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

 Package: hipathia

 Title: HiPathia: High-throughput Pathway Analysis

-Version: 2.99.0

+Version: 2.99.1

 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")),

@@ -15,13 +15,12 @@ Description: Hipathia is a method for the computation of signal transduction alo

   the intensity of the signal arriving to it. It also provides a new approach 

   to functional analysis allowing to compute the signal arriving to the 

   functions annotated to each pathway. 

-Depends: R (>= 3.6), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1)

+Depends: R (>= 4.1), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1)

 License: GPL-2

 Encoding: UTF-8

-LazyData: true

 Imports:

-  coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors

-RoxygenNote: 7.0.0

+  coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors, ggplot2, ggpubr, dplyr, tibble, visNetwork, reshape2, MetBrewer

+RoxygenNote: 7.2.2

 Suggests: BiocStyle, knitr, rmarkdown, testthat

 VignetteBuilder: knitr

 biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO

@@ -1,6 +1,12 @@

 # Generated by roxygen2: do not edit by hand

+export(DAcomp)

+export(DAoverview)

+export(DAreport)

+export(DAsummary)

+export(DAtop)

 export(create_report)

+export(define_colors)

 export(do_pca)

 export(do_wilcoxon)

 export(get_go_names)

@@ -24,6 +30,7 @@ export(normalize_paths)

 export(paths_to_go_ancestor)

 export(pathway_comparison_plot)

 export(pca_plot)

+export(plotVG)

 export(quantify_terms)

 export(save_results)

 export(top_pathways)

@@ -32,32 +39,32 @@ export(visualize_report)

 import(AnnotationHub)

 import(MultiAssayExperiment)

 import(SummarizedExperiment)

+import(ggplot2)

 import(grDevices)

 import(graphics)

 import(igraph)

 import(preprocessCore)

 import(servr)

-import(ggplot2)

 import(visNetwork)

 importFrom(DelayedArray,colMaxs)

 importFrom(DelayedArray,colMins)

 importFrom(DelayedArray,rowMaxs)

 importFrom(DelayedArray,rowMins)

+importFrom(MetBrewer,scale_fill_met_c)

+importFrom(S4Vectors,DataFrame)

+importFrom(dplyr,filter)

+importFrom(dplyr,mutate)

 importFrom(dplyr,recode_factor)

 importFrom(dplyr,select)

-importFrom(dplyr,mutate)

-importFrom(dplyr,filter)

 importFrom(ggpubr,ggarrange)

-importFrom(grDevices,rgb)

 importFrom(grDevices,colorRamp)

+importFrom(grDevices,rgb)

 importFrom(matrixStats,colMeans2)

 importFrom(matrixStats,colMedians)

 importFrom(matrixStats,colProds)

 importFrom(matrixStats,rowVars)

-importFrom(MetBrewer,scale_fill_met_c)

 importFrom(methods,is)

 importFrom(reshape2,melt)

-importFrom(tibble,tibble)

 importFrom(stats,TukeyHSD)

 importFrom(stats,aov)

 importFrom(stats,cor.test)

@@ -70,5 +77,5 @@ importFrom(stats,princomp)

 importFrom(stats,quantile)

 importFrom(stats,var)

 importFrom(stats,wilcox.test)

-importFrom(S4Vectors,DataFrame)

+importFrom(tibble,tibble)

 importFrom(utils,head)

@@ -45,3 +45,10 @@ Version 2.3.2 (2019-05-17)

 Version 2.13.1 (2022-07-27)

   + Fixing bug in nodes DE with limma with high rates of 0-variance genes. 

+

+Version 2.99.0 (2022-12-01)

+  + Adding parameters uni.terms and GO.terms to hipathia, to compute functional activity within this function.

+  + Adding functions DAcomp, DAtop, DAsummary, DAoverview, define_colors, plotVG, DAreport. 

+  + Modifyng structure of objects, by creating object DAdata, which includes more information than traditional hipathia results object. This includes:

+    - Activity values of nodes, paths, and selected functions

+    - Extra information about the paths, nodes and functions in rowData elements of the SummarizedExperiments

@@ -76,8 +76,9 @@ DAcomp <- function(hidata, groups, expdes, g2 = NULL,

                     (any(c("uni.terms", "GO.terms") %in% names(hidata)) &

                      fun.method == "wilcoxon")))

     stop("Wilcoxon comparison method needs two groups to compare,

-         introduced in arguments expdes and g2 (ex. expdes = 'case', g2 = 'control').

