Package: FEAST 1.13.0

Kenong Su

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.

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

FEAST_1.13.0.tar.gz
FEAST_1.13.0.zip(r-4.5)FEAST_1.13.0.zip(r-4.4)FEAST_1.13.0.zip(r-4.3)
FEAST_1.13.0.tgz(r-4.4-arm64)FEAST_1.13.0.tgz(r-4.4-x86_64)FEAST_1.13.0.tgz(r-4.3-arm64)FEAST_1.13.0.tgz(r-4.3-x86_64)
FEAST_1.13.0.tar.gz(r-4.5-noble)FEAST_1.13.0.tar.gz(r-4.4-noble)
FEAST_1.13.0.tgz(r-4.4-emscripten)FEAST_1.13.0.tgz(r-4.3-emscripten)
FEAST.pdf |FEAST.html✨
FEAST/json (API)
NEWS

# Install 'FEAST' in R:

install.packages('FEAST', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/suke18/feast/issues

Datasets:

Y - An example single cell count expression matrix
trueclass - An example single cell dataset for the cell label information

On BioConductor:FEAST-1.13.0(bioc 3.20)FEAST-1.12.0(bioc 3.19)

bioconductor-package

15 exports 0.49 score 115 dependencies 87 mentions

Last updated 2 months agofrom:c3f7d2ef95

Exports:align_CellType cal_F2 cal_MSE Consensus eval_Cluster FEAST FEAST_fast Norm_Y process_Y SC3_Clust Select_Model_short_SC3 Select_Model_short_TSCAN setUp_BPPARAM TSCAN_Clust Visual_Rslt

Dependencies:abind askpass base64enc BH Biobase BiocGenerics BiocParallel bitops bslib cachem caTools class cli cluster codetools colorspace combinat commonmark cpp11 crayon curl DelayedArray DEoptimR digest doParallel doRNG e1071 fansi farver fastICA fastmap fontawesome foreach formatR fs futile.logger futile.options GenomeInfoDb GenomeInfoDbData GenomicRanges ggplot2 glue gplots gtable gtools htmltools httpuv httr igraph IRanges irlba isoband iterators jquerylib jsonlite KernSmooth labeling lambda.r later lattice lifecycle magrittr MASS Matrix MatrixGenerics matrixStats mclust memoise mgcv mime munsell mvtnorm nlme openssl pcaPP pheatmap pillar pkgconfig plyr promises proxy R6 rappdirs RColorBrewer Rcpp RcppArmadillo rlang rngtools robustbase ROCR rrcov S4Arrays S4Vectors sass SC3 scales shiny SingleCellExperiment snow sourcetools SparseArray SummarizedExperiment sys tibble TrajectoryUtils TSCAN UCSC.utils utf8 vctrs viridisLite withr WriteXLS xtable XVector zlibbioc

The FEAST User's Guide

Kenong Su, Hao Wu

Rendered fromFEAST.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2021-09-09
Started: 2021-03-15

Help page	Topics
Align the cell types from the prediction with the truth.	align_CellType
Calculate the gene-level F score and corresponding significance level.	cal_F2
Calculate the gene-level fisher score.	cal_Fisher2
Calculate 3 metrics and these methods are exported in C codes. flag = 1 - Rand index, flag = 2 - Fowlkes and Mallows's index, flag = 3 - Jaccard index	cal_metrics
Standard way to preprocess the count matrix. It is the QC step for the genes.	cal_MSE
Consensus Clustering	Consensus
Calculate the a series of the evaluation statistics.	eval_Cluster
FEAST main function	FEAST
FEAST main function (fast version)	FEAST_fast
Normalize the count expression matrix by the size factor and take the log transformation.	Norm_Y
Standard way to preprocess the count matrix. It is the QC step for the genes.	process_Y
Calculate the purity between two vectors.	Purity
SC3 Clustering	SC3_Clust
Using clustering results based on feature selection to perform model selection.	Select_Model_short_SC3
Using clustering results (from TSCAN) based on feature selection to perform model selection.	Select_Model_short_TSCAN
An example single cell dataset for the cell label information (Yan)	trueclass
TSCAN Clustering	TSCAN_Clust
function for convert a vector to a binary matrix	vector2matrix
Using clustering results based on feature selection to perform model selection.	Visual_Rslt
An example single cell count expression matrix (Yan)	Y

Package: FEAST 1.13.0

FEAST: FEAture SelcTion (FEAST) for Single-cell clustering

The FEAST User's Guide

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)