Package: SIMLR 1.31.0

Luca De Sano

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]

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SIMLR.pdf |SIMLR.html✨
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Bug tracker:https://github.com/batzogloulabsu/simlr/issues

On BioConductor:SIMLR-1.31.0(bioc 3.20)SIMLR-1.30.0(bioc 3.19)

bioconductor-package

4 exports 1.38 score 7 dependencies 1 dependents 40 mentions

Last updated 2 months agofrom:8cf2039c1f

Exports:SIMLRSIMLR_Estimate_Number_of_ClustersSIMLR_Feature_RankingSIMLR_Large_Scale

Dependencies:latticeMatrixpracmaRcppRcppAnnoyRcppEigenRSpectra