-         Please provide both arguments or change comparison method to 'limma'.")

+         introduced in arguments expdes and g2 (ex. expdes = 'case', g2 =

+         'control'). Please provide both arguments or change comparison method

+         to 'limma'.")

   # Node comparison

   if(node.method == "wilcoxon"){

@@ -122,14 +123,16 @@ DAcomp <- function(hidata, groups, expdes, g2 = NULL,

                           order = order)

   }

     mesdf <- get_measured_nodes(hidata)[rownames(path.comp),]

-    alt <- get_altered_nodes(hidata, node.comp, conf.level)[rownames(path.comp),]

+    alt <- get_altered_nodes(hidata,

+                             node.comp, conf.level)[rownames(path.comp),]

     path.comp <- tibble(ID = rowData(hidata[["paths"]])$path.ID,

                         name = rowData(hidata[["paths"]])$path.name,

                         path.comp,

                         N.nodes = mesdf$num.nodes,

                         N.gene.nodes = mesdf$num.gene.nodes,

                         N.measured.nodes = mesdf$num.measured.nodes,

-                        ratio.measured.gene.nodes = mesdf$ratio.measured.gene.nodes,

+                        ratio.measured.gene.nodes =

+                            mesdf$ratio.measured.gene.nodes,

                         nodes = rowData(hidata[["paths"]])$path.nodes,

                         N.DA.nodes = alt$N.DA.nodes,

                         DA.nodes = alt$DA.nodes)

@@ -212,29 +215,32 @@ DAcomp <- function(hidata, groups, expdes, g2 = NULL,

 #' @importFrom dplyr recode_factor

 #' @importFrom dplyr mutate

 #'

-topDA <- function(DAdata, n = 10, conf.level = 0.05, adjust = TRUE, colors = "hiro"){

+DAtop <- function(DAdata, n = 10, conf.level = 0.05, adjust = TRUE,

+                  colors = "hiro"){

     colors <- define_colors(colors)

     toplist <- lapply(names(DAdata), function(feat){

         DA <- DAdata[[feat]]

         if(feat == "nodes") DA$name <- paste(DA$name, "(node)")

         if(adjust == TRUE){

             newn <- min(n, sum(DA$FDRp.value < conf.level))

-            DA[order(DA$p.value, decreasing = FALSE),][1:newn,] %>%

+            DA[order(DA$p.value, decreasing = FALSE),][seq_along(newn),] %>%

                 mutate(logPV = abs(log10(FDRp.value)) * sign(statistic),

                        feature = feat)

         }else{

             newn <- min(n, sum(DA$p.value < conf.level))

-            DA[order(DA$p.value, decreasing = FALSE),][1:newn,] %>%

+            DA[order(DA$p.value, decreasing = FALSE),][seq_along(newn),] %>%

                 mutate(logPV = abs(log10(p.value)) * sign(statistic),

                        feature = feat)

         }

     })

     names(toplist) <- names(DAdata)

-    top <- do.call(rbind, lapply(toplist, function(tl) select(tl, c(name, logPV, feature))))

+    top <- do.call(rbind, lapply(toplist, function(tl) select(tl, c(name, logPV,

+                                                                    feature))))

     top$name <- factor(top$name, levels = top$name[nrow(top):1])

     top$feature <- factor(top$feature,

                           levels = c("nodes", "paths",

-                                     names(DAdata)[!names(DAdata) %in% c("nodes", "paths")]))

+                                     names(DAdata)[!names(DAdata) %in%

+                                                       c("nodes", "paths")]))

     top$feature <- recode_factor(top$feature, nodes = "Nodes", paths = "Paths",

                                  uni.terms = "Uniprot", GO.terms = "GO terms")

     dir <- c("UP", "DOWN")

@@ -243,7 +249,8 @@ topDA <- function(DAdata, n = 10, conf.level = 0.05, adjust = TRUE, colors = "hi

     print(ggplot(top, aes(x = name, y = logPV, color = direction)) +

               geom_point(stat = "identity") +

-              scale_color_manual(name = "Status", values = c(colors$down, colors$up)) +

+              scale_color_manual(name = "Status", values = c(colors$down,

+                                                             colors$up)) +

               # scale_fill_met_d("Hiroshige", direction = 1) +

               ylab("abs(Log10 of Adjusted P-value) * direction") +

               xlab("") +

@@ -275,12 +282,13 @@ topDA <- function(DAdata, n = 10, conf.level = 0.05, adjust = TRUE, colors = "hi

 #'

 #' @return Plot and tibble including top \code{n} altered pathways.

