We present single-cell interpretation via multikernel learning (simLr), an analytic framework and software which learns a similarity measure from single-cell rna-seq data in order to perform dimension reduction, clustering and visualization. on seven published data sets, we benchmark simLr against state-of-the-art methods. We show that simLr is scalable and greatly enhances clustering performance while improving the visualization and interpretability of single-cell sequencing data.

Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., Batzoglou, S. (2017). Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning. NATURE METHODS, 14(4), 414-416 [10.1038/nMeth.4207].

Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning

Ramazzotti D.
Penultimo
;
2017

Abstract

We present single-cell interpretation via multikernel learning (simLr), an analytic framework and software which learns a similarity measure from single-cell rna-seq data in order to perform dimension reduction, clustering and visualization. on seven published data sets, we benchmark simLr against state-of-the-art methods. We show that simLr is scalable and greatly enhances clustering performance while improving the visualization and interpretability of single-cell sequencing data.
Articolo in rivista - Articolo scientifico
Visualization, Single-cell, scRNA-seq
English
2017
14
4
414
416
none
Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., Batzoglou, S. (2017). Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning. NATURE METHODS, 14(4), 414-416 [10.1038/nMeth.4207].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/285178
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