodstringistringrsurvivaltinytextkWidgetstnetvarbvsvctrsVGAMviridisLiteWGCNAwidgetToolswithrxfunyamlzigg

Introduction to Patterns: a modeling tool dedicated to biological network modeling

Frédéric Bertrand and Myriam Maumy-Bertrand

Rendered fromIntroPatterns.Rmdusingknitr::rmarkdownon May 13 2026.

Last update: 2025-09-15
Started: 2019-05-03

Network inference and analysis of CLL data

Frédéric Bertrand and Myriam Maumy-Bertrand

Rendered fromExampleCLL.Rmdusingknitr::rmarkdownon May 13 2026.

Last update: 2025-09-15
Started: 2019-05-03

Readme and manuals

Help Manual

Help pageTopics
Analysing the networkanalyze_network analyze_network,omics_network-method analyze_network-methods
Coerce a matrix into a omics_array object.as.omics_array
Create initial F matrices for cascade networks inference.CascadeFinit
Create F matrices shaped for cascade networks inference.CascadeFshape
Expression data from healthy and malignant (chronic lymphocytic leukemia, CLL) human B-lymphocytes after B-cell receptor stimulation (GSE 39411 dataset)CLL
A function to explore a dataset and cluster its rows.clustExploration clustExploration,omics_array-method clustExploration-methods
A function to explore a dataset and cluster its rows.clustInference clustInference,omics_array,numeric-method clustInference-methods
Some basic criteria of comparison between actual and inferred network.compare compare,omics_network,omics_network,numeric-method compare-methods
Choose the best cutoffcutoff cutoff,omics_network-method cutoff-methods
Dimension of the datadim dim,omics_array-method dim-methods
Human transcription factors from HumanTFDBdoc
See the evolution of the network with change of cutoffevolution evolution,omics_network-method evolution-methods
Simulates omicsarray data based on a given network.gene_expr_simulation gene_expr_simulation,omics_network-method gene_expr_simulation-methods
Find the neighborhood of a set of nodes.geneNeighborhood geneNeighborhood,omics_network-method geneNeighborhood-methods
Methods for selecting genesgenePeakSelection genePeakSelection,omics_array,numeric-method genePeakSelection-methods geneSelection geneSelection,list,list,numeric-method geneSelection,omics_array,numeric-method geneSelection,omics_array,omics_array,numeric-method geneSelection-methods
Overview of a omics_array objecthead,ANY-method head,omics_array-method head-methods methods
Create initial F matrices using specific intergroup actions for network inference.IndicFinit
Create F matrices using specific intergroup actions for network inference.IndicFshape
Reverse-engineer the networkinference inference,omics_array-method inference-methods
Details on some probesets of the affy_hg_u133_plus_2 platform.infos
jetsetscoresHuman transcription factors from HumanTFDBjetsetscores
Simulated microarray.M
Simulated network for examples.Net
Reverse-engineered network of the M and Net simulated data.Net_inf_PL
A example of an inferred network (4 groups case).network
Generates a network.network_random
A example of an inferred cascade network (2 groups case).network2gp
A example of an inferred cascade network (4 groups case).networkCascade
Class '"omics_array"'omics_array-class
Class '"omics_network"'omics_network-class
Class '"omics_predict"'omics_predict-class
Plotplot,omics_array,ANY-method plot,omics_network,ANY-method plot,omics_predict,ANY-method plot-methods
Plot functions for the F matrices.plotF
Returns the position of edges in the networkposition position,omics_network-method position-methods
Methods for Function 'predict'predict predict,ANY-method predict,omics_array-method predict-methods
Function to merge probesetsprobeMerge probeMerge,omics_array-method
Replace matrix values by band.replaceBand
Replace matrix values triangular lower part and by band for the upper part.replaceDown
Replace matrix values triangular upper part and by band for the lower part.replaceUp
Selection of genes.Selection
'Show' methodsshow,ANY-method show,omics_array-method show,omics_network-method show-methods
'Summary' methodssummary,ANY-method summary,omics_array-method summary-methods
Makes the union between two omics_array objects.unionOmics unionOmics,list,ANY-method unionOmics,omics_array,omics_array-method unionOmics-methods
Cluster a omics_array object: determine optimal fuzzification parameter and number of clusters.unsupervised_clustering_auto_m_c unsupervised_clustering_auto_m_c,omics_array-method
Cluster a omics_array object: performs the clustering.unsupervised_clustering unsupervised_clustering,omics_array,numeric,numeric-method