cytoMEM
This is the released version of cytoMEM; for the devel version, see cytoMEM.
Marker Enrichment Modeling (MEM)
Bioconductor version: Release (3.23)
MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD.
Author: Sierra Lima [aut]
, Kirsten Diggins [aut]
, Jonathan Irish [aut, cre]
Maintainer: Jonathan Irish <jonathan.irish at vanderbilt.edu>
citation("cytoMEM")):
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M (2015). "Orchestrating high-throughput genomic analysis with Bioconductor." Nature Methods, 12(2), 115–121. doi:10.1038/nmeth.3252.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004). "Bioconductor: open software development for computational biology and bioinformatics." Genome Biology, 5(10), R80. doi:10.1186/gb-2004-5-10-r80.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("cytoMEM")
For older versions of R, please refer to the appropriate Bioconductor release.
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("cytoMEM")| Intro_to_Marker_Enrichment_Modeling_Analysis | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | CellBiology, Classification, Clustering, DataImport, DataRepresentation, FlowCytometry, Proteomics, SingleCell, Software, SystemsBiology |
| Version | 1.16.0 |
| In Bioconductor since | BioC 3.15 (R-4.2) (4.5 years) |
| License | GPL-3 |
| Depends | R (>= 4.2.0) |
| Imports | gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods |
| System Requirements | |
| URL | https://github.com/cytolab/cytoMEM |
See More
| Suggests | knitr, rmarkdown |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Links To Me | |
| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | cytoMEM_1.16.0.tar.gz |
| Windows Binary (x86_64) | cytoMEM_1.16.0.zip |
| macOS Binary (big-sur-x86_64) | cytoMEM_1.16.0.tgz |
| macOS Binary (sonoma-arm64) | cytoMEM_1.16.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/cytoMEM |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/cytoMEM |
| Bioc Package Browser | https://code.bioconductor.org/browse/cytoMEM/ |
| Package Short Url | https://bioconductor.org/packages/cytoMEM/ |
| Package Downloads Report | Download Stats |