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.