DOI: 10.18129/B9.bioc.FEAST  

This is the development version of FEAST; for the stable release version, see FEAST.

FEAture SelcTion (FEAST) for Single-cell clustering

Bioconductor version: Development (3.18)

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.

Author: Kenong Su [aut, cre], Hao Wu [aut]

Maintainer: Kenong Su <kenong.su at emory.edu>

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biocViews Clustering, FeatureExtraction, Sequencing, SingleCell, Software
Version 1.9.0
In Bioconductor since BioC 3.13 (R-4.1) (2 years)
License GPL-2
Depends R (>= 4.1), mclust, BiocParallel, SummarizedExperiment
Imports SingleCellExperiment, methods, stats, utils, irlba, TSCAN, SC3, matrixStats
Suggests rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0), BiocStyle
BugReports https://github.com/suke18/FEAST/issues
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