diff --git a/.Rbuildignore b/.Rbuildignore
index 5719857..655e8af 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -2,4 +2,6 @@
 ^\.git$
 ^\.gitignore$
 ^\.lintr$
-^\.travis.yml$
\ No newline at end of file
+^\.travis.yml$
+^.*\.Rproj$
+^\.Rproj\.user$
diff --git a/.gitignore b/.gitignore
index 07e3a03..5336c35 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,5 @@
 *.swp
 *.so
 *.o
-*.rds
\ No newline at end of file
+*.rds
+.Rproj.user
diff --git a/DESCRIPTION b/DESCRIPTION
index 68e9b47..1d27c0d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -39,4 +39,4 @@ Description: MetNet contains functionality to infer metabolic network topologies
   the two matrices are combined to form a adjacency matrix inferred from both quantitative 
   and structure information. 
 License: GPL (>= 3)
-RoxygenNote: 6.1.1
+RoxygenNote: 7.1.1
diff --git a/MetNet.Rproj b/MetNet.Rproj
new file mode 100644
index 0000000..21a4da0
--- /dev/null
+++ b/MetNet.Rproj
@@ -0,0 +1,17 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
diff --git a/NAMESPACE b/NAMESPACE
index 58fd65e..abacebb 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
+export(adjacency_list)
+export(annotaionNames)
 export(aracne)
 export(bayes)
 export(clr)
@@ -11,7 +13,9 @@ export(randomForest)
 export(rtCorrection)
 export(statistical)
 export(structural)
+export(summary_mz)
 export(threshold)
+export(threshold_p)
 importFrom(BiocParallel,bplapply)
 importFrom(GENIE3,GENIE3)
 importFrom(bnlearn,arcs)
diff --git a/R/combine.R b/R/combine.R
index 8856329..f44036e 100644
--- a/R/combine.R
+++ b/R/combine.R
@@ -11,6 +11,7 @@
 #' in the returned when the sum exceeds the `threshold`.
 #' \code{combine} returns this consensus matrix supported
 #' by the structural and statistical adjacency matrices.
+#' 
 #'
 #' @param structural list containing `numeric` structural adjacency matrix in
 #' the first entry and `character` structural adjanceny matrix in the second
@@ -20,6 +21,11 @@
 #'
 #' @param threshold numeric, threshold value to be applied to define a
 #' connection as present
+#' 
+#' @param model character, defines model that is used for building the consensus 
+#' matrix. The model is part of `statistical`. The default is "Consensus" which creates
+#' the consensus matrix by using a combination of models present in `statistical`. 
+#' Besides, "pearson" and "spearman" models were implemented.
 #'
 #' @details The matrices will be added and a unweighted connection will
 #' be reported when the value exceeds a certain value.
@@ -31,6 +37,7 @@
 #' `character` the corresonding type/putative link at this position.
 #'
 #' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
+#' Liesa Salzer, \email{liesa.salzer@@helmholtz-muenchen.de}
 #'
 #' @examples
 #' data("x_test", package = "MetNet")
@@ -50,191 +57,95 @@
 #' combine(struct_adj, stat_adj)
 #'
 #' @export
-#' #' Changes to MetNet: New attributes added, if model = "combined" (default) the result will be the same 
-#' as in MetNet, except the output list items were named to "combined" and "Character" 
-#' if model = "pearson" or "spearman" than also the corresponding weighted statistical 
-#' adjacency matrix is required as attribute (weighted_statistical = XY)
-#' The output in this case will be a list containing 4 listitems, where combination relies on the
-#' unweighted adjacency matrix of either Pearson or Spearman. 
-#' Moreover corresponding Correlation and p-values will be displayes as listitems
-combine <- function(structural, statistical, threshold = 1, model = "combined", weighted_statistical) {
-    
-    ## Is changed since structural list is now lenght 3
-    if (!is.list(structural) | length(structural) != 3)
-        stop("structural is not a list of length 3")
-    
-    if (!is.matrix(structural[[1]]) | !is.numeric(structural[[1]]))
-        stop("structural[[1]] is not a numeric matrix")
-    
-    if (!is.matrix(structural[[2]]) | !is.character(structural[[2]]))
-        stop("structural[[2]] is not a character matrix")
-    
-    ## Additional to MetNet:
-    if (!is.matrix(statistical[[3]]) | !is.numeric(statistical[[3]]))
-        stop("statistical is not a numeric matrix")
-    
-    if (!all(rownames(structural[[1]]) == rownames(structural[[2]])))
-        stop(c("rownames of structural[[1]] are not identical to rownames of ",
-               "structural[[2]]"))
-    
-    if (!all(colnames(structural[[1]]) == colnames(structural[[2]])))
-        stop(c("colnames of structural[[1]] are not identical to colnames of ",
-               "structural[[2]]"))
-    
-    if (!all(rownames(structural[[1]]) == rownames(statistical)))
-        stop("rownames are not identical")
-    
-    if (!all(colnames(structural[[1]]) == colnames(statistical)))
-        stop("colnames are not identical")
-    
-    if (!is.numeric(threshold)) stop("threshold is not numeric")
-    
-    ## create list to store results
-    res <- list()
-    
-    
-    ## Changes to MetNet: Distinguish between default (model == "combined") and model = "pearson" or "spearman"
-    if(model == "pearson"){
-        
-        ## create the first entry of the list
-        ## sum the matrices structural and statistical, if the value is above
-        ## threshold then assign 1, otherwise 0
-        cons_num <- structural[[1]] + statistical[["pearson"]]
-        cons_num <- ifelse(cons_num > threshold, 1, 0)
-        
-        ## if p-values have previously been calculated
-        if("Correlation Value" %in% names(weighted_statistical[["pearson"]][1])){
-            cons_corr <- ifelse(cons_num == 1, weighted_statistical[["pearson"]][["Correlation Value"]], "")
-            cons_p <- ifelse(cons_num == 1, weighted_statistical[["pearson"]][["p-Value"]], "")}
-        else {
-            cons_corr <- ifelse(cons_num == 1, weighted_statistical[["pearson"]], "")
-            cons_p <- NaN
-        }}
-    
-    if(model == "spearman"){
-        ## create the first entry of the list
-        ## sum the matrices structural and statistical, if the value is above
-        ## threshold then assign 1, otherwise 0
-        cons_num <- structural[[1]] + statistical[["spearman"]]
-        cons_num <- ifelse(cons_num > threshold, 1, 0)
-        
-        ## if p-values have previously been calculated
-        if("Correlation Value" %in% names(weighted_statistical[["spearman"]][1])){
-            cons_corr <- ifelse(cons_num == 1, weighted_statistical[["spearman"]][["Correlation Value"]], "")
-            cons_p <- ifelse(cons_num == 1, weighted_statistical[["spearman"]][["p-Value"]], "")}
-        else {
-            cons_corr <- ifelse(cons_num == 1, weighted_statistical[["spearman"]], "")
-            cons_p <- NaN
-        }}
-    
-    
-    if(model == "combined"){
-        ## create the first entry of the list
-        ## sum the matrices structural and statistical, if the value is above
-        ## threshold then assign 1, otherwise 0
-        cons_num <- structural[[1]] + statistical[[3]]
-        cons_num <- ifelse(cons_num > threshold, 1, 0)
-        cons_corr <- NaN
-        cons_p <- NaN
-    }
-    
-    ## create the second entry of the list
-    ## if element in cons_num is equal to 1, take the element in structural[[2]]
-    ## (the type of link), otherwise ""
-    cons_char <- ifelse(cons_num == 1, structural[[2]], "")
-    
-    ## assign to list
-    ## Compared to MetNet names were assigned
-    res[[model]] <- cons_num
-    res[["Character"]] <- cons_char
-    
-    ## assign Correlation and p-values to list if model is "pearson" or "spearman"
-    if(model == "pearson" || model == "spearman"){
-        res[["Correlation Value"]] <- cons_corr
-        res[["p-Value"]] <- cons_p
-    }
-    
-    
-    return(res)
-}
-
+combine <- function(structural, statistical, threshold = 1, model = "Consensus") {
+  
+  
+  if (!is.matrix(structural[[1]]) | !is.numeric(structural[[1]]))
+    stop("structural[[1]] is not a numeric matrix")
+  
+  
+  if (!is.matrix(statistical[["Consensus"]]) | !is.numeric(statistical[["Consensus"]]))
+    stop("statistical is not a numeric matrix")
+  
+  if (!all(rownames(structural[[1]]) == rownames(structural[[2]])))
+    stop(c("rownames of structural[[1]] are not identical to rownames of ",
+           "structural[[2]]"))
+  
+  if (!all(colnames(structural[[1]]) == colnames(structural[[2]])))
+    stop(c("colnames of structural[[1]] are not identical to colnames of ",
+           "structural[[2]]"))
+  
+  if (!all(rownames(structural[[1]]) == rownames(statistical)))
+    stop("rownames are not identical")
+  
+  if (!all(colnames(structural[[1]]) == colnames(statistical)))
+    stop("colnames are not identical")
+  
+  if (!is.numeric(threshold)) stop("threshold is not numeric")
+  
+  ## create list to store results
+  res <- list()
+  
+  
 
