binclust.it: Bin Clustering
In JulianSpagnuolo/FACkit: FACS Analysis Tool Kit

Bin Clustering

1 2	binclust.it(expdata, markers, clust.col, noise.clust.id = "0", minpts = 4, alpha = 0.001, maxit = 5)

`markers`	Character vector, identifies vectors in data containing the marker expression data.
`clust.col`	Character vector, column name identifying the vector of cluster ids.
`noise.clust.id`	If a noise cluster is present in the data, this parameter will remove it from sub-clustering. Ie. DBScan identifies noise points as cluster "0". If NULL, this parameter is not used. Default is NULL.
`minpts`	integer. Minimum number of cells a cluster must contain in order to be kept. Default is 4.
`alpha`	numeric. alpha param for qchisq call to identify cluster outliers by mahalanobis distance (see qchisq)
`maxit`	integer. Maximum number of iterations to run. Default is 5.
`x`	data.frame. Contains marker expression and cluster id data.
`bin.list`	list of matrices. Each member of the list is a matrix defining the sub-clusters within each original clusters that should be split. Output from binmat function.