R/regenrich_diffExpr.R

In RegEnrich: Gene regulator enrichment analysis

#' @rdname regenrich_diffExpr
#' @export
setGeneric("regenrich_diffExpr",
           function(object, ...) standardGeneric("regenrich_diffExpr"))

.regenrich_diffExpr = function(object, ...) {
  argsIn = list(...)
  
  deaParams = c("method", "minMeanExpr", "design", "reduced",
                "contrast", "coef", "name", "fitType", "sfType", "betaPrior",
                "minReplicatesForReplace", "useT", "minmu", "parallel",
                "BPPARAM", "altHypothesis", "listValues", "cooksCutoff",
                "independentFiltering", "alpha", "filter", "theta", 
                "filterFun", "addMLE", "blind", "ndups", "spacing", "block", 
                "correlation", "weights", "proportion", "stdev.coef.lim", 
                "trend", "robust", "winsor.tail.p")
  object = checkParams(object, argsIn, deaParams)
  ParamsIn = getParamsIn(object)
  deaParams = ParamsIn[deaParams]
  
  deaParams$method = match.arg(deaParams$method, c("Wald_DESeq2", "LRT_DESeq2", 
                                                   "limma", "LRT_LM"))
  
  resCall = as.call(c(list(quote(DEA), expr = quote(assay(object)),
                           pData = quote(colData(object))), deaParams))
  resDEA = eval(resCall)
  
  # expression after filtering (and normalization)
  object = object[rownames(resDEA$normExprHigh),]
  
  # differential expression results (DeaSet object)
  mcols(object) = resDEA$pFC
  
  # Since differential expression analysis is changed, the
  # network, enrichment and regulator ranking must be changed
  object@network = newTopNetwork()
  object@topNetwork = newTopNetwork()
  object@resEnrich = newEnrich()
  object@resScore = newScore()
  
  # update paramsOut
  object@paramsOut = list(DeaMethod = deaParams$method, networkType = NULL,
                          enrichTest = NULL, percent = NULL)
  
  return(object)
}
#' Differential expression analysis step
#'
#' This is the first step of RegEnrich analysis.
#' differential expression analysis by this function needs to
#' be performed on a `RegenrichSet` object.
#'
#' @param object a `RegenrichSet` object, which is initialized by
#' \code{\link{RegenrichSet}} function.
#'
#' @param ... arguments for differential analysis.
#' After constructing a `RegenrichSet` object,
#' all arguments for RegEnrich analysis have been initialized and
#' stored in `paramsIn`` slot. while the arguments for differential analysis
#' can be re-specified here.\cr\cr
#' These arguments include 'method', 'minMeanExpr', 'design', 'reduced',
#' 'contrast',
#' 'coef', 'name', 'fitType', 'sfType', 'betaPrior', 'minReplicatesForReplace',
#' 'useT', 'minmu', 'parallel', 'BPPARAM', 'altHypothesis',
#' 'listValues', 'cooksCutoff', 'independentFiltering', 'alpha',
#' 'filter', 'theta', 'filterFun', 'addMLE', 'blind', 'ndups',
#' 'spacing', 'block', 'correlation', 'weights', 'proportion',
#' 'stdev.coef.lim', 'trend', 'robust', and 'winsor.tail.p'.\cr
#' See \code{\link{RegenrichSet}} function for more details about these
#' arguments.
#' @return This function returns a `RegenrichSet` object with an updated
#' `resDEA` slot, which is a `DeaSet` object, and an updated `paramsIn` slot.
#' See \code{\link{newDeaSet}} function for more details about `DeaSet` class.
#' If an argument not in the above list is specified in the regenrich_diffExpr
#' function, a warning or error will be raised.
#'
#' @examples
#' # library(RegEnrich)
#' data("Lyme_GSE63085")
#' data("TFs")
#' 
#' data = log2(Lyme_GSE63085$FPKM + 1)
#' colData = Lyme_GSE63085$sampleInfo
#' 
#' # Take first 2000 rows for example
#' data1 = data[seq(2000), ]
#'
#' design = model.matrix(~0 + patientID + week, data = colData)
#' 
#' # Initializing a 'RegenrichSet' object
#' object = RegenrichSet(expr = data1,
#'                       colData = colData,
#'                       method = 'limma', minMeanExpr = 0,
#'                       design = design,
#'                       contrast = c(rep(0, ncol(design) - 1), 1),
#'                       networkConstruction = 'COEN',
#'                       enrichTest = 'FET')
#' # Using the predifined parameters in the previous step
#' (object = regenrich_diffExpr(object))
#'
#' # re-specifying parameter 'minMeanExpr'
#' print(slot(object, 'paramsIn')$minMeanExpr)
#' (object = regenrich_diffExpr(object, minMeanExpr = 1))
#' print(slot(object, 'paramsIn')$minMeanExpr)
#'
# \donttest{
#' # Unrecognized argument 'unrecognizedArg' (Error)
#' # object = regenrich_diffExpr(object, minMeanExpr = 1,
#' #                             unrecognizedArg = 23)
#' 
#' # Argument not for differential expression analysis (Warning)
#' # print(slot(object, 'paramsIn')$networkConstruction)
#' # (object = regenrich_diffExpr(object, minMeanExpr = 1,
#' #                              networkConstruction = 'GRN'))
#' # print(slot(object, 'paramsIn')$networkConstruction) # not changed
# }
#' @rdname regenrich_diffExpr
#' @include regenrichClasses.R
#' @seealso Initialization of a `RegenrichSet` object
#' \code{\link{RegenrichSet}},and next step
#' \code{\link{regenrich_network}}.
#'
#' @export
#'
setMethod(f = "regenrich_diffExpr", signature = "RegenrichSet",
          definition = .regenrich_diffExpr)