-#' exportNet2gml is a function that exports adjacency matrices to gml using igraph
-#' Needs following attributes:
-#' x: adjacency matrix that needs to be exported
-#' from: originated from wich function, possible values are 
-#' - "structural" Produces a gml file with edge attributes containing mass difference values, data saved as "structural_type.gml"
-#' - "statistical+p" produces a gml file with edge attributes containing correlation values and p-values, saved as "statistical.'model'.gml"
-#' (TO DO: ADD "statistical")
-#' - "combine" produces a gml file with edge attributes containing correlation and p-values for pearson/ spearman correlations, 
-#' saved as "combined.gml"
-exportNet2gml <- function (x, from, adjacency_list, ...) {
-    #Check arguments
-    if (!from %in% c("structural", "statistical+p", "combine"))
-        stop("type not in 'structural', 'statistical+p', 'combine'")
-    
-    if ("structural" %in% from) {
-        
-        mat <- x[[1]]
-        net       <- 
-            graph_from_adjacency_matrix(mat, mode = "undirected")
-        E(net)$sourceID <-  as.character(adjacency_list$`Var1`)
-        E(net)$targetID <-  as.character(adjacency_list$`Var2`)
-        E(net)$`mass difference` <-  adjacency_list$`mass difference`
-        
-        write_graph(net, "structural.gml", format = c("gml"))
-    }
-    else if ("statistical+p" %in% from) {
-        
-        for (i in 1:length(x)) {
-            cor_list <- x[[i]]
-            ##Plot structural adjacency matrix and export to gml
-            net_cor <-
-                igraph::graph_from_adjacency_matrix(cor_list[[1]], mode = "undirected", weighted = T)
-            net_p   <-
-                igraph::graph_from_adjacency_matrix(cor_list[[2]], mode = "undirected", weighted = T)
-            net_comb <- union(net_cor, net_p)
-            names(edge_attr(net_comb))[1] <- "correlation"
-            names(edge_attr(net_comb))[2] <- "p"
-            # #net_plot <- plot(net_type, edge.width = 5, vertex.label.cex = 0.5, edge.color = "grey")
-            q <- names(x[i])
-            #E(net_cor)$`correlation_tst` <-  adjacency_list[,"pearson"]
-            
-            # rownames(cor_list[[1]])
-            write_graph(net_comb, file = sprintf('statistical.%s.gml', q), format = c("gml"))
-            
-        }
-        
-        
-        net <- graph_from_data_frame(adjacency_list, directed = TRUE, vertices = NULL)
-        E(net)$sourceID <-  as.character(adjacency_list$`Feature1`)
-        E(net)$targetID <-  as.character(adjacency_list$`Feature2`)
-        write_graph(net, file = "statistical.gml", format = c("gml"))
-        
-        
-    }
-    
-    else if ("combine" %in% from) {
-        if ("pearson" %in% names(x[1]) | "spearman" %in% names(x[1]) ){
-            net <- graph_from_adjacency_matrix(x[[1]], mode = "undirected")
-            E(net)$sourceID <-  as.character(adjacency_list$`Var1`)
-            E(net)$targetID <-  as.character(adjacency_list$`Var2`)
-            E(net)$massdifference <-  adjacency_list$`value`
-            E(net)$correlation <-  adjacency_list$`Correlation Value`
-        }
-        else { #if "combined" or other model
-            net       <- 
-                graph_from_adjacency_matrix(x[[1]], mode = "undirected", weighted = T)
-            
-        }
-        write_graph(net, "combined.gml", format = c("gml"))
-        
-        
-    }
+  ## create the first entry of the list
+  ## sum the matrices structural and statistical, if the value is above
+  ## threshold then assign 1, otherwise 0
+  cons_num <- structural[[1]] + statistical[[model]]
+  cons_num <- ifelse(cons_num > threshold, 1, 0)
+  
+  ## create the second entry of the list
+  ## if element in cons_num is equal to 1, take the element in structural[[2]]
+  ## (the type of link), otherwise ""
+  cons_char <- ifelse(cons_num == 1, structural[[2]], "")
+  
+  ## assign to list
+  res[[1]] <- cons_num
+  res[[2]] <- cons_char
+  
+  return(res)
 }
 
-#' adjacency_list is a function that creates a list of an adjacency matrix x
-#' from: originated from which function, possiblie attributes are "structural", "statistical", "combine"
+
+#' @name adjacency_list
+#'
+#' @aliases adjacency_list
+#'
+#' @title Create a list from adjacency matrices
+#'
+#' @description
+#' The function `adjacency_list` creates a dataframe from the adjacency matrix.
+#' Unique values are displayed by the dataframe. Missing values and the upper triangle of 
+#' the adjacency matrix are left out.
+#' The function returns a dataframe containing different column outputs depending 
+#' on the source of the adjacency matrix, which is defined by `from`.
+#' 
+#' @param x
+#' `data.frame`, adjacency matrix, that has previously been generated 
+#' by the functions `structural`, `statistical`, or `combine`
+#' 
+#' @param from
+#' `character` defines the function from which the adjacency matrix originated.
+#' This can be either "structural", "statistical", or "combine".
+#'
+#' @details
+#' The function `adjacency_list` includes functionality to generate a dataframe from
+#' adjacency matrices. The output depends on the original function from which the adjacency
+#' matrix was generated. 
+#'
+#' @return `data.frame` containing the listed values each pair of features from 
+#' adjacency matrix `x` specified by `from`.
+#'
+#' @author Liesa Salzer, \email{liesa.salzer@@helmholtz-muenchen.de}
+#'
+#' @examples
+#' data("x_test", package = "MetNet")
+#' x <- x_test[1:10, 3:ncol(x_test)]
+#' x <- as.matrix(x)
+#' stat_adj <- statistical(x = x, model = c("pearson", "spearman"))
+#' adjacency_list(x = stat_adj, from = "statistical")
+#'
+#' @export
 adjacency_list <- function(x, from){
     
     if (!(all(from %in% c("structural", "statistical", "combine"))))
@@ -242,31 +153,46 @@ adjacency_list <- function(x, from){
     
     if ("structural" %in% from) {
         
+      # Checks if upper triangle of the adjacency matrix is the same as the lower one
+      # If yes, the upper triangle will be cleared
+      if (all(x[[2]][upper.tri(x[[2]])] == x[[2]][t(lower.tri(x[[2]]))]) == TRUE) {
         
         x[[2]][upper.tri(x[[2]])] <- ''
         x[[3]][upper.tri(x[[3]])] <- ''
-        
-        list_type <- melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '')
-        list_mass <- melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '')
-        combine <- add_column(list_type,  `mass difference`= list_mass$value) %>% as.data.frame()
+      }
+   
+      
+        list_type <- reshape2::melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '')
+        list_mass <- reshape2::melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '')
+        combine <- merge(list_type, list_mass, by = c("Var1", "Var2"))
+        colnames(combine) <- c("Var1", "Var2", "value", "mass-difference")
         return(combine)
+      
     }
+  
     else if ("statistical" %in% from) {
         
         for (i in seq_along(x)) {
             if (i == 1) {
+              if (all(x[[i]][upper.tri(x[[i]])] == x[[i]][t(lower.tri(x[[i]]))]) == TRUE) {
+                
                 x[[i]][upper.tri(x[[i]])] <- ''
-                list_corr <- melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '') %>% 
+              }  
+              
+                list_corr <- reshape2::melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '') %>% 
                     select(Var1, Var2, value) 
-                colnames(list_corr) <- c("Feature1", "Feature2", names(x[i]))
-                #return(list_corr)
+                colnames(list_corr) <- c("Var1", "Var2", names(x[i]))
             }
             if (i != 1){
                 model = names(x[i])
-                x[[i]][upper.tri(x[[i]])] <- ''
-                list_corr2 <- melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '')
-                list_comb <- add_column(list_corr, list_corr2$value)
-                list_comb <- as.data.frame(list_comb) 
+                
+                if (all(x[[i]][upper.tri(x[[i]])] == x[[i]][t(lower.tri(x[[i]]))]) == TRUE) {
+                  
+                  x[[i]][upper.tri(x[[i]])] <- ''
+                } 
+                
+                list_corr2 <- reshape2::melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '')
+                list_comb <- merge(list_corr, list_corr2, by = c("Var1", "Var2") )
                 colnames(list_comb)[i+2] <- c(names(x[i]))
                 list_corr <- list_comb
             } 
@@ -274,25 +200,73 @@ adjacency_list <- function(x, from){
         return(list_corr)
     }
     else if ("combine" %in% from){
+      if (all(x[[2]][upper.tri(x[[2]])] == x[[2]][t(lower.tri(x[[2]]))]) == TRUE) {
+        
         x[[2]][upper.tri(x[[2]])] <- ''
-        x[[3]][upper.tri(x[[3]])] <- ''
-        x[[4]][upper.tri(x[[4]])] <- ''
+      } 
+      
         
