SIMLR: Title: SIMLR: Single-cell Interpretation via Multi-kernel LeaRning
Version 1.4.0

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 to 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. SIMLR is capable of separating known subpopulations more accurately in single-cell data sets than do existing dimension reduction methods. Additionally, SIMLR demonstrates high sensitivity and accuracy on high-throughput peripheral blood mononuclear cells (PBMC) data sets generated by the GemCode single-cell technology from 10x Genomics.

Package details

AuthorBo Wang [aut], Daniele Ramazzotti [aut, cre], Luca De Sano [aut], Junjie Zhu [ctb], Emma Pierson [ctb], Serafim Batzoglou [ctb]
Bioconductor views Clustering GeneExpression Sequencing SingleCell
MaintainerDaniele Ramazzotti <[email protected]>
Licensefile LICENSE
Version1.4.0
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 Nov. 17, 2017, 9:28 a.m.