Package: mgsa 1.53.0

Sebastian Bauer

mgsa: Model-based gene set analysis

Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology.

Authors:Sebastian Bauer <[email protected]>, Julien Gagneur <[email protected]>

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mgsa.pdf |mgsa.html✨
mgsa/json (API)

# Install 'mgsa' in R:
install.packages('mgsa', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/sba1/mgsa-bioc/issues

Uses libs:
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On BioConductor:mgsa-1.53.0(bioc 3.20)mgsa-1.52.0(bioc 3.19)

bioconductor-package

21 exports 1.95 score 5 dependencies 5 mentions

Last updated 2 months agofrom:238e3206e2

Exports:alphaMcmcPostalphaPostbetaMcmcPostbetaPostitemAnnotationsitemIndicesmgsansamplesplotpMcmcPostpopulationSizepPostreadGAFrestartssetAnnotationssetsMcmcPostsetsResultsshowstepsstudySetSizeInPopulationsubMgsaSets

Dependencies:bitopscaToolsgplotsgtoolsKernSmooth

Overview of the mgsa package.

Rendered frommgsa.Rnwusingutils::Sweaveon Jul 03 2024.

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

Readme and manuals

Help Manual

Help pageTopics
Model-based gene set analysismgsa-package
posterior estimates of the parameter alpha for each MCMC runalphaMcmcPost alphaMcmcPost,MgsaMcmcResults-method
Posterior for alphaalphaPost alphaPost,MgsaResults-method
posterior estimates of the parameter beta for each MCMC runbetaMcmcPost betaMcmcPost,MgsaMcmcResults-method
Posterior for betabetaPost betaPost,MgsaResults-method
This functions takes a 1:1 mapping of go.ids to items and returns a full MgsaGOSets instance. The structure of GO is gathered from GO.db. It is sufficient to specify just the directly asserted mapping (or annotation), i.e., the most specific ones. The true path rule is taken account, that is, if an item is annotated to a term then it will be also annotated to more general terms (some people prefer to say that just the transitive closure is calculated).createMgsaGoSets
Example GO sets for mgsaexample-go example_go
Example objects for mgsaexample-o example_o
Item annotations of a MgsaSetsitemAnnotations itemAnnotations,MgsaSets,character-method itemAnnotations,MgsaSets,missing-method
Item indices of a MgsaSetsitemIndices itemIndices,MgsaSets,character-method itemIndices,MgsaSets,numeric-method
Length of a MgsaSets.length,MgsaSets-method
Performs an MGSA analysismgsa mgsa,character,list-method mgsa,character,MgsaSets-method mgsa,integer,list-method mgsa,logical,list-method mgsa,numeric,list-method
Gene Ontology annotationsMgsaGoSets-class
Instances of this class are used to hold the additional information that was provided by running (possibly multiple times) an MCMC algorithm.MgsaMcmcResults-class
Results of an MGSA analysisMgsaResults-class
Sets of items and their annotationsMgsaSets-class
How many samples per MCMC run collectednsamples nsamples,MgsaMcmcResults-method
Plot method for MgsaResults objectsplot,MgsaResults-method
posterior estimates of the parameter p for each MCMC runpMcmcPost pMcmcPost,MgsaMcmcResults-method
Size of the population of a MgsaResultspopulationSize populationSize,MgsaResults-method
Posterior for betapPost pPost,MgsaResults-method
Read a Gene Ontology annotation filereadGAF
How many MCMC runsrestarts restarts,MgsaMcmcResults-method
Set annotations of a MgsaSetssetAnnotations setAnnotations,MgsaSets,character-method setAnnotations,MgsaSets,missing-method
posterior estimates of the the set marginal probabilities for each MCMC runsetsMcmcPost setsMcmcPost,MgsaMcmcResults-method
Posterior for each setsetsResults setsResults,MgsaResults-method
Show an MgsaResultsshow,MgsaResults-method
Show an MgsaSetsshow,MgsaSets-method
How many steps per MCMC runsteps steps,MgsaMcmcResults-method
Size of the study set of a MgsaResultsstudySetSizeInPopulation studySetSizeInPopulation,MgsaResults-method
Subset of an MgsaSetssubMgsaSets subMgsaSets,MgsaSets,character-method