# FEAture SelcTion (FEAST) for Single-cell clustering

## Documentation for package ‘FEAST’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

align_CellType | Align the cell types from the prediction with the truth.
cal_F2 | Calculate the gene-level F score and corresponding significance level.
cal_Fisher2 | Calculate the gene-level fisher score.
cal_metrics | 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_MSE | Standard way to preprocess the count matrix. It is the QC step for the genes.
Consensus | Consensus Clustering
eval_Cluster | Calculate the a series of the evaluation statistics.
FEAST | FEAST main function
FEAST_fast | FEAST main function (fast version)
Norm_Y | Normalize the count expression matrix by the size factor and take the log transformation.
process_Y | Standard way to preprocess the count matrix. It is the QC step for the genes.
Purity | Calculate the purity between two vectors.
SC3_Clust | SC3 Clustering
Select_Model_short_SC3 | Using clustering results based on feature selection to perform model selection.
Select_Model_short_TSCAN | Using clustering results (from TSCAN) based on feature selection to perform model selection.
Seurat_Clust | Seurat Clustering
trueclass | An example single cell dataset for the cell label information (Yan)
TSCAN_Clust | TSCAN Clustering
vector2matrix | function for convert a vector to a binary matrix
Visual_Rslt | Using clustering results based on feature selection to perform model selection.
Y | An example single cell count expression matrix (Yan)