Package: gaga 2.59.0

David Rossell

gaga: GaGa hierarchical model for high-throughput data analysis

Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).

Authors:David Rossell <[email protected]>.

gaga_2.59.0.tar.gz
gaga_2.59.0.zip(r-4.7)gaga_2.59.0.zip(r-4.6)gaga_2.59.0.zip(r-4.5)
gaga_2.59.0.tgz(r-4.6-x86_64)gaga_2.59.0.tgz(r-4.6-arm64)gaga_2.59.0.tgz(r-4.5-x86_64)gaga_2.59.0.tgz(r-4.5-arm64)
gaga_2.59.0.tar.gz(r-4.7-arm64)gaga_2.59.0.tar.gz(r-4.7-x86_64)gaga_2.59.0.tar.gz(r-4.6-arm64)gaga_2.59.0.tar.gz(r-4.6-x86_64)
gaga_2.59.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
gaga/json (API)

# Install 'gaga' in R:

install.packages('gaga', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

On BioConductor:gaga-2.59.0(bioc 3.24)gaga-2.58.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncology onechannel massspectrometry multiplecomparison differentialexpression classification

4.26 score 1 packages 15 scripts 460 downloads 2 mentions 24 exports 10 dependencies

Last updated from:bf2188e897. Checks:1 ERROR, 4 NOTE, 2 OK, 7 WARNING. Indexed: yes.

Target	Result	Time
bioc-checks	ERROR	143
linux-devel-arm64	NOTE	128
linux-devel-x86_64	NOTE	152
source / vignettes	OK	165
linux-release-arm64	NOTE	127
linux-release-x86_64	NOTE	190
macos-release-arm64	WARNING	92
macos-release-x86_64	WARNING	203
macos-oldrel-arm64	WARNING	126
macos-oldrel-x86_64	WARNING	219
windows-devel	WARNING	122
windows-release	WARNING	100
windows-oldrel	WARNING	196
wasm-release	OK	93

Exports:adjustfitNN buildPatterns checkfit classpred dcgamma findgenes fitGG fitNN fitNNSingleHyp forwsimDiffExpr geneclus getpar mcgamma parest plotForwSim posmeansGG powclasspred powfindgenes rcgamma seqBoundariesGrid simGG simLNN simnewsamples simNN

Dependencies:Biobase BiocGenerics cluster coda EBarrays generics lattice Matrix mgcv nlme

Manual for the gaga library

Rendered fromgagamanual.Rnwusingutils::Sweaveon May 30 2026.

Last update: 2013-11-01
Started: 2013-11-01

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Build a matrix with all possible patterns given a number of groups where samples may belong to.	buildPatterns
Check goodness-of-fit of GaGa and MiGaGa models	checkfit checkfit.gagafit
Predict the class that a new sample belongs to.	classpred classpred.gagafit
Approximate gamma shape distribution	dcgamma mcgamma rcgamma
Find differentially expressed genes after GaGa or Normal-Normal fit.	findgenes findgenes.gagafit findgenes.nnfit
Fit GaGa hierarchical model	adjustfitNN fitGG fitNN fitNNSingleHyp
Forward simulation for differential expression.	forwsimDiffExpr forwsimDiffExpr.gagafit forwsimDiffExpr.nnfit
Cluster genes into expression patterns.	geneclus geneclus.gagafit
Extract hyper-parameter estimates from a gagafit or nnfit object	getpar getpar.gagafit getpar.nnfit
Parameter estimates and posterior probabilities of differential expression for GaGa and MiGaGa model	parest parest.gagafit
Plot forward simulation trajectories	plotForwSim
Gene-specific posterior means	posmeansGG posmeansGG.gagafit
Expected probability that a future sample is correctly classified.	powclasspred powclasspred.gagafit
Power computations for differential expression	powfindgenes
Print an object of class gagaclus	print.gagaclus
Print an object of class gagafit or nnfit	print.gagafit print.nnfit
Print an object of class gagahyp	print.gagahyp
Evaluate expected utility for parametric sequential stopping boundaries.	seqBoundariesGrid
Prior predictive simulation	simGG simLNN simNN
Posterior predictive simulation	simnewsamples simnewsamples.gagafit simnewsamples.nnfit