recipe bioconductor-simlr

Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)

Homepage

https://bioconductor.org/packages/3.11/bioc/html/SIMLR.html

License

file LICENSE

Recipe

/bioconductor-simlr/meta.yaml

Links

biotools: simlr, doi: 10.1101/118901

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.

package bioconductor-simlr

(downloads) docker_bioconductor-simlr

Versions

1.14.0-0, 1.12.0-0, 1.10.0-1, 1.8.1-0, 1.8.0-0, 1.6.0-0, 1.4.0-0

Depends
Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-simlr

and update with:

conda update bioconductor-simlr

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-simlr:<tag>

(see bioconductor-simlr/tags for valid values for <tag>)