 #'

+#' @export

 #' @examples

 #' data(DAdata)

 #' DAsummary(DAdata)

 #'

 DAsummary <- function(DAdata, n = 10, conf.level = 0.05, adjust = TRUE,

-                      ratio = F, colors = "hiro", order.by = "number"){

+                      ratio = FALSE, colors = "hiro", order.by = "number"){

     # Summary

     Psumm <- pathway_summary(DAdata, conf.level, adjust = adjust,

                              order.by = order.by)

@@ -308,7 +316,8 @@ DAsummary <- function(DAdata, n = 10, conf.level = 0.05, adjust = TRUE,

 #' @export

 #' @importFrom tibble tibble

 #'

-DAoverview <- function(DAdata, conf.level = 0.05, adjust = TRUE, colors = "hiro"){

+DAoverview <- function(DAdata, conf.level = 0.05, adjust = TRUE,

+                       colors = "hiro"){

     # Summary

     summ <- lapply(names(DAdata), function(feat){

         data <- DAdata[[feat]]

@@ -316,14 +325,18 @@ DAoverview <- function(DAdata, conf.level = 0.05, adjust = TRUE, colors = "hiro"

             summdf <- data.frame(feature = feat,

                                  total = nrow(data),

                                  sigs = sum(data$FDRp.value < conf.level),

-                                 UPs = sum(data$FDRp.value < conf.level & data$statistic > 0),

-                                 DOWNs = sum(data$FDRp.value < conf.level & data$statistic < 0))

+                                 UPs = sum(data$FDRp.value < conf.level &

+                                               data$statistic > 0),

+                                 DOWNs = sum(data$FDRp.value < conf.level &

+                                                 data$statistic < 0))

         }else{

             summdf <- data.frame(feature = feat,

                                  total = nrow(data),

                                  sigs = sum(data$p.value < conf.level),

-                                 UPs = sum(data$p.value < conf.level & data$statistic > 0),

-                                 DOWNs = sum(data$p.value < conf.level & data$statistic < 0))

+                                 UPs = sum(data$p.value < conf.level &

+                                               data$statistic > 0),

+                                 DOWNs = sum(data$p.value < conf.level &

+                                                 data$statistic < 0))

         }

     })

     summ <- tibble(do.call(rbind, summ))

@@ -345,8 +358,10 @@ pathway_summary <- function(DAdata, conf = 0.05, adjust = TRUE,

         mini <- comp[comp$pathway.ID == pathway,]

         if(adjust == TRUE){

             pdf <- data.frame(sigs = sum(mini$FDRp.value < conf),

-                              UPs = sum(mini$FDRp.value < conf & mini$statistic > 0),

-                              DOWNs = sum(mini$FDRp.value < conf & mini$statistic < 0),

+                              UPs = sum(mini$FDRp.value < conf &

+                                            mini$statistic > 0),

+                              DOWNs = sum(mini$FDRp.value < conf &

+                                              mini$statistic < 0),

                               total = nrow(mini),

                               ratio.sigs = sum(mini$FDRp.value < conf)/nrow(mini),

                               ratio.UPs = sum(mini$FDRp.value < conf &

@@ -355,8 +370,10 @@ pathway_summary <- function(DAdata, conf = 0.05, adjust = TRUE,

                                                     mini$statistic < 0)/nrow(mini))

         }else{

             pdf <- data.frame(sigs = sum(mini$p.value < conf),

-                              UPs = sum(mini$p.value < conf & mini$statistic > 0),

-                              DOWNs = sum(mini$p.value < conf & mini$statistic < 0),

