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source("http://bioconductor.org/biocLite.R")
biocLite("flowMap")
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flowMap
A probabilistic algorithm for matching and comparing multiple flow cytometry samples
Bioconductor version: 3.0
This package provides an algorithm to compare and match cell populations across multiple flow cytometry samples. The method is based on the Friedman-Rafsky test, a nonparametric multivariate statistical test, where two cell distributions match if they occupy a similar feature space. The algorithm allows the users to specify a reference sample for comparison or to construct a reference sample from the available data. The output of the algorithm is a set of text files where the cell population labels are replaced by a metaset of population labels, generated from the matching process.
Author: Chiaowen Joyce Hsiao and Yu Qian
Maintainer: Chiaowen Joyce Hsiao <joyce.hsiao1 at gmail.com>
Citation (from within R,
enter citation("flowMap")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("flowMap")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("flowMap")
| R Script | Multiple sample comparison in flow cytometry data with flowMap | |
| Reference Manual |
Details
| biocViews | FlowCytometry, MultipleComparison, Software |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL (>=2) |
| Depends | R (>= 3.0.1), ade4 (>= 1.5-2), doParallel (>= 1.0.3), abind (>= 1.4.0), reshape2 (>= 1.2.2), ggplot2 (>= 0.9.3.1), scales (>= 0.2.3), methods (>= 2.14) |
| Imports | |
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| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
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| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | flowMap_1.4.0.tar.gz |
| Windows Binary | flowMap_1.4.0.zip |
| Mac OS X 10.6 (Snow Leopard) | flowMap_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | flowMap_1.4.0.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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