Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Our next meeting will be at 3:00 on June 23th, in room 4160 of the Discovery building. Our Selected paper is Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning.
The abstract is as follows.
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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