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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

1
Department of Systems Biology, Columbia University, New York, NY, USA.
2
Department of Biological Sciences, Columbia University, New York, NY, USA.
3
Department of Bioengineering, Stanford University, Stanford, CA, USA.
4
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.

Abstract

Motivation:

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.

Results:

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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