R/IGSAinput.R

In MIGSA: Massive and Integrative Gene Set Analysis

#' @include IGSAinput-class.R
setGeneric(name = "get_fit", def = function(M, fit_options, params) {
  standardGeneric("get_fit")
})

#' @importFrom limma contrasts.fit lmFit treat voom
#' @importFrom stats p.adjust
#' @include ExprData-class.R
#' @include FitOptions-class.R
#' @include SEAparams.R
setMethod(
  f = "get_fit",
  signature = c("ExprData", "FitOptions", "SEAparams"),
  definition = function(M, fit_options, params) {
    act_treat_lfc <- treat_lfc(params)
    act_design <- designMatrix(fit_options)
    act_contrast <- contrast(fit_options)
    act_adj_meth <- adjust_method(params)

    # apply voom
    if (is(M, "DGEList")) {
      M <- voom(M, design = act_design)
    }

    # Adjust the model
    fit <- lmFit(M, act_design)
    # treat correction
    fit2 <- treat(contrasts.fit(fit, act_contrast), lfc = act_treat_lfc)
    # Adjusted pvalues
    fit2$p.adjust <- apply(fit2$p.value, 2, p.adjust, method = act_adj_meth)

    return(fit2)
  }
)

setGeneric(
  name = "igsaGetDEGenes",
  def = function(seaParams, exprData, fitOptions) {
    standardGeneric("igsaGetDEGenes")
  }
)

#' @importFrom futile.logger flog.info
#' @include ExprData-class.R
#' @include FitOptions-class.R
#' @include IGSAinput-class.R
setMethod(
  f = "igsaGetDEGenes",
  signature = c("SEAparams", "ExprData", "FitOptions"),
  definition = function(seaParams, exprData, fitOptions) {
    # get the fit
    act_fit <- get_fit(exprData, fitOptions, seaParams)

    de_coff <- de_cutoff(seaParams)
    # get the DE genes depending on the cutoff value
    dif <- act_fit$p.adjust[, , drop = FALSE] <= de_coff
    dif <- unique(rownames(dif)[dif])

    flog.info(paste(
      "DE genes", length(dif), "of a total of",
      nrow(exprData), "(",
      round(length(dif) / nrow(exprData) * 100, 2), "%)"
    ))

    return(dif)
  }
)