R/FootprintFilter.R

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

#' @title Create a FootprintFilter object 
#'
#' @description
#' A FootprintFilter object allows for filtering based on gene footprinting databases. Using its
#' associated \code{getCandidates} method and URIs for both a genome database and project database,
#' a FootprintFilter object can be used to filter a list of possible transcription factors to those
#' that match footprint motifs for a supplied target gene.
#'
#' @include CandidateFilter.R
#' @import methods
#' 
#' @name FootprintFilter-class
#' @rdname FootprintFilter-class
#' @aliases FootprintFilter

#----------------------------------------------------------------------------------------------------
.FootprintFilter <- setClass("FootprintFilter", contains = "CandidateFilter")

#----------------------------------------------------------------------------------------------------
printf <- function(...) print(noquote(sprintf(...)))
#----------------------------------------------------------------------------------------------------
#' @rdname FootprintFilter-class
#'
#' @param mtx.assay An assay matrix of gene expression data
#' @param quiet A logical denoting whether or not the filter should print output
#'
#' @seealso \code{\link{getCandidates-FootprintFilter}}, \code{\link{getFilterAssayData}}
#'
#' @export
#'
#' @return An object of the FootprintFilter class
#' 
#' @family Filtering Objects
#' 
#' @examples
#' load(system.file(package="TReNA", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
#' footprint.filter <- FootprintFilter(mtx.assay = mtx.sub)

FootprintFilter <- function(mtx.assay=matrix(), quiet=TRUE)
{
    .FootprintFilter(CandidateFilter(mtx.assay = mtx.assay, quiet = quiet))

} # FootprintFilter, the constructor
#----------------------------------------------------------------------------------------------------
#' Get candidate genes using the variance filter
#'
#' @aliases getCandidates-FootprintFilter
#' 
#' @param obj An object of class FootprintFilter
#' @param extraArgs A named list containing 5 fields:
#' \itemize{
#' \item{"target.gene" A designated target gene that should be part of the mtx.assay data}
#' \item{"genome.db.uri" A connection to a genome database containing footprint information}
#' \item{"project.db.uri" A connection to a project database containing footprint information}
#' \item{"size.upstream" An integer denoting the distance upstream of the target gene to look for footprints}
#' \item{"size.downstream" An integer denoting the distance downstream of the target gene to look for footprints}
#' }
#'
#' @seealso \code{\link{FootprintFilter}}
#' 
#' @family getCandidate Methods
#' 
#' @return A vector containing all genes with variances less than the target gene
#'
#' @examples
#'
#' # Use footprint filter with the included SQLite database for MEF2C to filter candidates
#' # in the included Alzheimer's dataset
#' load(system.file(package="TReNA", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
#' footprint.filter <- FootprintFilter(mtx.assay = mtx.sub)
#'
#' target.gene <- "MEF2C"
#' db.address <- system.file(package="TReNA", "extdata")
#' genome.db.uri <- paste("sqlite:/",db.address,"genome.sub.db", sep = "/")
#' project.db.uri <- paste("sqlite:/",db.address,"project.sub.db", sep = "/")
#' 
#' tfs <- getCandidates(footprint.filter, extraArgs = list("target.gene" = target.gene,
#' "genome.db.uri" = genome.db.uri, "project.db.uri" = project.db.uri,
#' "size.upstream" = 1000, "size.downstream" = 1000))
    

setMethod("getCandidates", "FootprintFilter",

          function(obj,extraArgs){

              # Extract the arguments
              target.gene <- extraArgs[["target.gene"]]
              genome.db.uri <- extraArgs[["genome.db.uri"]]
              project.db.uri <- extraArgs[["project.db.uri"]]
              size.upstream <- extraArgs[["size.upstream"]]
              size.downstream <- extraArgs[["size.downstream"]]
              
              # Create a FootprintFinder object and find the footprints
              fp <- FootprintFinder(genome.db.uri, project.db.uri, quiet=TRUE)
              tbl.fp <- try(getFootprintsForGene(fp, target.gene,
                                                 size.upstream=size.upstream, size.downstream=size.downstream),
                            silent = TRUE)
              
              
              # Convert the footprints to genes if it's a table and close the database connection
              if(!(class(tbl.fp) == "try-error")){
                  tbl.out <- mapMotifsToTFsMergeIntoTable(fp, tbl.fp)                        
                  closeDatabaseConnections(fp)
                  # Intersect the footprints with the rows in the matrix                  
                  candidate.tfs <- intersect(tbl.out$tf, rownames(getFilterAssayData(obj)))                             
                  # Return the TFs                  
                  return(list("tfs" = candidate.tfs,                              
                              "tbl" = tbl.out))}
              else{closeDatabaseConnections(fp)
                  return(NULL)}                                   
          }
          
)
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