suke18/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.

Getting started

Package details

AuthorKenong Su [aut, cre], Hao Wu [aut]
Bioconductor views Clustering FeatureExtraction Sequencing SingleCell
MaintainerKenong Su <>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
suke18/FEAST documentation built on Sept. 14, 2021, 12:22 a.m.