Package: runibic 1.29.0

Patryk Orzechowski

runibic: runibic: row-based biclustering algorithm for analysis of gene expression data in R

This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data.

Authors:Patryk Orzechowski, Artur Pańszczyk

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NEWS

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

Bug tracker:https://github.com/athril/runibic/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On BioConductor:runibic-1.29.0(bioc 3.21)runibic-1.28.0(bioc 3.20)

microarrayclusteringgeneexpressionsequencingcoveragecppopenmp

5.20 score 4 stars 7 scripts 290 downloads 2 mentions 10 exports 81 dependencies

Last updated 5 months agofrom:c55baab949. Checks:1 OK, 5 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 27 2025
R-4.5-win-x86_64NOTEMar 27 2025
R-4.5-linux-x86_64NOTEMar 27 2025
R-4.4-win-x86_64NOTEMar 27 2025
R-4.4-linux-x86_64NOTEMar 27 2025
R-4.3-win-x86_64NOTEMar 27 2025

Exports:backtrackLCSBCUnibicBCUnibicDcalculateLCSclusterpairwiseLCSrunibicruniDiscretizeset_runibic_paramsunisort

Dependencies:abindadditivityTestsaskpassbiclustBiobaseBiocGenericsbriocallrclassclicolorspacecpp11crayoncurlDelayedArraydescdiffobjdigestdplyrevaluatefansifarverflexclustfsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehttrIRangesisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixMatrixGenericsmatrixStatsmgcvmimemodeltoolsmunsellnlmeopensslpillarpkgbuildpkgconfigpkgloadpraiseprocessxpspurrrR6RColorBrewerRcpprlangrprojrootS4ArraysS4VectorsscalesSparseArraystringistringrSummarizedExperimentsystestthattibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewaldowithrXVector

runibic: UniBic in R Tutorial

Patryk Orzechowski, Artur Pańszczyk

Rendered fromrunibic.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2018-09-20
Started: 2017-10-04

Citation

For the sequential and parallel versions of UniBic biclustering algorithm please use the following citations:

Zhenjia Wang, Guojun Li, Robert W. Robinson, Xiuzhen Huang; UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data. Scientific Reports 6, 2016; Article number: 23466 doi: doi:10.1038/srep23466">https://doi:10.1038/srep23466>

Patryk Orzechowski, Artur Pańszczyk, Xiuzhen Huang, Jason H. Moore; runibic: a Bioconductor package for parallel row-based biclustering of gene expression data Bioinformatics, 2018; bty512 doi: https://doi.org/10.1093/bioinformatics/bty512

If you found runibic package useful in your work, please use both or at least the second citation.

Corresponding BibTeX entries: 

  @Article{,
    title = {UniBic: Sequential row-based biclustering algorithm for
      analysis of gene expression data.},
    author = {Zhenjia Wang and Guojun Li and Robert W. Robinson and
      Xiuzhen Huang},
    journal = {Scientific Reports},
    volume = {6},
    year = {2016},
    pages = {23466},
    doi = {doi:10.1038/srep23466},
  }

  @Article{,
    title = {{runibic}: a Bioconductor package for parallel row-based
      biclustering of gene expression data.},
    author = {Patryk Orzechowski and Artur Pańszczyk and Xiuzhen Huang
      and Jason H. Moore},
    journal = {Bioinformatics},
    year = {2018},
    pages = {bty512},
    doi = {https://doi.org/10.1093/bioinformatics/bty512},
  }

Readme and manuals

runibic: UniBic biclustering algorithm for R

This package contains implementation of UniBic biclustering algorithm for gene expression data [Wang2016] The algorithm tries to locate trend-preserving biclusters within complex and noisy data.

Functions

This package provides the following main functions:

  • BCUnibic/runibic - parallel UniBic for continuous data
  • BCUnibicD - parallel UniBic for discrete data

The package provides some additional functions:

  • pairwiseLCS - calculates Longest Common Subsequence (LCS) between two vectors
  • calculateLCS - calculates LCSes between all pairs of the input dataset
  • backtrackLCS - recovers LCS from the dynamic programming matrix
  • cluster - main part of UniBic algorithm (biclusters seeding and expanding)
  • unisort - returns matrix of indexes based on the increasing order in each row
  • discretize - performs discretization using Fibonacci heap (sorting method used originally in UniBic) or standard sorting

Installation

The package may be installed as follows:

install.packages("devtools")
devtools::install_github("athril/runibic")

Example

Gene expression dataset

This example presents how to use runibic package on gene expression dataset:

library(runibic)
library(biclust)
data(BicatYeast)
res <- biclust(method=BCUnibic(),BicatYeast)
drawHeatmap(BicatYeast, res, 1)
parallelCoordinates(BicatYeast,res,1)
Summarized experiment

This example presents how to use runibic package on SummarizedExperiment:

library(runibic)
library(biclust)
library(SummarizedExperiment)
data(airway, package="airway")
se <- airway[1:20,]
res<- runibic(se)
parallelCoordinates(assays(se)[[1]], res[[1]], 2)

Tutorial

Please check runibic tutorial

Citation

For the original sequential version of the UniBic please use the following citation:

Zhenjia Wang, Guojun Li, Robert W. Robinson, Xiuzhen Huang UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data Scientific Reports 6, 2016; 23466, doi: https://doi:10.1038/srep23466

If you use in your work this package with parallel version of UniBic please use the following citation:

Patryk Orzechowski, Artur Pańszczyk, Xiuzhen Huang Jason H. Moore: runibic: a Bioconductor package for parallel row-based biclustering of gene expression data bioRxiv, 2017; 210682, doi: https://doi.org/10.1101/210682

BibTex entry:

@article{orzechowski2018runibic,
  author = {Orzechowski, Patryk and Pańszczyk, Artur and Huang, Xiuzhen and Moore, Jason H},
  title = {runibic: a Bioconductor package for parallel row-based biclustering of gene expression data},
  journal = {Bioinformatics},
  volume = {},
  number = {},
  pages = {bty512},
  year = {2018},
  doi = {10.1093/bioinformatics/bty512},
  URL = {http://dx.doi.org/10.1093/bioinformatics/bty512},
  eprint = {/oup/backfile/content_public/journal/bioinformatics/pap/10.1093_bioinformatics_bty512/4/bty512.pdf}
}

References

  • [Wang2016] Wang, Zhenjia, et al. "UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data." Scientific reports 6 (2016): 23466.