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.

Package details

AuthorDaniele Ramazzotti [aut, cre], Bo Wang [aut], Luca De Sano [aut], Serafim Batzoglou [ctb]
Bioconductor views Clustering GeneExpression ImmunoOncology Sequencing SingleCell
MaintainerLuca De Sano <[email protected]>
Licensefile LICENSE
Version1.8.1
URL https://github.com/BatzoglouLabSU/SIMLR
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("SIMLR")

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SIMLR documentation built on Jan. 5, 2019, 6:56 p.m.