Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data sample. For instructions on manual installation, refer to the PDF file provided in the following documentation.
Package details 


Author  Habil Zare and Parisa Shooshtari 
Bioconductor views  Cancer CellBiology Clustering FlowCytometry HIV StemCells 
Maintainer  Habil Zare <[email protected]> 
License  GPL (>= 2) 
Version  1.31.0 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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