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Bioinformatics. 2018 Aug 15;34(16):2865-2866. doi: 10.1093/bioinformatics/bty176.

iterClust: a statistical framework for iterative clustering analysis.

Author information

Department of Systems Biology, Columbia University, New York, NY, USA.
Department of Biological Sciences, Columbia University, New York, NY, USA.
Department of Bioengineering, Stanford University, Stanford, CA, USA.
Herbert Irving Comprehensive Cancer Center, J.P. Sulzberger Columbia Genome Center, Department of Biomedical Informatics, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.



In a scenario where populations A, B1 and B2 (subpopulations of B) exist, pronounced differences between A and B may mask subtle differences between B1 and B2.


Here we present iterClust, an iterative clustering framework, which can separate more pronounced differences (e.g. A and B) in starting iterations, followed by relatively subtle differences (e.g. B1 and B2), providing a comprehensive clustering trajectory.

Availability and implementation:

iterClust is implemented as a Bioconductor R package.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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