test: split limma

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: dsOmics
 Type: Package
 Title: DataSHIELD server site Omic functions
-Version: 1.0.17.9000
+Version: 1.0.17.9001
 Date: 2021-07-08
 Authors@R: c(person("Yannick", "Marcon", , "[email protected]", role = "aut"),
   person("Xavier", "Esriba-Montagut", , "[email protected]", role = "aut"),
@@ -38,7 +38,7 @@ AggregateMethods:
    nSamplesDS,
    nFeaturesDS,
    tablePhenoDS,
-   limmaDS,
+   limmaDS2,
    getVariable,
    getChromosomeNamesDS,
    GWASDS,
@@ -65,6 +65,7 @@ AssignMethods:
    RNAseqPreprocDS,
    PRSDS,
    PRSDS_aux,
+   limmaDS,
    make_valid_namesDS,
    fastGWAS_S,
    fastGWAS_getFitted.values,

R/limmaDS.R

@@ -100,21 +100,22 @@ limmaDS <- function(Set, variable_names, covariable_names, type, contrasts,
                            variable_names = variable_names,
                            covariable_names = covariable_names,
                            sva=sva, method = method, big = big)
-  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)
+  return(res)
+  # 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)
 }

R/limmaDS2.R

@@ -0,0 +1,49 @@
+#'
+#' @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)
+}