iterClust: a statistical framework for iterative clustering analysis
Bioinformatics, 2018•academic.oup.com
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 (eg A and B) in starting iterations, followed by relatively subtle
differences (eg B1 and B2), providing a comprehensive clustering trajectory. Availability and
implementation iterClust is implemented as a Bioconductor R package. Supplementary …
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 (eg A and B) in starting iterations, followed by relatively subtle
differences (eg B1 and B2), providing a comprehensive clustering trajectory. Availability and
implementation iterClust is implemented as a Bioconductor R package. Supplementary …
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.
Oxford University Press