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DOI: 10.18129/B9.bioc.SIMLR    

SIMLR: Single-cell Interpretation via Multi-kernel LeaRning

Bioconductor version: Release (3.5)

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.

Author: Bo Wang [aut], Daniele Ramazzotti [aut, cre], Luca De Sano [aut], Junjie Zhu [ctb], Emma Pierson [ctb], Serafim Batzoglou [ctb]

Maintainer: Daniele Ramazzotti <daniele.ramazzotti at>

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biocViews Clustering, GeneExpression, Sequencing, SingleCell, Software
Version 1.2.3
In Bioconductor since BioC 3.4 (R-3.3) (1 year)
License file LICENSE
Depends R (>= 3.4)
Imports parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra
LinkingTo Rcpp
Suggests BiocGenerics, BiocStyle, testthat, knitr, igraph
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