-        list_mass <- melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '')
-        list_corr <- melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '')
-        list_p    <- melt(x[[4]]) %>% filter(Var1 != Var2) %>% filter(value != '')
-        listed <- add_column(list_mass, `Correlation Value` = list_corr$value)
-        listed <- add_column(listed, `p-Value` = list_p$value)
-        return(listed)
+        list_mass <- reshape2::melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '')
+        colnames(list_mass) <- c("Var1", "Var2", "value")
+
+        return(list_mass)
         
     }
     
 }
 
-#' sum_mass summarises the adjacency list containing mass difference values, 
-#' i.e. either adjacency list from structural or combine may be used
-sum_mass <- function(adjacency_list){
-    
+
+#' @name summary_mz
+#'
+#' @aliases summary_mz
+#'
+#' @title Create a summary from adjacency list containing mass-differences with possible filter 
+#'
+#' @description
+#' The function `summary_mz` creates a summary from the adjacency list.
+#' Individual mass differences are count over all features. The input may be 
+#' adjacency lists originating from the function `structural`,
+#' or `combine`. The parameter `filtered` will define if data will be 
+#' filtered above a certain threshold or not. 
+#' 
+#' @param adjacency_list
+#' `data.frame`, a list of the mass-difference adjacency mateix, that has previously 
+#' been generated by the function `adjacency_list(x, from = "statistical")`or 
+#' `adjacency_list(x, from = "combine")`
+#' 
+#' @param filter
+#' `number`/`FALSE`, leave empty or set to `FALSE` if unfiltered data are 
+#' required. Select a `number` as a threshold where mz differences were filtered.
+#' May be usefull at visualization (plotting) for big data. 
+#' 
+#'
+#' @details
+#' Summarises the adjacency list containing mass difference values, 
+#' i.e. either adjacency list from structural or combine may be used.
+#' Also the plots will be displayed. 
+#' The default is filter = F, so the unfiltered summary will be returned. 
+#' If filter is set to a `number`, e.g. 1000 only mz differences above 
+#' this threshold will be displayed. 
+#' The function can be applied for adjacency lists from `structural` and `combine`
+#' 
+#' @return 
+#' `data.frame` containing the numbers of present mz differences and
+#' corresponding name. Also a plot will be displayed.
+#'
+#' @author Liesa Salzer, \email{liesa.salzer@@helmholtz-muenchen.de}
+#'
+#' @examples
+#' data("x_test", package = "MetNet")
+#' x <- x_test[1:10, 3:ncol(x_test)]
+#' x <- as.matrix(x)
+#' stat_adj <- statistical(x = x, model = c("pearson", "spearman"))
+#' adj_l <- adjacency_list(x = stat_adj, from = "statistical")
+#' summary_mz(adjacency_list = adj_l) 
+#' summary_mz(adjacency_list = adj_l, filter = 100) # filtered
+#'
+#'
+#'@export
+summary_mz <- function(adjacency_list, filter = F, ...){
     if("mass difference" %in% names(adjacency_list)){
         sum_mass <- adjacency_list %>% group_by(`mass difference`) %>% summarise(count=n()) %>%
             as.data.frame()
@@ -307,10 +281,98 @@ sum_mass <- function(adjacency_list){
         
     }
     
-    plot_list <- ggplot(sum_comb, aes(x=Type, y=Counts, fill=Type)) + geom_bar(stat = "identity") + theme_minimal() + 
-        labs(title = "Numbers of destinct type of a biochemical reaction")+ scale_fill_brewer(palette = "Blues") + theme(legend.position = "right")
-    plot(plot_list)
     
-    return(sum_comb)
+    if (filter == F) {
+        plot_list <- ggplot(sum_comb, aes(x=Type, y=Counts)) + 
+            geom_bar(stat = "identity") + 
+            theme_minimal() + 
+            coord_flip() + 
+            labs(title = "Numbers of determined mass differences")
+        
+        plot(plot_list)
+        
+        return(sum_comb)
+    }
+    
+    else if (filter != F) {
+        sum_f <- filter(sum_comb,sum_comb$Counts >= filter)
+        plot_list_f <- ggplot(sum_f, aes(x=Type, y=Counts)) + 
+            geom_bar(stat = "identity") + 
+            theme_minimal() + 
+            coord_flip() + 
+            labs(title = "Numbers of determined mass differences", 
+                 subtitle = "(filtered)")
+        
+        plot(plot_list_f)
+        
+        return(sum_f)
+        
+    }
+    
+   
 }
 
+
+
+
+
+
+#' @name annotaionNames 
+#'
+#' @aliases annotaionNames 
+#'
+#' @title Add annotations to adjacency list
+#'
+#' @description
+#' The function `annotationNames` adds available annotation values for all features
+#' to the adjacency list. 
+#' 
+#' @param
+#' `list` is the adjacency list, and `names` is a dataframe containing the feature names as rows,
+#' mz values, RT values and annotations in columns
+#' 
+#'
+#' @details
+#' annotaionNames adds annotation to an adjacency list as additional column. 
+#'
+#' @return `data.frame` containing the the `list`-values and additional column with annotations
+#'
+#' @author Liesa Salzer, \email{liesa.salzer@@helmholtz-muenchen.de}
+#'
+#' @examples
+#' data("x_test", package = "MetNet")
+#' x <- x_test[1:10, 3:ncol(x_test)]
+#' x <- as.matrix(x)
+#' stat_adj <- statistical(x = x, model = c("pearson", "spearman"))
+#' adj_l <- adjacency_list(x = stat_adj, from = "statistical")
+#' annotationNames(list = adj_l, names = annotationFile)
+#' 
+#' @export
+annotaionNames <- function (list, names) {
+    Var1_names <- c()
+    
+    for (i in 1:length(list$Var1)) {
+        
+        
+        value <- names[row.names(names) == list$Var1[i], "manualAnnotation"]
+        value <- as.character(value)
+        Var1_names <- c(Var1_names, value)
+        
+    }  
+    Var2_names <- c()
+    
+    for (i in 1:length(list$Var2)) {
+        
+        
+        value <- names[row.names(names) == list$Var2[i], "manualAnnotation"]
+        value <- as.character(value)
+        Var2_names <- c(Var2_names, value)
+        
+    }
+    
+    
+    list$Var1_annotation <- Var1_names
+    list$Var2_annotation <- Var2_names
+    
+    return(list)
+}
\ No newline at end of file
diff --git a/R/statistical.R b/R/statistical.R
index 641b87a..afcc464 100644
--- a/R/statistical.R
+++ b/R/statistical.R
@@ -355,15 +355,23 @@ correlation <- function(x, type = "pearson", use = "pairwise.complete.obs") {
 
 #' correlation_p is an additional function (complementary to correlation()), and needed for positive/negative pearson/spearman
 #' correlation value and p-value calculation
+#' @details dependencies: Hmisc package
 correlation_p <- function(x, type = "pearson", use = "pairwise.complete.obs") {
     
     ## for pearson/spearman correlation
-    if (type %in% c("pearson", "spearman")) {
-        cor_list <- rcorr(x = t(x), type = type)
+    if (type %in% c("spearman")) {
+        cor_list <- Hmisc::rcorr(x = t(x), type = type)
+        names(cor_list) <- c("spearman_corr", "spearman_n", "spearman_p")
+        
+    }
+    ## for pearson/spearman correlation
+    if (type %in% c("pearson")) {
+        cor_list <- Hmisc::rcorr(x = t(x), type = type)
+        names(cor_list) <- c("pearson_corr", "pearson_n", "pearson_p")
+        
     }
     
     
-    names(cor_list) <- c("Correlation Value", "n", "p-Value")
     ## exclude "n" column
     cor_list <- cor_list[-2] 
     
