Bi-CoPaM ensemble clustering application to five Escherichia coli bacterial datasets

Basel Abu-Jamous, Rui Fa, David J. Roberts, Asoke K. Nandi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Bi-CoPaM ensemble clustering has the ability to mine a set of microarray datasets collectively to identify the subsets of genes consistently co-expressed in all of them. It also has the capability of considering the entire gene set without pre-filtering as it implicitly filters out less interesting genes. While it showed success in revealing new insights into the biology of yeast, it has never been applied to bacteria. In this study, we apply Bi-CoPaM to five bacterial datasets, identifying two clusters of genes as the most consistently co-expressed. Strikingly, their average profiles are consistently negatively correlated in most of the datasets. Thus, we hypothesise that they are regulated by a common biological machinery, and that their genes with unknown biological processes may be participating in the same processes in which most of their genes known to participate. Additionally, our results demonstrate the applicability of Bi-CoPaM to a wide range of species.

Original languageEnglish
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
ISBN (Electronic)9780992862619
Publication statusPublished - 10 Nov 2014
Externally publishedYes
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal
Duration: 1 Sept 20145 Sept 2014

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference22nd European Signal Processing Conference, EUSIPCO 2014

Bibliographical note

Publisher Copyright:
© 2014 EURASIP.


  • Bi-CoPaM
  • Escherichia coli bacteria
  • gene clustering
  • microarray data analysis


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