mgsa_score-methods: Model-based gene set analysis (MGSA)
In gCMAP: Tools for Connectivity Map-like analyses

Description Arguments Value Methods Note See Also Examples

This method is a wrapper for the mgsa methods from the Bioconductor package mgsa, which must be available on the system for the methods to run. The model-based gene set analysis (MGSA) analyzes all categories at once by embedding them in a Bayesian network, naturally taking overlap between categories into account and avoiding the need for multiple testing correction. Please consult the mgsa help page for more details.

`query`	A `CMAPCollection`, `GeneSet`, or `GeneSetCollection` object containing the 'query' gene sets to compare against the 'sets'
`sets`	A `CMAPCollection`, `GeneSetCollection` or `GeneSet` object
`universe`	A character string of gene ids for all genes that could potentially be of interest, e.g. all genes represented on a microarray, all annotated genes, etc.
`keep.scores`	Logical: store the identifiers for the genes detected in 'query' and 'sets' ? (Default: FALSE) The size of the generated CMAPResults object increases with the number of contained gene sets. For very large collections, setting this parameter to 'TRUE' may require large amounts of memory.
`element`	A character string corresponding to the assayDataElementName of the NChannelSet object to be thresholded on the fly with the `induceCMAPCollection`.
`lower`	The lower threshold for the `induceCMAPCollection`.
`higher`	The 'higher' threshold for the `induceCMAPCollection`.
`min.set.size`	Number of genes a gene set induced by `induceCMAPCollection` needs to contain to be included in the analysis (Default:5).
`...`	Additional arguments passed to mgsa function from the mgsa package, including the following: alpha: Grid of values for the parameter alpha. Values represent probabilities of false-positive events and hence must be in [0,1]. numeric beta: Grid of values for the parameter beta. Values represent probabilities of false-negative events and hence must be in [0,1]. numeric. p: Grid of values for the parameter p. Values represent probabilities of term activity and therefore must be in [0,1]. numeric. steps: The number of steps of each run of the MCMC sampler. integer of length 1. A recommended value is 1e6 or greater. restarts: The number of different runs of the MCMC sampler. integer of length 1. Must be greater or equal to 1. A recommended value is 5 or greater. threads: The number of threads that should be used for concurrent restarts. A value of 0 means to use all available cores. Defaults to 'getOption(mc.cores, default=0)', which will instruct mgsa to use all available cores.

A CMAPResults object. The reported p-values represent '1-marginal posterior probability'. For the 'effect' column, the p-values have been transformed to z-scores using a standard normal distribution.

signature(query = "GeneSet", sets = "CMAPCollection", universe = "character")
signature(query = "GeneSet", sets = "NChannelSet", universe = "character")
signature(query = "SignedGeneSet", sets = "CMAPCollection", universe = "character")
signature(query = "SignedGeneSet", sets = "NChannelSet", universe = "character")
signature(query = "GeneSetCollection", sets = "CMAPCollection", universe = "character")
signature(query = "GeneSetCollection", sets = "NChannelSet", universe = "character")
signature(query = "GeneSetCollection", sets = "GeneSetCollection", universe = "character")
signature(query = "GeneSet", sets = "GeneSetCollection", universe = "character")
signature(query = "GeneSet", sets = "GeneSet", universe = "character")
signature(query = "CMAPCollection", sets = "CMAPCollection", universe = "character")
signature(query = "CMAPCollection", sets = "GeneSetCollection", universe = "character")

This Bayesian approach does not require any additional correction of p-values for multiple testing. For consistency, the returned CMAPResults object contains a padj column duplicating the content of the pval column.

mgsa

if( is.element("mgsa", installed.packages()[,1])){
   require( "mgsa", character.only = TRUE )
   
   data(gCMAPData)
   gene.set.collection <- induceCMAPCollection(gCMAPData, "z", 
   higher=2, lower=-2)

    ## compare all gene sets in the gene.set.collection 
    ## to each other
    universe = featureNames(gCMAPData)
     mgsa_score(gene.set.collection, gene.set.collection, 
     universe = universe)
  }