@@ -531,6 +539,10 @@ addToList <- function(l, name, object) {
 #' semipartial), Spearman correlation (also partial and semipartial)
 #' and score-based structure learning (Bayes). The function returns a
 #' list of adjacency matrices that are defined by `model`.
+#' If `p` = TRUE and `model` == "pearson" or "spearman", than the output 
+#' contains pearson/spearman positive and negative correlation values and
+#' an additional list entry with their corresponding p-values
+#' (saved as "pearson_p" or "spearman_p")
 #'
 #' @param
 #' x `matrix` that contains intensity values of
@@ -541,6 +553,10 @@ addToList <- function(l, name, object) {
 #' (`"lasso"`, `"randomForest"`, `"clr"`, `"aracne"`, `"pearson"`,
 #' `"pearson_partial"`, `"pearson_semipartial"`, `"spearman"`,
 #' `"spearman_partial"`, `"spearman_semipartial"`, `"bayes"`)
+#' 
+#' @param 
+#' p `logical` by default set to FALSE, if set to TRUE p-values will
+#' be calculated, works only for `model` "pearson" or "spearman"
 #'
 #' @param
 #' ... parameters passed to the functions  `lasso`, `randomForest`,
@@ -567,9 +583,10 @@ addToList <- function(l, name, object) {
 #' will be used in `lasso`, `clr` and/or `aracne`.
 #'
 #' @return `list` containing the respective adjacency matrices specified by
-#' `model`
+#' `model` and `p`
 #'
 #' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
+#' Liesa Salzer, \email{liesa.salzer@@helmholtz-muenchen.de}
 #'
 #' @examples
 #' data("x_test", package = "MetNet")
@@ -578,10 +595,6 @@ addToList <- function(l, name, object) {
 #' statistical(x = x, model = c("pearson", "spearman"))
 #'
 #' @export
-#'Changes to MetNet:
-#' additional attribute in funtion: p; default is FALSE (so works like MetNet), if p is TRUE and model is spearman/pearson
-#' than the output will contain lists of pearson/spearman containing the corresponding correlation values (INCLUDING positive 
-#' and negative values) and p-values
 statistical <- function(x, model, p = FALSE, ...) {
     
     ## check if model complies with the implemented model and return error
@@ -594,6 +607,14 @@ statistical <- function(x, model, p = FALSE, ...) {
     ## check if x is numeric matrix and return error if not so
     if (mode(x) != "numeric") stop("x is not a numerical matrix")
     
+    if (TRUE %in% p){
+        #if (model != "spearman" || model != "pearson")
+           # stop("'model' not implemented in statistical(p = TRUE)")
+        
+        if (!(all(model %in% c("pearson", "spearman"))))
+            stop("'model' not implemented in statistical")
+    }
+    
     ## z-scale x and transpose
     x_z <- apply(x, 1, function(y) {
         (y - mean(y, na.rm = TRUE)) / sd(y, na.rm = TRUE)
@@ -702,7 +723,8 @@ statistical <- function(x, model, p = FALSE, ...) {
         #diag(pearson) <- NaN
         diag(pearson[[1]]) <- NaN
         diag(pearson[[2]]) <- NaN
-        l <- addToList(l, "pearson", pearson)
+        l <- addToList(l, object = pearson[[1]], name = "pearson")
+        l <- addToList(l, object = pearson[[2]], name = "pearson_p")
         print("pearson finished.")
     }
     
@@ -713,7 +735,9 @@ statistical <- function(x, model, p = FALSE, ...) {
         #diag(spearman) <- NaN
         diag(spearman[[1]]) <- NaN
         diag(spearman[[2]]) <- NaN
-        l <- addToList(l, "spearman", spearman)
+        
+        l <- addToList(l, object = spearman[[1]], name = "spearman")
+        l <- addToList(l, object = spearman[[2]], name = "spearman_p")
         print("spearman finished.")
     }
     
@@ -850,11 +874,14 @@ getLinks <- function(mat, exclude = "== 1") {
 #' `statistical` is taken.
 #' For `type = "mean"`, the average rank in `statistical` is taken.
 #' Subsequently the first `n` unique ranks are returned.
+#' 
 #'
 #' @return `matrix`, binary adjacency matrix given the links supported by the
 #' `type` and the `args`
 #'
-#' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
+#' @author Thomas Naake, \email{thomasnaake@@googlemail.com},
+#' Liesa Salzer, \email{liesa.salzer@@helmholtz-muenchen.de}
+#' 
 #' @examples
 #' data("x_test", package = "MetNet")
 #' x <- x_test[1:10, 3:ncol(x_test)]
@@ -877,211 +904,279 @@ getLinks <- function(mat, exclude = "== 1") {
 #' ## type = "mean"
 #' threshold(statistical = l, type = "mean", args = args)
 #' @export
-#'Changes to MetNet: new attribute for type: "threshold_p"
-#'A list is created instead of a single matrix as output containing 1/0 assigned values of 
-#'model matrices (e.g. pearson and spearman) and consensus matrix.
-#'
-#'If "treshold_p" is selected in 'type' all values are assigned to 1 if their p-value 
-#'is BELOW a defined threshold (defined in'args')
-#'If "treshold" is selected in 'type' all values are assigned to 1 if their Correlation-value 
-#'is ABOVE a defined threshold (defined in'args')
 threshold <- function(statistical, type, args,
                       values = c("all", "min", "max"), ...) {
+  
+  ## checks if p values are included in list and deletes them
+  if ( TRUE == any(endsWith(names(statistical), "_p"))) {
+    l <- statistical[!endsWith(names(statistical), "_p")]
+  }
+  
+  else {l <- statistical}
+  
+  
+  ## args, either N for tops
+  ## args, either N for tops
+  ## or a list of threshold
+  if (any(duplicated(names(args)))) {
+    stop("names(args) contain duplicated entries")
+  }
+  
+  if (!type %in% c("top1", "top2", "mean", "threshold"))
+    stop("type not in 'top1', 'top2', 'mean', 'threshold'")
+  
+  ## check args
+  if (type %in% c("threshold")) {
+    if (!(all(names(l) %in% names(args)))) {
+      stop("'args' does not contain entries for all 'model's in ",
+           "'statistical'")
+    }
     
-    l <- statistical
-    ## args, either N for tops
-    ## or a list of threshold
-    if (any(duplicated(names(args)))) {
-        stop("names(args) contain duplicated entries")
+    if (!"threshold" %in% names(args) && length(args$threshold) != 1) {
+      stop("'args' does not contain entry 'threshold' of length 1")
     }
+  }
+  
+  ## check match.arg for values
+  values <- match.arg(values)
+  
+  
+  if (type %in% c("top1", "top2", "mean")) {
+    if (!("n"  %in% names(args) && length(args$n) == 1 &&
+          is.numeric(args$n)))
+      stop("args does not contain the numeric entry `n` of length 1")
+  }
+  
+  if (type == "threshold") {
+    ## iterate through the list and remove the links below or above the
+    ## threshold and write to list
+    l <- lapply(seq_along(l), function(x) {
+      
+      
+      
+      ## find corresponding model in l
+      name_x <- names(l)[x]
+      
+      ## get corresponding threshold in args
+      threshold_x <- args[[names(l)[x]]]
+      
+
+        ## get corresponding adjacency matrix in l
+        ## corresponds to MetNet function
+        l_x <- l[[name_x]]
+        ## for pearson/spearman correlation models (incl. partial and
+        ## semi-partial), lasso, randomForest, clr, aracne and bayes higher
+        ## values corresond to higher confidence
+        ## only assign 1 to values that are above the threshold
+        ifelse(abs(l_x) > threshold_x, 1, 0)
     
-    ##Changes to MetNet: new attribute for type: "threshold_p"
-    if (!type %in% c("top1", "top2", "mean", "threshold", "threshold_p"))
-        stop("type not in 'top1', 'top2', 'mean', 'threshold', 'threshold_p'")
+      
+    })
     
-    ## check args
-    if (type %in% c("threshold")) {
-        if (!(all(names(l) %in% names(args)))) {
-            stop("'args' does not contain entries for all 'model's in ",
-                 "'statistical'")
-        }
-        
-        if (!"threshold" %in% names(args) && length(args$threshold) != 1) {
-            stop("'args' does not contain entry 'threshold' of length 1")
-        }
-    }
+    ## allow for compatibility of arguments
+    ## calculate consenses from the binary matrices
+    cons <- threeDotsCall(sna::consensus, dat = l, ...)
     
