Package: SIMLR 1.33.0

SIMLR: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)

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.

Authors:Daniele Ramazzotti [aut], Bo Wang [aut], Luca De Sano [cre, aut], Serafim Batzoglou [ctb]