+                              UPs = sum(mini$p.value < conf &

+                                            mini$statistic > 0),

+                              DOWNs = sum(mini$p.value < conf &

+                                              mini$statistic < 0),

                               total = nrow(mini),

                               ratio.sigs = sum(mini$p.value < conf)/nrow(mini),

                               ratio.UPs = sum(mini$p.value < conf &

@@ -373,14 +390,18 @@ pathway_summary <- function(DAdata, conf = 0.05, adjust = TRUE,

         mini <- ndata[ndata$pathway.ID == pathway,]

         if(adjust == TRUE){

             ndf <- data.frame(sig.nodes = sum(mini$FDRp.value < conf),

-                              UP.nodes = sum(mini$FDRp.value < conf & mini$statistic > 0),

-                              DOWN.nodes = sum(mini$FDRp.value < conf & mini$statistic < 0),

+                              UP.nodes = sum(mini$FDRp.value < conf &

+                                                 mini$statistic > 0),

+                              DOWN.nodes = sum(mini$FDRp.value < conf &

+                                                   mini$statistic < 0),

                               gene.nodes = sum(mini$type == "gene"),

                               total.nodes = nrow(mini))

         }else{

             ndf <- data.frame(sig.nodes = sum(mini$p.value < conf),

-                              UP.nodes = sum(mini$p.value < conf & mini$statistic > 0),

-                              DOWN.nodes = sum(mini$p.value < conf & mini$statistic < 0),

+                              UP.nodes = sum(mini$p.value < conf &

+                                                 mini$statistic > 0),

+                              DOWN.nodes = sum(mini$p.value < conf &

+                                                  mini$statistic < 0),

                               gene.nodes = sum(mini$type == "gene"),

                               total.nodes = nrow(mini))

         }

@@ -410,9 +431,9 @@ pathway_summary <- function(DAdata, conf = 0.05, adjust = TRUE,

 #' @importFrom dplyr mutate

 #' @importFrom dplyr select

 #'

-summary_plot <- function(Psumm, n.paths = 10, ratio = F, colors = "vg"){

+summary_plot <- function(Psumm, n.paths = 10, ratio = FALSE, colors = "vg"){

-    pdata <- Psumm[1:n.paths,]

+    pdata <- Psumm[seq_along(n.paths),]

     pdata$name <- factor(pdata$name, levels = pdata$name[n.paths:1])

     palette <- define_colors(colors)

@@ -422,10 +443,12 @@ summary_plot <- function(Psumm, n.paths = 10, ratio = F, colors = "vg"){

         mutate(DOWN = DOWNs) %>%

         select(c(name, UP, DOWN, Not))

     data1 <- melt(d1, "name")

-    data1$variable <- factor(data1$variable, levels = unique(data1$variable)[c(3,1,2)])

+    data1$variable <- factor(data1$variable,

+                             levels = unique(data1$variable)[c(3,1,2)])

     g1 <- ggplot(data1, aes(x = name, y = value, fill = variable)) +

         geom_bar(stat = "identity") +

-        scale_fill_manual(name = "Status", values = c("#dfe0df", palette$up, palette$down)) +

+        scale_fill_manual(name = "Status",

+                          values = c("#dfe0df", palette$up, palette$down)) +

         # scale_fill_met_d("Hiroshige", direction = 1) +

         ylab("Total significant paths") +

         xlab("Pathway") +

@@ -442,7 +465,8 @@ summary_plot <- function(Psumm, n.paths = 10, ratio = F, colors = "vg"){

         geom_point(aes(color = variable, size = nodes)) +

         geom_point(aes(size = nodes - 5), color = "white") +

         geom_point(aes(color = variable, size = value)) +

-        scale_color_manual(name = "Status", values = c(palette$up, palette$down)) +

+        scale_color_manual(name = "Status",

+                           values = c(palette$up, palette$down)) +

         ylab("DE nodes") +

         ggtitle("") +

         theme_minimal() +

@@ -488,16 +512,21 @@ nsig_plot <- function(summ, colors = "vg"){

         mutate(UP = UPs) %>%

         mutate(DOWN = DOWNs) %>%

         select(c(feature, UP, DOWN, Not))

-    d1$feature <- factor(d1$feature, levels = c("nodes", "paths", d1$feature[!d1$feature %in% c("nodes", "paths")]))

+    d1$feature <- factor(d1$feature,

+                         levels = c("nodes", "paths",

+                                    d1$feature[!d1$feature %in%

+                                                   c("nodes", "paths")]))

     d1$feature <- recode_factor(d1$feature, nodes = "Nodes", paths = "Paths",

                   uni.terms = "Uniprot", GO.terms = "GO terms")

     data1 <- melt(d1, "feature")

     # data1$feature <- factor(data1$feature, levels = levels(data1$feature)[length(levels(data1$feature)):1])

-    data1$variable <- factor(data1$variable, levels = unique(data1$variable)[c(3,1,2)])

+    data1$variable <- factor(data1$variable,

+                             levels = unique(data1$variable)[c(3,1,2)])

     g <- ggplot(data1, aes(x = feature, y = value, fill = variable)) +

         geom_bar(stat = "identity") +

-        scale_fill_manual(name = "Status", values = c("#dfe0df", palette$up, palette$down)) +

+        scale_fill_manual(name = "Status",

+                          values = c("#dfe0df", palette$up, palette$down)) +

         ylab("") +

         xlab("Feature") +

         ggtitle("Results overview") +

@@ -596,12 +625,14 @@ get_edges_status <- function(pg, edgename, DApaths, adjust = TRUE){

 }

 #' @importFrom dplyr mutate

-prepare_DAedges <- function(DApaths, name, pathways, cols, conf = 0.05, adjust = TRUE){

+prepare_DAedges <- function(DApaths, name, pathways, cols, conf = 0.05,

+                            adjust = TRUE){

     # require(dplyr)

     pg <- pathways$pathigraphs[[name]]

     # Define colors

-    color.edge.type <- c(cols$up, cols$down, cols$both, "lightgray", "gainsboro") # c(met.brewer("Egypt", 4), "gainsboro") # c("#0571b0", "green", "#ca0020", "#ffc868", "gainsboro")

+    color.edge.type <- c(cols$up, cols$down, cols$both, "lightgray",

+                         "gainsboro") # c(met.brewer("Egypt", 4), "gainsboro") # c("#0571b0", "green", "#ca0020", "#ffc868", "gainsboro")

     names(color.edge.type) <- c("UP", "DOWN", "Both", "None", "function")

     # Create edges tibble

@@ -771,6 +802,9 @@ prepare_nodes <- function(name, pathways, conf = 0.05, adjust = TRUE,

 #' pathways <- load_pathways("hsa")

 #' plotVG("hsa04010", pathways)

 #'

+#' data(DAdata)

+#' plotVG("hsa04010", pathways, DAdata)

+#'

 #' @import visNetwork

 #' @export

 #'

@@ -789,11 +823,14 @@ plotVG <- function(name, pathways, DAdata = NULL, colors = "hiro",

                              color = c("lightgray", "gainsboro"),

                              width = c(10, 1))

     }else{

-        nodes <- prepare_DAnodes(DAdata, name, pathways, cols, conf, adjust, no.col)

-        edges <- prepare_DAedges(DAdata[["paths"]], name, pathways, cols, conf, adjust)

+        nodes <- prepare_DAnodes(DAdata, name, pathways, cols, conf, adjust,

+                                 no.col)

+        edges <- prepare_DAedges(DAdata[["paths"]], name, pathways, cols, conf,

+                                 adjust)

         submain <- "Differential activation plot"

         ledges <- data.frame(label = c("UP", "DOWN", "Both", "None", "function"),

-                             color = c(cols$up, cols$down, cols$both, "lightgray", "gainsboro"),

+                             color = c(cols$up, cols$down, cols$both,

+                                       "lightgray", "gainsboro"),

                              width = c(10, 10, 10, 10, 1))

     }

@@ -807,7 +844,8 @@ plotVG <- function(name, pathways, DAdata = NULL, colors = "hiro",

 #' @import visNetwork

 plotVisGraphDE <- function(nodes, edges, ledges, main = "Pathway",

                            submain = "Differential activation plot",

-                           cols = list(no = "BlanchedAlmond", up = "red", down = "blue"),

+                           cols = list(no = "BlanchedAlmond", up = "red",

+                                       down = "blue"),

                            height = "800px"){

     # require(visNetwork, quietly = TRUE)

@@ -887,8 +925,8 @@ plotVisGraphDE <- function(nodes, edges, ledges, main = "Pathway",

         visOptions(highlightNearest = list(enabled = TRUE,

                                            degree = 100,

                                            algorithm = "hierarchical",

-                                           hover = F,

-                                           labelOnly = F),

+                                           hover = FALSE,

+                                           labelOnly = FALSE),

                    # nodesIdSelection = list(enabled = TRUE,

                    #                         main = "Select by gene",

                    #                         values = nodes$label[groups == "gene"]),

@@ -897,7 +935,7 @@ plotVisGraphDE <- function(nodes, edges, ledges, main = "Pathway",

                    #                   values = unique(nodes$label[groups == "function"]),

                    #                   multiple = TRUE)

         ) %>%

-        visLegend(position = "right", useGroups = T, main = "Legend",

+        visLegend(position = "right", useGroups = TRUE, main = "Legend",

                   addEdges = ledges)

 }

new file mode 100644

@@ -0,0 +1,89 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/devel.R

+\name{DAcomp}

+\alias{DAcomp}

+\title{Compares the gene expression, pathway activation level and the function

+activation level of the}

+\usage{

+DAcomp(

+  hidata,

+  groups,

+  expdes,

+  g2 = NULL,

+  path.method = "wilcoxon",

+  node.method = "limma",

+  fun.method = "wilcoxon",

+  order = FALSE,

+  paired = FALSE,

+  adjust = TRUE,

+  conf.level = 0.05,

+  sel_assay = 1

+)

+}

+\arguments{

+\item{hidata}{Either a SummarizedExperiment object or a matrix, returned by

+function \code{hipathia}.}

+

+\item{groups}{Either a character indicating the name of the column in colData

+including the classes to compare, or a character vector with the class to

+which each sample belongs.

+Samples must be ordered as in \code{hidata}.}

+

+\item{expdes}{String, either an equation expression to pas to \code{limma},

+or the label of the first group to be compared}

+

+\item{g2}{String, label of the second group to be compared, if not specified

+in \code{expdes}.}

+

+\item{path.method}{String, method to be used when comparing pathways.

+Options include \code{wilcoxon} (default, performs a Wilcoxon test comparing

+conditions \code{expdes} and \code{g2} - in this case, mandatory parameter)

+and \code{limma} (performs a limma DE analysis using functions \code{lmFit},

+\code{contrasts.fit} and \code{eBayes} using the formula in \code{expdes} or

+comparing conditions \code{expdes} and \code{g2}.}

+

+\item{node.method}{String, method to be used when comparing nodes.

+Options include \code{wilcoxon} (performs a Wilcoxon test comparing

+conditions \code{expdes} and \code{g2} - in this case, mandatory parameter)

+and \code{limma} (default, performs a limma DE analysis using functions

+\code{lmFit}, \code{contrasts.fit} and \code{eBayes} using the formula in

+\code{expdes} or comparing conditions \code{expdes} and \code{g2}.}

+

+\item{fun.method}{String, method to be used when comparing functions.

+Options include \code{wilcoxon} (default, performs a Wilcoxon test comparing

+conditions \code{expdes} and \code{g2} - in this case, mandatory parameter)

+and \code{limma} (performs a limma DE analysis using functions \code{lmFit},

+\code{contrasts.fit} and \code{eBayes} using the formula in \code{expdes} or

+comparing conditions \code{expdes} and \code{g2}.}

+

+\item{order}{Boolean, whether to order the results table by the

+\code{FDRp.value} column. Default is FALSE.}

+

+\item{paired}{Boolean, whether the samples to be compared are paired.

+If TRUE, function \code{wilcoxsign_test} from package \code{coin} is

+used. If FALSE, function \code{wilcox.test} from package \code{stats}

+is used.}

+

+\item{adjust}{Boolean, whether to adjust the p.value with

+Benjamini-Hochberg FDR method. Default is TRUE.}

+

+\item{sel_assay}{Character or integer, indicating the assay to be normalized

+in the SummarizedExperiment. Default is 1.}

+

+\item{conf_level}{Numeric, cut off for significance. Default is 0.05.}

+}

+\value{

+List including comparison results for nodes, pathways and functions,

+if present.