-    ## check args
-    if (type %in% c("threshold")) {
-        if (!(all(names(l) %in% names(args)))) {
-            stop("'args' does not contain entries for all 'model's in ",
-                 "'statistical'")
-        }
+    ## threshold consensus that it is a binary matrix
+    cons <- ifelse(cons >= args$threshold, 1, 0)
+    
+    rownames(cons) <- colnames(cons) <- colnames(l[[1]])
+    
+  }
+  
+  else { ## if type is in "top1", "top2" or "mean"
+    l_df <- lapply(seq_along(l), function(x) {
+      
+      ## find corresponding model in l
+      name_x <- names(l)[x]
+      
+      ## get corresponding adjacency matrix in l
+      l_x <- l[[name_x]]
+      
+      ## take the respective minimum or maximum depending on `values`,
+      ## do not do anything if `values` is equal to `all`
+      if (values %in% c("min", "max")) {
         
-        if (!"threshold" %in% names(args) && length(args$threshold) != 1) {
-            stop("'args' does not contain entry 'threshold' of length 1")
-        }
-    }
-    ## complementary to "threshold":
-    if (type %in% c("threshold_p")) {
-        if (!(all(names(l) %in% names(args)))) {
-            stop("'args' does not contain entries for all 'model's in ",
-                 "'statistical'")
-        }
+        ## get values from the lower triangle
+        lower_tri <- l_x[lower.tri(l_x)]
+        
+        ## get values from the upper triangle (requires transposing)
+        l_x_t <- t(l_x)
+        upper_tri <- l_x_t[lower.tri(l_x_t)]
         
-        if (!"threshold_p" %in% names(args) && length(args$threshold) != 1) {
-            stop("'args' does not contain entry 'threshold' of length 1")
+        ## get min of lower_tri and upper_tri
+        if (values == "min") {
+          values <- apply(rbind(lower_tri, upper_tri), 2, min)
+        } else {
+          values <- apply(rbind(lower_tri, upper_tri), 2, max)
         }
-    }
+        
+        ## write back to the matrix
+        l_x[lower.tri(l_x)] <- values
+        l_x <- t(l_x)
+        l_x[lower.tri(l_x)] <- values
+      }
+      
+      ## for pearson/spearman correlation (incl. partial and
+      ## semi-partial), lasso, randomForest, clr, aracne and bayes
+      ## higher values corresond to higher confidence
+      if (grepl(name_x, pattern = "lasso|randomForest|bayes")) {
+        ## set values that are equal to 0 to NaN (values that are 0)
+        ## do not explain the variability
+        res <- getLinks(l_x, exclude = "== 0")
+      }
+      if (grepl(name_x, pattern = "pearson|spearman|clr|aracne")) {
+        res <- getLinks(l_x, exclude = NULL)
+      }
+      
+      res
+    })
     
-    ## check match.arg for values
-    values <- match.arg(values)
+    names(l_df) <- names(l)
     
-    if (type %in% c("top1", "top2", "mean")) {
-        if (!("n"  %in% names(args) && length(args$n) == 1 &&
-              is.numeric(args$n)))
-            stop("args does not contain the numeric entry `n` of length 1")
-    }
+    ## bind together the ranks of the models, stored in l_df
+    ranks <- lapply(l_df, function(x) x$rank)
+    ranks <- do.call("cbind", ranks)
+    colnames(ranks) <- names(l_df)
     
+    ## calculate the consensus information, i.e. either get the first or
+    ## second top rank per row or calculate the average across rows
+    ## depending on the type argument
+    cons_val <- topKnet(ranks, type)
     
+    ## bind row and col information with cons information
+    row_col <- l_df[[1]][, c("row", "col")]
+    ranks <- cbind(row_col, cons_val)
     
+    ## get the top N features
+    n <- args$n
+    top_n <- sort(unique(cons_val))[1:n]
+    ranks_top <- ranks[cons_val %in% top_n, ]
     
-    if (type == "threshold" || type == "threshold_p") {
-        ## iterate through the list and remove the links below or above the
-        ## threshold and write to list
-        l <- lapply(seq_along(l), function(x) {
-            
-            ## find corresponding model in l
-            name_x <- names(l)[x]
-            
-            ## get corresponding threshold in args
-            threshold_x <- args[[names(l)[x]]]
-            
-            ## Changed to MetNet
-            if ("threshold" %in% type) {
-                
-                if("Correlation Value" %in% names(l[[name_x]][1])) {
-                    ## get corresponding adjacency matrix of Correlation Values in l
-                    ## is used when statistical was calculated with p=TRUE
-                    l_x <- l[[name_x]]$`Correlation Value`
-                    
-                    ## only assign 1 to values that are above the threshold
-                    ifelse(l_x > threshold_x, 1, 0)
-                } 
-                else{
-                    ## get corresponding adjacency matrix in l
-                    ## corresponds to MetNet function
-                    l_x <- l[[name_x]]
-                    ## for pearson/spearman correlation models (incl. partial and
-                    ## semi-partial), lasso, randomForest, clr, aracne and bayes higher
-                    ## values corresond to higher confidence
-                    ## only assign 1 to values that are above the threshold
-                    ifelse(l_x > threshold_x, 1, 0)
-                }
-            }
-            else if("threshold_p" %in% type){
-                ## get corresponding adjacency matrix of p-Values in l
-                ## is used when statistical was calculated with p=TRUE
-                l_x <- l[[name_x]]$`p-Value`
-                ## only assign 1 to values that are below the threshold
-                ifelse(l_x < threshold_x, 1, 0)
-            }      
-            
-        })
-        
-        ## allow for compatibility of arguments
-        ## calculate consenses from the binary matrices
-        cons <- threeDotsCall(sna::consensus, dat = l, ...)
-        
-        ## threshold consensus that it is a binary matrix
-        cons <- ifelse(cons >= args$threshold, 1, 0)
-        
-        rownames(cons) <- colnames(cons) <- colnames(l[[1]])
-    } 
-    else { ## if type is in "top1", "top2" or "mean"
-        l_df <- lapply(seq_along(l), function(x) {
-            
-            ## find corresponding model in l
-            name_x <- names(l)[x]
-            
-            ## get corresponding adjacency matrix in l
-            l_x <- l[[name_x]]
-            
-            ## take the respective minimum or maximum depending on `values`,
-            ## do not do anything if `values` is equal to `all`
-            if (values %in% c("min", "max")) {
-                
-                ## get values from the lower triangle
-                lower_tri <- l_x[lower.tri(l_x)]
-                
-                ## get values from the upper triangle (requires transposing)
-                l_x_t <- t(l_x)
-                upper_tri <- l_x_t[lower.tri(l_x_t)]
-                
-                ## get min of lower_tri and upper_tri
-                if (values == "min") {
-                    values <- apply(rbind(lower_tri, upper_tri), 2, min)
-                } else {
-                    values <- apply(rbind(lower_tri, upper_tri), 2, max)
-                }
-                
-                ## write back to the matrix
-                l_x[lower.tri(l_x)] <- values
-                l_x <- t(l_x)
-                l_x[lower.tri(l_x)] <- values
-            }
-            
-            ## for pearson/spearman correlation (incl. partial and
-            ## semi-partial), lasso, randomForest, clr, aracne and bayes
-            ## higher values corresond to higher confidence
-            if (grepl(name_x, pattern = "lasso|randomForest|bayes")) {
-                ## set values that are equal to 0 to NaN (values that are 0)
-                ## do not explain the variability
-                res <- getLinks(l_x, exclude = "== 0")
-            }
-            if (grepl(name_x, pattern = "pearson|spearman|clr|aracne")) {
-                res <- getLinks(l_x, exclude = NULL)
-            }
-            
-            res
-        })
-        
-        names(l_df) <- names(l)
-        
-        ## bind together the ranks of the models, stored in l_df
-        ranks <- lapply(l_df, function(x) x$rank)
-        ranks <- do.call("cbind", ranks)
-        colnames(ranks) <- names(l_df)
-        
-        ## calculate the consensus information, i.e. either get the first or
-        ## second top rank per row or calculate the average across rows
-        ## depending on the type argument
-        cons_val <- MetNet:::topKnet(ranks, type)
-        
-        ## bind row and col information with cons information
-        row_col <- l_df[[1]][, c("row", "col")]
-        ranks <- cbind(row_col, cons_val)
-        
-        ## get the top N features
-        n <- args$n
-        top_n <- sort(unique(cons_val))[1:n]
-        ranks_top <- ranks[cons_val %in% top_n, ]
-        
-        ## write links in ranks_top to binary adjacency matrix cons
-        cons <- matrix(0, nrow = ncol(l[[1]]), ncol = ncol(l[[1]]))
-        rownames(cons) <- colnames(cons) <- colnames(l[[1]])
-        cons[as.numeric(rownames(ranks_top))] <- 1
-        
-    }
+    ## write links in ranks_top to binary adjacency matrix cons
+    cons <- matrix(0, nrow = ncol(l[[1]]), ncol = ncol(l[[1]]))
+    rownames(cons) <- colnames(cons) <- colnames(l[[1]])
+    cons[as.numeric(rownames(ranks_top))] <- 1
     
