Determination of essential phenotypic elements of clusters in high-dimensional entities-DEPECHE.

ABSTRACT: Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make such highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better than the currently available best performing clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to

SUBMITTER: Theorell A 

PROVIDER: S-EPMC6405191 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature