recipe bioconductor-feast

FEAture SelcTion (FEAST) for Single-cell clustering






Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set.

package bioconductor-feast

(downloads) docker_bioconductor-feast



Required By:


With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-feast

and update with:

conda update bioconductor-feast

or use the docker container:

docker pull<tag>

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

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