-    ## Changes to MetNet: A list is created as output containing 1/0 assigned values of 
-    ## model matrices (e.g. pearson and spearman) and consensus matrix
-    names(l) <- names(statistical)
-    l[["Consensus"]] <- cons
-    class(l[[3]]) <- "numeric"
-    return(l)
+  }
+  
+  names(l) <- names(statistical[!endsWith(names(statistical), "_p")])
+  l[["Consensus"]] <- cons
+  #class(l[[3]]) <- "numeric"
+  return(l)
+  
 }
+  
+#' @name threshold_p
+#'
+#' @aliases threshold_p
+#'
+#' @title  Threshold the statistical adjacency matrices (using p-values)
+#'
+#' @description
+#' The function `threshold_p` takes as input a list of adjacency matrices
+#' as returned from the function `statistical`. 'threshold_p` will identify the 
+#' strongest link that are lower then a certain p-value threshold 
+#'
+#' @param statistical `list` containing adjacency matrices that contains p-values
+#'
+#' @param args `list` of arguments, has to contain thresholds for weighted
+#' adjacency matrices depending on the statistical model
+#' (a named list, where names are identical to `model`s in `statistical`)
+#' For example "pearson_p" is the corresponding name for pearson p-values
+#' 
+#' @param ... parameters passed to the function `consensus` in the
+#' `sna` package 
+#'
+#' @details
+#' `args` has to contain numeric vector of
+#' length 1 with names equal to `names(statistical)` for each `model`
+#' (`names(statistical)`) and the entry `threshold`, a numerical `vector(1)`
+#' to threshold the consensus
+#' matrix after using the `consensus` function from the `sna` package.
+#' 
+#'
+#' When combining the adjacency matrices the `threshold` value defines if an 
+#' edge is reported or not. The value should be carefully defined by the user.
+#' 
+#'
+#' @return `matrix`, binary adjacency matrix given the links supported by the `args`
+#'
+#' @author Liesa Salzer, \email{liesa.salzer@@helmholtz-muenchen.de}
+#' 
+#' @examples
+#' data("x_test", package = "MetNet")
+#' x <- x_test[1:10, 3:ncol(x_test)]
+#' x <- as.matrix(x)
+#' model <- c("pearson", "spearman")
+#' l <- statistical(x, model = model, p = TRUE)
+#'
+#' args <- list("pearson_p" = 0.05, "spearman" = 0.05, threshold = 1)
+#' threshold_p(statistical = l, type = "threshold", args = args)
+#'
+#'
+#' @export 
+threshold_p <- function(statistical, args, ...) {
+  
+  ## checks if p values are included
+  if ( TRUE != any(endsWith(names(statistical), "_p"))) {
+    stop("p-values missing")
+  }
+  
+  else {  l <- statistical[endsWith(names(statistical), "_p")]
+}
+  if ( TRUE != any(endsWith(names(args), "_p"))) {
+    stop("Please use `_p` arguments")
+  }
+  
+  ## or a list of threshold
+  if (any(duplicated(names(args)))) {
+    stop("names(args) contain duplicated entries")
+  }
+  
+
+    ## iterate through the list and remove the links below or above the
+    ## threshold and write to list
+    l <- lapply(seq_along(l), function(x) {
+      
+      
+      
+      ## find corresponding model in l
+      name_x <- names(l)[x]
+      
+      ## get corresponding threshold in args
+      threshold_x <- args[[name_x]]
+      
+  
+        ## get corresponding adjacency matrix in l
+        ## corresponds to MetNet function
+        l_x <- l[[name_x]]
+        ## for pearson/spearman correlation models (incl. partial and
+        ## semi-partial), lasso, randomForest, clr, aracne and bayes higher
+        ## values corresond to higher confidence
+        ## only assign 1 to values that are above the threshold
+        ifelse(l_x < threshold_x, 1, 0)
+      
+      
+    })
     
+    ## allow for compatibility of arguments
+    ## calculate consenses from the binary matrices
+    cons <- threeDotsCall(sna::consensus, dat = l, ...)
+    
+    ## threshold consensus that it is a binary matrix
+    cons <- ifelse(cons >= args$threshold, 1, 0)
+    
+    rownames(cons) <- colnames(cons) <- colnames(l[[1]])
+    
+  
+  
+  names(l) <- names(statistical[!endsWith(names(statistical), "_p")])
+  l[["Consensus"]] <- cons
+  #class(l[[3]]) <- "numeric"
+  return(l)
+  
+}
 #' @name topKnet
 #' @aliases topKnet
 #' @title Return consensus ranks from a matrix containing ranks
@@ -1215,4 +1310,4 @@ threeDotsCall <- function(fun, ...) {
     ## call the function
     res <- do.call(fun, input)
     return(res)
-}
\ No newline at end of file
+}
diff --git a/R/structural.R b/R/structural.R
index e4e939a..b4f0d1c 100644
--- a/R/structural.R
+++ b/R/structural.R
@@ -11,6 +11,8 @@
 #' loss/addition of functional groups. `structural` returns
 #' the unweighted `numeric` `matrix` together with a `character` `matrix` with
 #' the type of loss/addition as a list at the specific positions.
+#' Changes to MetNet:
+#' structural() has additional list entry of matrix containing mass values of respective matches
 #'
 #' @param
 #' x `matrix`, where columns are the samples and the rows are features
@@ -62,9 +64,6 @@
 #' struct_adj <- structural(x_test, transformation, ppm = 5, directed = TRUE)
 #'
 #' @export
-#' #' Changes to MetNet:
-#' structural() has additional list entry of matrix containing mass values of respective matches
-#' 
 structural <- function(x, transformation, ppm = 5, directed = FALSE) {
     
     if (!is.data.frame(transformation))
@@ -224,7 +223,7 @@ rtCorrection <- function(structural, x, transformation) {
 
     ## check arguments
     if (!is.list(structural)) stop("structural is not a list")
-    if (!length(structural) == 2) stop("structural is not a list of length 2")
+    #if (!length(structural) == 2) stop("structural is not a list of length 2")
     ## allocate structural[[1]] and structural[[2]] to adj and group
     adj <- structural[[1]]
     group <- structural[[2]]
diff --git a/data/.peaklist_example.RData.icloud b/data/.peaklist_example.RData.icloud
new file mode 100644
index 0000000..5bee4e4
Binary files /dev/null and b/data/.peaklist_example.RData.icloud differ
diff --git a/data/peaklist_example.RData b/data/peaklist_example.RData
deleted file mode 100644
index 157fade..0000000
Binary files a/data/peaklist_example.RData and /dev/null differ
diff --git a/man/addToList.Rd b/man/addToList.Rd
index ee4334c..f373045 100644
--- a/man/addToList.Rd
+++ b/man/addToList.Rd
@@ -33,6 +33,7 @@ cor_pearson <- correlation(x, type = "pearson")
 cor_spearman <- correlation(x, type = "spearman")
 l <- list(pearson = cor_pearson)
 MetNet:::addToList(l, "spearman", cor_spearman)
+Changes to MetNet: Not tested anymore if object is a matrix (since object is a list)
 }
 \author{
 Thomas Naake, \email{thomasnaake@googlemail.com}
diff --git a/man/adjacency_list.Rd b/man/adjacency_list.Rd
new file mode 100644
index 0000000..87de249
--- /dev/null
+++ b/man/adjacency_list.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/combine.R
+\name{adjacency_list}
+\alias{adjacency_list}
+\title{Create a list from adjacency matrices}
+\usage{
+adjacency_list(x, from)
+}
+\arguments{
+\item{x}{`data.frame`, adjacency matrix, that has previously been generated 
+by the functions `structural`, `statistical`, or `combine`}
+
+\item{from}{`character` defines the function from which the adjacency matrix originated.
+This can be either "structural", "statistical", or "combine".}
+}
+\value{
+`data.frame` containing the listed values each pair of features from 
+adjacency matrix `x` specified by `from`.
+}
+\description{
+The function `adjacency_list` creates a dataframe from the adjacency matrix.
+Unique values are displayed by the dataframe. Missing values and the upper triangle of 
+the adjacency matrix are left out.
+The function returns a dataframe containing different column outputs depending 
+on the source of the adjacency matrix, which is defined by `from`.
+}
+\details{
+The function `adjacency_list` includes functionality to generate a dataframe from
+adjacency matrices. The output depends on the original function from which the adjacency
+matrix was generated.
+}
+\examples{
+data("x_test", package = "MetNet")
+x <- x_test[1:10, 3:ncol(x_test)]
+x <- as.matrix(x)
+stat_adj <- statistical(x = x, model = c("pearson", "spearman"))
+adjacency_list(x = stat_adj, from = "statistical")
+
+}
+\author{
+Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de}
+}
diff --git a/man/annotaionNames.Rd b/man/annotaionNames.Rd
new file mode 100644
index 0000000..b3adedc
--- /dev/null
+++ b/man/annotaionNames.Rd
@@ -0,0 +1,34 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/combine.R
+\name{annotaionNames}
+\alias{annotaionNames}
+\title{Add annotations to adjacency list}
+\usage{
+annotaionNames(list, names)
+}
+\arguments{
+\item{`list`}{is the adjacency list, and `names` is a dataframe containing the feature names as rows,
+mz values, RT values and annotations in columns}
+}
+\value{
+`data.frame` containing the the `list`-values and additional column with annotations
+}
+\description{
+The function `annotationNames` adds available annotation values for all features
+to the adjacency list.
+}
+\details{
+annotaionNames adds annotation to an adjacency list as additional column.
+}
+\examples{
+data("x_test", package = "MetNet")
+x <- x_test[1:10, 3:ncol(x_test)]
+x <- as.matrix(x)
+stat_adj <- statistical(x = x, model = c("pearson", "spearman"))
+adj_l <- adjacency_list(x = stat_adj, from = "statistical")
+annotationNames(list = adj_l, names = annotationFile)
+
+}
+\author{
+Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de}
+}
diff --git a/man/combine.Rd b/man/combine.Rd
index d7baade..075ce7a 100644
--- a/man/combine.Rd
+++ b/man/combine.Rd
@@ -4,7 +4,7 @@
 \alias{combine}
 \title{Combine structural and statistical adjacency matrix}
 \usage{
-combine(structural, statistical, threshold = 1)
+combine(structural, statistical, threshold = 1, model = "Consensus")
 }
 \arguments{
 \item{structural}{list containing `numeric` structural adjacency matrix in
@@ -15,6 +15,11 @@ entry}
 
