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.
|Author||Habil Zare and Parisa Shooshtari|
|Date of publication||None|
|Maintainer||Habil Zare <firstname.lastname@example.org>|
|License||GPL (>= 2)|
Building_Communities: Builds the communities from the set of all data points.
check.SamSPECTRAL.input: Checks the input to SamSPECTRAL.
Civilized_Spectral_Clustering: Runs the spectral clustering algorithm on the sample points.
Conductance_Calculation: Computes the conductance between communities.
Connecting: Combines the spectral clusters to build the connected...
eigen.values.10: Eigenvalues for building the SamSPECTRAL vignette.
eigen.values.1000: Eigenvalues for building the SamSPECTRAL vignette.
kneepointDetection: Fits 2 regression lines to data to estimate the knee (or...
SamSPECTRAL: Identifies the cell populations in flow cytometry data.
SamSPECTRAL-package: Identifying cell populations in flow cytometry data.
small: Flow cytometry data to test SamSPECTRAL algorithm.
stmFSC: Flow cytometry data to test SamSPECTRAL algorithm.