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
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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.
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