 \item{threshold}{numeric, threshold value to be applied to define a
 connection as present}
+
+\item{model}{character, defines model that is used for building the consensus 
+matrix. The model is part of `statistical`. The default is "Consensus" which creates
+the consensus matrix by using a combination of models present in `statistical`. 
+Besides, "pearson" and "spearman" models were implemented.}
 }
 \value{
 `list`, in the first entry `matrix` of type `numeric`containing the
@@ -55,4 +60,5 @@ combine(struct_adj, stat_adj)
 }
 \author{
 Thomas Naake, \email{thomasnaake@googlemail.com}
+Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de}
 }
diff --git a/man/correlation_p.Rd b/man/correlation_p.Rd
new file mode 100644
index 0000000..1adc38d
--- /dev/null
+++ b/man/correlation_p.Rd
@@ -0,0 +1,16 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/statistical.R
+\name{correlation_p}
+\alias{correlation_p}
+\title{correlation_p is an additional function (complementary to correlation()), and needed for positive/negative pearson/spearman
+correlation value and p-value calculation}
+\usage{
+correlation_p(x, type = "pearson", use = "pairwise.complete.obs")
+}
+\description{
+correlation_p is an additional function (complementary to correlation()), and needed for positive/negative pearson/spearman
+correlation value and p-value calculation
+}
+\details{
+dependencies: Hmisc package
+}
diff --git a/man/mat_test.Rd b/man/mat_test.Rd
index 05993b6..410ae92 100644
--- a/man/mat_test.Rd
+++ b/man/mat_test.Rd
@@ -4,7 +4,9 @@
 \name{mat_test}
 \alias{mat_test}
 \title{Example data for \code{MetNet}: unit tests}
-\format{\code{matrix}}
+\format{
+\code{matrix}
+}
 \source{
 set.seed(1)
 random_numbers <- rnorm(140, mean = 10, sd = 2)
diff --git a/man/mat_test_z.Rd b/man/mat_test_z.Rd
index 14ded0f..412caf4 100644
--- a/man/mat_test_z.Rd
+++ b/man/mat_test_z.Rd
@@ -4,7 +4,9 @@
 \name{mat_test_z}
 \alias{mat_test_z}
 \title{Example data for \code{MetNet}: unit tests}
-\format{\code{matrix}}
+\format{
+\code{matrix}
+}
 \source{
 set.seed(1)
 random_numbers <- rnorm(140, mean = 10, sd = 2)
diff --git a/man/peaklist.Rd b/man/peaklist.Rd
index 0041663..1468c6a 100644
--- a/man/peaklist.Rd
+++ b/man/peaklist.Rd
@@ -4,7 +4,9 @@
 \name{peaklist}
 \alias{peaklist}
 \title{Example data for \code{MetNet}: data input}
-\format{\code{data.frame}}
+\format{
+\code{data.frame}
+}
 \source{
 Internal peaklist from metabolite profiling of Nicotiana species
 after W+OS and MeJA treatment. The data was processed by \code{xcms} and
diff --git a/man/statistical.Rd b/man/statistical.Rd
index a42f877..d911df9 100644
--- a/man/statistical.Rd
+++ b/man/statistical.Rd
@@ -4,7 +4,7 @@
 \alias{statistical}
 \title{Create a list of adjacency matrices from statistical methods}
 \usage{
-statistical(x, model, ...)
+statistical(x, model, p = FALSE, ...)
 }
 \arguments{
 \item{x}{`matrix` that contains intensity values of
@@ -15,12 +15,15 @@ features/metabolites (rows) per sample (columns).}
 `"pearson_partial"`, `"pearson_semipartial"`, `"spearman"`,
 `"spearman_partial"`, `"spearman_semipartial"`, `"bayes"`)}
 
