DESCRIPTION

Package: SIMLR
Version: 1.32.0
Date: 2024-10-31
Title: 
    Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)
Authors@R: c(person("Daniele", "Ramazzotti", role=c("aut"),email="daniele.ramazzotti@unimib.it",
                comment = c(ORCID = "0000-0002-6087-2666")),
             person("Bo", "Wang", role=c("aut"), email="wangbo.yunze@gmail.com"),
             person("Luca", "De Sano", role=c("cre","aut"), email="luca.desano@gmail.com",
                comment = c(ORCID = "0000-0002-9618-3774")),
             person("Serafim", "Batzoglou", role=c("ctb")))
Depends:
    R (>= 4.1.0),
Imports:
    parallel,
    Matrix,
    stats,
    methods,
    Rcpp,
    pracma,
    RcppAnnoy,
    RSpectra
Suggests:
    BiocGenerics,
    BiocStyle,
    testthat,
    knitr,
    igraph
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 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. 
Encoding: UTF-8
License: file LICENSE
URL: https://github.com/BatzoglouLabSU/SIMLR
BugReports: https://github.com/BatzoglouLabSU/SIMLR
biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell
RoxygenNote: 7.3.2
LinkingTo: Rcpp
NeedsCompilation: yes
VignetteBuilder: knitr