Package: SIMLR
Version: 1.0.1
Date: 2016-10-20
Title: SIMLR: Single-cell Interpretation via Multi-kernel LeaRning
Authors@R: c(person("Bo", "Wang", role=c("aut"), email="bowang87@stanford.edu"),
             person("Daniele", "Ramazzotti", role=c("aut", "cre"), email="daniele.ramazzotti@yahoo.com"),
             person("Luca", "De Sano", role=c("aut"), email="l.desano@campus.unimib.it"),
             person("Junjie", "Zhu", role=c("ctb")),
             person("Emma", "Pierson", role=c("ctb")),
             person("Serafim", "Batzoglou", role=c("ctb")))
Maintainer: Daniele Ramazzotti <daniele.ramazzotti@yahoo.com>
Depends: R (>= 3.3),
Imports: parallel, Matrix, stats, methods,
Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph, scran,
Description: 
    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. 
Encoding: UTF-8
LazyData: TRUE
License: file LICENSE
URL: https://github.com/BatzoglouLabSU/SIMLR
BugReports: https://github.com/BatzoglouLabSU/SIMLR
biocViews: Clustering, GeneExpression, Sequencing, SingleCell
RoxygenNote: 5.0.1
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2016-11-09 01:39:42 UTC; biocbuild
Author: Bo Wang [aut],
  Daniele Ramazzotti [aut, cre],
  Luca De Sano [aut],
  Junjie Zhu [ctb],
  Emma Pierson [ctb],
  Serafim Batzoglou [ctb]
Built: R 3.3.1; i386-w64-mingw32; 2016-11-09 05:47:33 UTC; windows
Archs: i386, x64