+\item{p}{`logical` by default set to FALSE, if set to TRUE p-values will
+be calculated, works only for `model` "pearson" or "spearman"}
+
 \item{...}{parameters passed to the functions  `lasso`, `randomForest`,
 `clr`, `aracne`, `correlation` and/or `bayes`}
 }
 \value{
 `list` containing the respective adjacency matrices specified by
-`model`
+`model` and `p`
 }
 \description{
 The function `statitical` infers adjacency matrix topologies from
@@ -32,6 +35,10 @@ cellular networks (ARACNE), Pearson correlation (also partial and
 semipartial), Spearman correlation (also partial and semipartial)
 and score-based structure learning (Bayes). The function returns a
 list of adjacency matrices that are defined by `model`.
+If `p` = TRUE and `model` == "pearson" or "spearman", than the output 
+contains pearson/spearman positive and negative correlation values and
+an additional list entry with their corresponding p-values
+(saved as "pearson_p" or "spearman_p")
 }
 \details{
 The function `statistical` includes functionality to calculate adjacency
@@ -62,4 +69,5 @@ statistical(x = x, model = c("pearson", "spearman"))
 }
 \author{
 Thomas Naake, \email{thomasnaake@googlemail.com}
+Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de}
 }
diff --git a/man/structural.Rd b/man/structural.Rd
index abd4851..2e222d5 100644
--- a/man/structural.Rd
+++ b/man/structural.Rd
@@ -37,6 +37,8 @@ adjacency matrix using differences in m/z values that are matched against a
 loss/addition of functional groups. `structural` returns
 the unweighted `numeric` `matrix` together with a `character` `matrix` with
 the type of loss/addition as a list at the specific positions.
+Changes to MetNet:
+structural() has additional list entry of matrix containing mass values of respective matches
 }
 \details{
 `structural` accesses the column `"mz"` of
diff --git a/man/summary_mz.Rd b/man/summary_mz.Rd
new file mode 100644
index 0000000..cdd8d7e
--- /dev/null
+++ b/man/summary_mz.Rd
@@ -0,0 +1,51 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/combine.R
+\name{summary_mz}
+\alias{summary_mz}
+\title{Create a summary from adjacency list containing mass-differences with possible filter}
+\usage{
+summary_mz(adjacency_list, filter = F, ...)
+}
+\arguments{
+\item{adjacency_list}{`data.frame`, a list of the mass-difference adjacency mateix, that has previously 
+been generated by the function `adjacency_list(x, from = "statistical")`or 
+`adjacency_list(x, from = "combine")`}
+
+\item{filter}{`number`/`FALSE`, leave empty or set to `FALSE` if unfiltered data are 
+required. Select a `number` as a threshold where mz differences were filtered.
+May be usefull at visualization (plotting) for big data.}
+}
+\value{
+`data.frame` containing the numbers of present mz differences and
+corresponding name. Also a plot will be displayed.
+}
+\description{
+The function `summary_mz` creates a summary from the adjacency list.
+Individual mass differences are count over all features. The input may be 
+adjacency lists originating from the function `structural`,
+or `combine`. The parameter `filtered` will define if data will be 
+filtered above a certain threshold or not.
+}
+\details{
+Summarises the adjacency list containing mass difference values, 
+i.e. either adjacency list from structural or combine may be used.
+Also the plots will be displayed. 
+The default is filter = F, so the unfiltered summary will be returned. 
+If filter is set to a `number`, e.g. 1000 only mz differences above 
+this threshold will be displayed. 
+The function can be applied for adjacency lists from `structural` and `combine`
+}
+\examples{
+data("x_test", package = "MetNet")
+x <- x_test[1:10, 3:ncol(x_test)]
+x <- as.matrix(x)
+stat_adj <- statistical(x = x, model = c("pearson", "spearman"))
+adj_l <- adjacency_list(x = stat_adj, from = "statistical")
+summary_mz(adjacency_list = adj_l) 
+summary_mz(adjacency_list = adj_l, filter = 100) # filtered
+
+
+}
+\author{
+Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de}
+}
diff --git a/man/threshold.Rd b/man/threshold.Rd
index eb56e01..17656d3 100644
--- a/man/threshold.Rd
+++ b/man/threshold.Rd
@@ -92,5 +92,6 @@ threshold(statistical = l, type = "top2", args = args)
 threshold(statistical = l, type = "mean", args = args)
 }
 \author{
-Thomas Naake, \email{thomasnaake@googlemail.com}
+Thomas Naake, \email{thomasnaake@googlemail.com},
+Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de}
 }
diff --git a/man/threshold_p.Rd b/man/threshold_p.Rd
new file mode 100644
index 0000000..6ad1f72
--- /dev/null
+++ b/man/threshold_p.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/statistical.R
+\name{threshold_p}
+\alias{threshold_p}
+\title{Threshold the statistical adjacency matrices (using p-values)}
+\usage{
+threshold_p(statistical, args, ...)
+}
+\arguments{
+\item{statistical}{`list` containing adjacency matrices that contains p-values}
+
+\item{args}{`list` of arguments, has to contain thresholds for weighted
+adjacency matrices depending on the statistical model
+(a named list, where names are identical to `model`s in `statistical`)
+For example "pearson_p" is the corresponding name for pearson p-values}
+
+\item{...}{parameters passed to the function `consensus` in the
+`sna` package}
+}
+\value{
+`matrix`, binary adjacency matrix given the links supported by the `args`
+}
+\description{
+The function `threshold_p` takes as input a list of adjacency matrices
+as returned from the function `statistical`. 'threshold_p` will identify the 
+strongest link that are lower then a certain p-value threshold
+}
+\details{
+`args` has to contain numeric vector of
+length 1 with names equal to `names(statistical)` for each `model`
+(`names(statistical)`) and the entry `threshold`, a numerical `vector(1)`
+to threshold the consensus
+matrix after using the `consensus` function from the `sna` package.
+
+
+When combining the adjacency matrices the `threshold` value defines if an 
+edge is reported or not. The value should be carefully defined by the user.
+}
+\examples{
+data("x_test", package = "MetNet")
+x <- x_test[1:10, 3:ncol(x_test)]
+x <- as.matrix(x)
+model <- c("pearson", "spearman")
+l <- statistical(x, model = model, p = TRUE)
+
+args <- list("pearson_p" = 0.05, "spearman" = 0.05, threshold = 1)
+threshold_p(statistical = l, type = "threshold", args = args)
+
+
+}
+\author{
+Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de}
+}
diff --git a/man/x_test.Rd b/man/x_test.Rd
index 63e27df..c4de6d1 100644
--- a/man/x_test.Rd
+++ b/man/x_test.Rd
@@ -4,7 +4,9 @@
 \name{x_test}
 \alias{x_test}
 \title{Example data for \code{MetNet}: data input}
-\format{\code{matrix}}
+\format{
+\code{matrix}
+}
 \source{
 data("peaklist_example", package = "MetNet")
 peaklist[, 3:dim(peaklist)[2]] <- apply(peaklist[, 3:dim(peaklist)[2]], 2,
diff --git a/vignettes/MetNet.Rmd b/vignettes/MetNet.Rmd
index 9b1799d..4b9aaa2 100644
--- a/vignettes/MetNet.Rmd
+++ b/vignettes/MetNet.Rmd
@@ -114,8 +114,8 @@ send a mail to the developer.
 To install `MetNet` enter the following to the `R` console
 
 ```{r install, eval=FALSE}
-install.packages("BiocManager")
-BiocManager::install("MetNet") 
+#install.packages("BiocManager")
+#BiocManager::install("MetNet") 
 ```
 
 Before starting with the analysis, load the `MetNet` package. This 
@@ -125,6 +125,7 @@ will also load the required packages `glmnet`, `stabs`,
 for functions in the statistical adjacency matrix inference.
 ```{r load_MetNet,eval=TRUE}
 library(MetNet)
+library(tidyverse)
 ``` 
 
 The data format that is compatible with the `MetNet` framework is
@@ -238,9 +239,32 @@ from these structural information we enter
 
 ```{r structure,eval=TRUE,echo=TRUE}
 struct_adj <- structural(x = x_test, transformation = transformations, ppm = 10)
+
+struct_list <- adjacency_list(struct_adj, from = "structural")
+
 ```
 in the `R` console. 
 
+The function `adjacency_list` will then create a list from the corresponding adjacency matrix. The origin of the matrix is defined by `from`, which is in this case the previously used function `"structural"`.
+
+```{r,eval=TRUE,echo=TRUE}
+struct_list <- adjacency_list(struct_adj, from = "structural")
+```
+
+Some overview on the mass-difference distribution of the data can be observed using the `summary_mz` function. For larger data-sets, also a `filter` can be applied to visualize only mass-difference above a certain threshold.
+
+```{r,eval=TRUE,echo=TRUE}
+summary_mz(struct_list, filter = 0)
+```
+
+We can also add putative annotations to the adjacency list using `annotationNames`. Therefore we need to provide a data frame containing the feature names as rownames and the annotations as additional column called `"manualAnnotation"`. 
+
+```{r,eval=TRUE,echo=TRUE}
+x_names <- cbind(x_test, manualAnnotation = c("test_annotation", rep("", 35)))
+
+struct_annotation <- annotaionNames(list = struct_list, names = x_names)
+```
+
 ## Refining the structural adjacency matrix (optional) {-}
 Depending on the chemical group added the retention time will differ depending
 on the chemical group added, e.g. an addition of a glycosyl group will 
@@ -340,13 +364,23 @@ as links in the unweighted consensus adjacency matrix.
 In the following example, we will create a list of unweighted adjacency matrices 
 using 
 Pearson and Spearman correlation using the intensity values as input data. 
+After that, again a list is created from the adjacency matrices. 
 
 ```{r statistical,eval=TRUE,echo=TRUE}
 x_int <- x_test[, 3:dim(x_test)[2]]
 x_int <- as.matrix(x_int)
 stat_adj_l <- statistical(x_int, model = c("pearson", "spearman"))
+
+stat_adj_list <- adjacency_list(stat_adj_l, from = "statistical")
 ```
 
+For pearson and spearman correlation also negative correlations can be calculated including their corresponding p-values using the attribute `p = TRUE` in the `statistical` function. 
+
+```{r,eval=TRUE,echo=TRUE}
+stat_adj_p <- statistical(x_int, model = c("pearson", "spearman"), p = TRUE)
+
+stat_adj_list_p <- adjacency_list(stat_adj_l, from = "statistical")
+```
 `threshold` implements four types to obtain an unweighted adjacency
 matrix. We will create for all types the unweighted consensus adjacency 
 matrices.
@@ -357,6 +391,10 @@ args_thr <- list("pearson" = 0.95, "spearman" = 0.95, threshold = 1)
 stat_adj_thr <- threshold(statistical = stat_adj_l, type = "threshold", 
     args = args_thr)
  
+## thresholding using p-values
+args_thr_p <- list("pearson_p" = 0.05, "spearman_p" = 0.05, threshold = 1)
+stat_adj_thr_p <- threshold_p(statistical = stat_adj_p, args = args_thr_p)
+
 ## type = "top1" 
 args_top <- list(n = 40)
 stat_adj_top1 <- threshold(statistical = stat_adj_l, type = "top2",