Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.
|Author||Daniele Ramazzotti [cre, aut] (<https://orcid.org/0000-0002-6087-2666>), Bo Wang [aut], Luca De Sano [aut] (<https://orcid.org/0000-0002-9618-3774>), Serafim Batzoglou [ctb]|
|Bioconductor views||Clustering GeneExpression ImmunoOncology Sequencing SingleCell|
|Maintainer||Luca De Sano <email@example.com>|
|Package repository||View on Bioconductor|
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