getCandidates-FootprintFilter-method: Get candidate genes using the footprint filter

In PriceLab/TReNA: Fit transcriptional regulatory networks using gene expression, priors, machine learning

Get candidate genes using the footprint filter

Description

Get candidate genes using the footprint filter

Usage

## S4 method for signature 'FootprintFilter'
getCandidates(obj)

Arguments

obj	An object of class FootprintFilter

Value

A list, where one element is the transcription factors found in the footprints and the other is a data frame containing all the meta data for the footprints

See Also

FootprintFilter

Other getCandidate Methods: getCandidates,GeneOntologyFilter-method, getCandidates,HumanDHSFilter-method, getCandidates,VarianceFilter-method, getCandidates()

Examples

# Use footprint filter with the included SQLite database for MEF2C to filter candidates
# in the included Alzheimer's dataset, using the Trena object to get regions
## Not run: 
   target.gene <- "MEF2C"
   db.address <- system.file(package="trena", "extdata")
   genome.db.uri <- paste("sqlite:/",db.address,"mef2c.neighborhood.hg38.gtfAnnotation.db", sep = "/")
   project.db.uri <- paste("sqlite:/",db.address,"mef2c.neigborhood.hg38.footprints.db", sep = "/")
   size.upstream <- 1000
   size.downstream <- 1000
      # Construct a Trena object and use it to retrive the regions
   trena <- Trena("hg38")
   regions <- getProximalPromoter(trena,target.gene, size.upstream, size.downstream)
   footprint.filter <- FootprintFilter(genomeDB = genome.db.uri, footprintDB = project.db.uri,
                                       regions = regions)
   footprints <- getCandidates(footprint.filter)
   
## End(Not run)

PriceLab/TReNA documentation built on March 21, 2023, 1:57 p.m.