clean up

Author: KarinSchork

R/PCA_helper.R

@@ -1,6 +1,6 @@
 #' A method for filtering the data for PCA.
-#' 
-#' 
+#'
+#'
 #' @param D                \strong{data.frame} \cr
 #'                         The data set containing intensities of the sample.
 #' @param impute           \strong{logical} \cr
@@ -9,23 +9,26 @@
 #'                         The imputation method. Options are "mean" or "median".
 #' @param propNA           \strong{numeric} \cr
 #'                         The proportion of allowed missing NAs for a protein, before it is discarded.
-#' 
-#' 
+#'
+#'
 #' @return The filtered data.
-#' 
+#' @examples
+#'
+#'
+#'
 
 filter_PCA_data <- function(D, impute = FALSE, impute_method = "mean", propNA = 0){
-  
+
   # proportion if missing values per protein
   mean_NA <- apply(D, 1, function(x) mean(is.na(x)))
 
   ### remove rows with too many missing values
   D <- D[mean_NA <= propNA, ]
-  
+
   if (nrow(D) == 0){
     return(NULL)
   }
-  
+
   ## perform imputation
   if (impute) {
     D <- as.data.frame(t(apply(D, 1, function(x) {
@@ -35,20 +38,20 @@ filter_PCA_data <- function(D, impute = FALSE, impute_method = "mean", propNA =
       }
       return(x)
     })))
-    
-    
+
+
   } else {
     D <- stats::na.omit(D)
     if (nrow(D) == 0){
       return(NULL)
     }
   }
-  
+
   ### remove proteins/peptides with an (almost) constant value (variance near zero)
   v <- matrixStats::rowVars(as.matrix(D))
   ind_zeroVar <- which(v < 1e-25)
   if (length(ind_zeroVar) > 0) D <- D[-ind_zeroVar,]
-  
+
   return(D)
 }
 

R/WIP_Clustering_Heatmap_Lineplots.R

@@ -10,7 +10,7 @@
 #' @param dist_method distance method for clustering, default is "correlation"
 #'
 #' @return save heatmap and data frame with cluster information, as well as line plots
-#' @export
+#'
 #'
 #' @examples # TODO
 Clustering_heatmap_lineplots2 <- function(D, id, output_path, suffix = "", nr_clusters = NULL, row_split = TRUE, dist_method = "correlation") {

R/automatedNormalization.R

@@ -13,56 +13,57 @@
 #'
 #' @return The normalized data as well as a message.
 #' @export
+#' @examples
 #'
 
-automatedNormalization <- function(DATA, 
-                                   method = "median", 
+automatedNormalization <- function(DATA,
+                                   method = "median",
                                    is_log_transformed = TRUE,
                                    log_base = 2,
                                    lts.quantile = 0.8){
-  
+
   mess <- ""
-  
+
   if(method == "loess" | method == "quantile" | method == "median"){
-    
+
     #### choose normalization function
     fun <- switch(method,
                   "loess" = limma::normalizeBetweenArrays,
                   "quantile" = limma::normalizeBetweenArrays,
                   "median" = limma::normalizeBetweenArrays)
-    
+
     ### choose arguments for normalization function
     args <- switch(method,
                    "loess" = list(object = DATA, method = "cyclicloess"),
                    "quantile" = list(object = DATA, method = "quantile"),
                    "median" = list(object = DATA, method = "scale"))
-    
- 
+
+
     DATA_norm <- do.call(fun, args)
     DATA_norm <- as.data.frame(DATA_norm)
-    
+
     mess <- paste0(mess, "Data successfully ", method ," normalized. \n")
-    
+
   }
-  
+
   if (method == "lts") {
-    
+
     ### reverse log-transformation, if data is log-transformed
-    if(is_log_transformed){     
+    if(is_log_transformed){
       DATA <- log_base^DATA
     }
-    
+
     DATA_norm <- vsn::vsn2(as.matrix(DATA), lts.quantile = lts.quantile)
     DATA_norm <- DATA_norm@hx
     DATA_norm <- as.data.frame(DATA_norm)
     mess <- paste0(mess, "Data successfully lts normalized. \n")
   }
-  
+
   if (method == "nonorm") {
     DATA_norm <- DATA
     mess <- paste0(mess, "Data successfully not normalized. \n")
   }
-  
+
   return(list("data" = DATA_norm, "message" = mess))
-  
-}
+
+}

R/workflow_ANOVA.R

@@ -1,8 +1,3 @@
-
-
-
-
-
 #' The workflow for ANOVA of quantitative proteomics data
 #'
 #' @param data_path              \strong{character} \cr