@@ -1,6 +1,6 @@

 Package: hipathia

 Title: HiPathia: High-throughput Pathway Analysis

-Version: 1.99.4

+Version: 1.99.5

 Authors@R: c(person("Marta R.", "Hidalgo", email = "marta.hidalgo@outlook.es", role = c("aut", "cre")),

 	     person("José", "Carbonell-Caballero", role = c("ctb")),

 	     person("Francisco", "Salavert", role = c("ctb")),

@@ -378,8 +378,8 @@ compute_node_signal <- function(actnode, node_val, node_signal, subgraph,

         }

         # If signal too low, signal do not propagate

-        if(sum(nas) == 0 && signal < response_tol)

-            signal <- rep(0, length(node_val))

+        if(sum(nas) == 0 && any(signal < response_tol))

+            signal[signal < response_tol] <- 0

     }

@@ -587,8 +587,13 @@ get_pathways_summary <- function(comp, metaginfo, conf = 0.05){

 #'

 top_pathways <- function(comp){

-    path_names <- as.character(comp$path_names)

-    comp$pathways <- sapply(strsplit(path_names, split = ":"), "[[", 1)

+    if("name" %in% colnames(comp)){

+        path_names <- as.character(comp$name)

+        comp$pathways <- sapply(strsplit(path_names, split = ":"), "[[", 1)

+    }else{

+        path_names <- rownames(comp)

+        comp$pathways <- sapply(strsplit(path_names, split = "-"), "[[", 2)

+    }

     pathways <- unique(comp$pathways)

     tests <- do.call(rbind, lapply(pathways, function(path) {

@@ -859,10 +859,11 @@ paths_to_go_ancestor <- function(pathways, comp_paths, comp_go, pval = 0.05){

 #' by dividing by the value obtained from running the method with a basal

 #' value of 0.5 at each node.

 #'

-#' @param path_vals Matrix of the pathway values

+#' @param path_vals SummarizedExperiment or matrix of the pathway values

 #' @param metaginfo Pathways object

 #'

-#' @return Matrix of normalized pathway values

+#' @return SummarizedExperiment or matrix of normalized pathway values, 

+#' depending on the class of \code{path_vals}.

 #'

 #' @examples

 #' data(path_vals)

@@ -876,17 +877,24 @@ normalize_paths <- function(path_vals, metaginfo){

     decomposed <- is_decomposed_matrix(path_vals)

     if(decomposed == TRUE){

         norm_factors <- metaginfo$path.norm[rownames(path_vals)]

-        path_norm <- normalize_data(path_vals/(norm_factors*0.99+0.01),

-                                    by_quantiles = FALSE,

-                                    by_gene = FALSE,

-                                    percentil = FALSE)

     }else{

         norm_factors <- metaginfo$eff.norm[rownames(path_vals)]

-        path_norm <- normalize_data(path_vals/(norm_factors*0.99+0.01),

-                                    by_quantiles = FALSE,

-                                    by_gene = FALSE,

-                                    percentil = FALSE)

     }

+    if(is(path_vals, "SummarizedExperiment")){

+        coldata <- colData(path_vals)

+        path_vals <- assay(path_vals, "paths")

+        se_flag <- TRUE

+    }else{

+        se_flag <- FALSE

+    }

+    path_norm <- normalize_data(path_vals/(norm_factors*0.99+0.01),

+                                by_quantiles = FALSE,

+                                by_gene = FALSE,

+                                percentil = FALSE)

+    if(se_flag == TRUE)

+        path_norm <- SummarizedExperiment(list(path_norm = path_norm), 

+                                          colData = coldata)

+    

     return(path_norm)

 }

@@ -7,12 +7,13 @@

 normalize_paths(path_vals, metaginfo)

 }

 \arguments{

-\item{path_vals}{Matrix of the pathway values}

+\item{path_vals}{SummarizedExperiment or matrix of the pathway values}

 \item{metaginfo}{Pathways object}

 }

 \value{

-Matrix of normalized pathway values

+SummarizedExperiment or matrix of normalized pathway values, 

+depending on the class of \code{path_vals}.

 }

 \description{

 Due to the nature of the Hipathia method, the length of a pathway may