R/limmaDS2.R

In isglobal-brge/dsOmics: DataSHIELD server site Omic functions

#'
#' @title Differential expression analysis using limma on the server-side
#' @description Performs differential expression analysis using LIMMA
#' @param Set either a \code{ExpressionSet} or a \code{RangedSummarizedExperiment}
#' @param variable_names grouping variable used to perform differential expression analysis
#' @param covariable_names name of variables used in the adjusted models
#' @param type ...
#' @param contrasts ...
#' @param levels ...
#' @param coef ...
#' @param sva should differential expression analysis be adjusted by SVA?
#' @param annotCols variables from the annotation data used in the output
#' @param method String indicating the method used in the regression (e.g. lmFit function of limma: "ls" or 
#' "robust". (Default: "ls") 
#' @param robust Logical value indicating whether robust method is applied in the eBayes function of limma. Default is FALSE.
#' @param normalization String indicating the normalize method used when using voom for RNAseq data (see normalized.method argument in limma::vomm)
#' @param voomQualityWeights Logical value indicating whether limma::voomWithQualityWeights should be used instead of
#' limma::voom. 
#' @param big Logical value indicating whether SmartSVA should be used instead of SVA 
#' (TRUE recommended for methylation or when having large number of samples). 
#' 
#' 
#' @return a matrix with genes ordered by p-value
#' @author Gonzalez, JR.
#' 
#' @import dplyr
#' @export 
#' 
limmaDS2 <- function(Set, res, type, contrasts, 
                     coef, annotCols, robust, sort.by){
  
  temp <- MEAL::getProbeResults(res, fNames=annotCols, coef = coef, 
                                contrast = contrasts, robust = robust, sort.by = sort.by)
  if(any(class(temp) == 'simpleError')){stop(paste(temp))}
  if(type == 1){
    if(inherits(Set, "ExpressionSet")){
      Set.counts <- Biobase::exprs(Set)
    }
    else if (inherits(Set, c("SummarizedExperiment","RangedSummarizedExperiment"))){
      Set.counts <- SummarizedExperiment::assay(Set)
    }
  }
  n <- apply(Set.counts, 1, function(x) sum(!is.na(x)))
  ans <- tibble::as_tibble(temp) %>% tibble::add_column(.before=1, id=rownames(temp)) %>%
    tibble::add_column(.after = 1, n=n) %>% dplyr::rename("beta" = "logFC") %>%
    dplyr::select(id, tail(names(.), length(annotCols)), everything()) %>%
    dplyr::select(id, n, beta, SE, t, P.Value, adj.P.Val, annotCols)
  return(ans)
}