Related Experiment Video
Updated: Mar 6, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
Published on: May 16, 2020
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning
Bo Wang1, Junjie Zhu2, Emma Pierson1
1Department of Computer Science, Stanford University, Stanford, California, USA.
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.
Related Concept Videos
RNA-seq
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
Evolutionary Relationships through Genome Comparisons

