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mgsa (version 1.14.2)

Model-based gene set analysis

Description

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.

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Version

Version

1.14.2

License

Artistic-2.0

Maintainer

Sebastian Bauer

Last Published

February 15th, 2017

Functions in mgsa (1.14.2)

createMgsaGoSets

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).
populationSize

Size of the population of a MgsaResults
example-go

Example GO sets for mgsa
pPost

Posterior for beta
studySetSizeInPopulation

Size of the study set of a MgsaResults
subMgsaSets

Subset of an MgsaSets
alphaPost

Posterior for alpha
alphaMcmcPost

posterior estimates of the parameter alpha for each MCMC run
MgsaResults

Results of an MGSA analysis
MgsaSets

Sets of items and their annotations
plot,MgsaResults-method

Plot method for MgsaResults objects
length,MgsaSets-method

Length of a MgsaSets.
itemIndices

Item indices of a MgsaSets
pMcmcPost

posterior estimates of the parameter p for each MCMC run
setsResults

Posterior for each set
show,MgsaResults-method

Show an MgsaResults
readGAF

Read a Gene Ontology annotation file
restarts

How many MCMC runs
example-o

Example objects for mgsa
itemAnnotations

Item annotations of a MgsaSets
mgsa

Performs an MGSA analysis
mgsa-package

Model-based gene set analysis
show,MgsaSets-method

Show an MgsaSets
steps

How many steps per MCMC run
betaMcmcPost

posterior estimates of the parameter beta for each MCMC run
MgsaGoSets

Gene Ontology annotations
betaPost

Posterior for beta
MgsaMcmcResults

Instances of this class are used to hold the additional information that was provided by running (possibly multiple times) an MCMC algorithm.
setAnnotations

Set annotations of a MgsaSets
setsMcmcPost

posterior estimates of the the set marginal probabilities for each MCMC run