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type: software
title: 'SIMLR: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)'
version: 1.31.0
abstract: 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:
- family-names: Ramazzotti
  given-names: Daniele
  email: daniele.ramazzotti@unimib.it
  orcid: https://orcid.org/0000-0002-6087-2666
- family-names: Wang
  given-names: Bo
  email: wangbo.yunze@gmail.com
- family-names: De Sano
  given-names: Luca
  email: luca.desano@gmail.com
  orcid: https://orcid.org/0000-0002-9618-3774
repository: https://bioc.r-universe.dev
repository-code: https://github.com/BatzoglouLabSU/SIMLR
url: https://github.com/BatzoglouLabSU/SIMLR
date-released: '2024-03-21'
contact:
- family-names: De Sano
  given-names: Luca
  email: luca.desano@gmail.com
  orcid: https://orcid.org/0000-0002-9618-3774