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biocLite("SamSPECTRAL")
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SamSPECTRAL
Identifies cell population in flow cytometry data.
Bioconductor version: 3.0
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
Maintainer: Habil Zare <zare at u.washington.edu>
Citation (from within R,
enter citation("SamSPECTRAL")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("SamSPECTRAL")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("SamSPECTRAL")
| R Script | A modified spectral clustering method for clustering Flow Cytometry Data | |
| Reference Manual |
Details
| biocViews | Cancer, CellBiology, Clustering, FlowCytometry, HIV, Software, StemCells |
| Version | 1.20.0 |
| In Bioconductor since | BioC 2.6 (R-2.11) |
| License | GPL (>= 2) |
| Depends | R (>= 2.10) |
| Imports | methods |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | SamSPECTRAL_1.20.0.tar.gz |
| Windows Binary | SamSPECTRAL_1.20.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | SamSPECTRAL_1.20.0.tgz |
| Mac OS X 10.9 (Mavericks) | SamSPECTRAL_1.20.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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