Learn R Programming

SamSPECTRAL (version 1.26.0)

Identifies cell population in flow cytometry data.

Description

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.

Copy Link

Version

Version

1.26.0

License

GPL (>= 2)

Maintainer

Habil Zare

Last Published

February 15th, 2017

Functions in SamSPECTRAL (1.26.0)

Building_Communities

Builds the communities from the set of all data points.
Connecting

Combines the spectral clusters to build the connected components.
Conductance_Calculation

Computes the conductance between communities.
check.SamSPECTRAL.input

Checks the input to SamSPECTRAL.
eigen.values.1000

Eigenvalues for building the SamSPECTRAL vignette.
eigen.values.10

Eigenvalues for building the SamSPECTRAL vignette.
Civilized_Spectral_Clustering

Runs the spectral clustering algorithm on the sample points.
SamSPECTRAL

Identifies the cell populations in flow cytometry data.
SamSPECTRAL-package

Identifying cell populations in flow cytometry data.
kneepointDetection

Fits 2 regression lines to data to estimate the knee (or elbow) point.
small

Flow cytometry data to test SamSPECTRAL algorithm.
stmFSC

Flow cytometry data to test SamSPECTRAL algorithm.