title: Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions.
abstract: BACKGROUND
  Biclustering is a popular method for identifying under which experimental conditions biological signatures are co-expressed. However, the general biclustering problem is NP-hard, offering room to focus algorithms on specific biological tasks. We hypothesize that conditional co-regulation of genes is a key factor in determining cell phenotype and that accurately segregating conditions in biclusters will improve such predictions. Thus, we developed a bicluster sampled coherence metric (BSCM) for determining which conditions and signals should be included in a bicluster.
  
  
  RESULTS
  Our BSCM calculates condition and cluster size specific p-values, and we incorporated these into the popular integrated biclustering algorithm cMonkey. We demonstrate that incorporation of our new algorithm significantly improves bicluster co-regulation scores (p-value = 0.009) and GO annotation scores (p-value = 0.004). Additionally, we used a bicluster based signal to predict whether a given experimental condition will result in yeast peroxisome induction. Using the new algorithm, the classifier accuracy improves from 41.9% to 76.1% correct.
  
  
  CONCLUSIONS
  We demonstrate that the proposed BSCM helps determine which signals ought to be co-clustered, resulting in more accurately assigned bicluster membership. Furthermore, we show that BSCM can be extended to more accurately detect under which experimental conditions the genes are co-clustered. Features derived from this more accurate analysis of conditional regulation results in a dramatic improvement in the ability to predict a cellular phenotype in yeast. The latest cMonkey is available for download at https://github.com/baliga-lab/cmonkey2. The experimental data and source code featured in this paper is available http://AitchisonLab.com/BSCM. BSCM has been incorporated in the official cMonkey release.
authors.forename: Samuel A
authors.initials: SA
authors.lastname: Danziger
authors.forename: David J
authors.initials: DJ
authors.lastname: Reiss
authors.forename: Alexander V
authors.initials: AV
authors.lastname: Ratushny
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Smith
authors.forename: Christopher L
authors.initials: CL
authors.lastname: Plaisier
authors.forename: John D
authors.initials: JD
authors.lastname: Aitchison
authors.forename: Nitin S
authors.initials: NS
authors.lastname: Baliga
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D005786:Gene Expression Regulation; D010641:Phenotype; D011019:Pneumonia, Mycoplasma / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012984:Software; D049490:Systems Biology / Q000379:methods*; D059467:Transcriptome