+}

+\description{

+Compares the gene expression, pathway activation level and the function

+activation level of the

+}

+\examples{

+data(path_vals)

+data(brca_design)

+sample_group <- brca_design[colnames(path_vals),"group"]

+comp <- DAcomp(path_vals, sample_group, g1 = "Tumor", g2 = "Normal")

+

+}

new file mode 100644

@@ -0,0 +1,33 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/devel.R

+\name{DAoverview}

+\alias{DAoverview}

+\title{Table and plot of total number of altered and not altered nodes, paths and

+functions (Uniprot keywords and/or GO terms, if present).}

+\usage{

+DAoverview(DAdata, conf.level = 0.05, adjust = TRUE, colors = "hiro")

+}

+\arguments{

+\item{DAdata}{List of comparison results, returned by function \code{DAcomp}.}

+

+\item{conf.level}{Numeric, cut off for significance. Default is 0.05.}

+

+\item{adjust}{Boolean, whether to adjust the p.value with

+Benjamini-Hochberg FDR method. Default is TRUE.}

+

+\item{colors}{String with the color scheme or vector of colors to be used.

+See  \code{define_colors} for available options. Default is "hiro".}

+}

+\value{

+Plot and tibble including the number of total, altered, UP- and

+DOWN-regulated features for nodes, paths and functions if present.

+}

+\description{

+Table and plot of total number of altered and not altered nodes, paths and

+functions (Uniprot keywords and/or GO terms, if present).

+}

+\examples{

+data(DAdata)

+DAoverview(DAdata)

+

+}

new file mode 100644

@@ -0,0 +1,65 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/devel.R

+\name{DAreport}

+\alias{DAreport}

+\title{Create visualization HTML}

+\usage{

+DAreport(

+  DAdata,

+  pathways,

+  conf.level = 0.05,

+  adjust = TRUE,

+  group_by = "pathway",

+  colors = "classic",

+  output_folder = NULL,

+  path = NULL,

+  verbose = TRUE

+)

+}

+\arguments{

+\item{DAdata}{List of comparison results, returned by function \code{DAcomp}.}

+

+\item{pathways}{Pathways object as returned by the \code{load_pathways}

+function}

+

+\item{conf.level}{Level of significance. By default 0.05.}

+

+\item{adjust}{Boolean, whether to adjust the p.value with

+Benjamini-Hochberg FDR method. Default is TRUE.}

+

+\item{group_by}{How to group the subpathways to be visualized. By default

+they are grouped by the pathway to which they belong. Available groupings

+include "uniprot", to group subpathways by their annotated Uniprot functions,

+"GO", to group subpathways by their annotated GO terms, and "genes", to group

+subpathways by the genes they include. Default is set to "pathway".}

+

+\item{colors}{String with the color scheme or vector of colors to be used.

+See  \code{define_colors} for available options. Default is "hiro".}

+

+\item{output_folder}{Name of the folder in which the report will be stored.}

+

+\item{path}{Absolute path to the parent directory in which `output_folder`

+will be saved. If it is not provided, it will be created in a temp folder.}

+

+\item{verbose}{Boolean, whether to show details about the results of the

+execution}

+}

+\value{

+Saves the results and creates a report to visualize them through

+a server in the specified \code{output_folder}. Returns the folder where

+the report has been stored.

+}

+\description{

+Saves the results of a DAdata comparison for the Hipathia pathway values

+into a folder, and creates a HTML from which to visualize the results on

+top of the pathways. The results are stored into the specified folder.

+If this folder does not exist, it will be created. The parent folder must

+exist.

+}

+\examples{

+data(DAdata)

+pathways <- load_pathways(species = "hsa", pathways_list = c("hsa03320",

+"hsa04012"))

+DAreport(DAdata, pathways)

+

+}

new file mode 100644

@@ -0,0 +1,50 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/devel.R

+\name{DAsummary}

+\alias{DAsummary}

+\title{Lists and plots the top \code{n} altered pathways, taking into account the

+number of altered .}

+\usage{

+DAsummary(

+  DAdata,

+  n = 10,

+  conf.level = 0.05,

+  adjust = TRUE,

+  ratio = F,

+  colors = "hiro",

+  order.by = "number"

+)

+}

+\arguments{

+\item{DAdata}{List of comparison results, returned by function \code{DAcomp}.}

+

+\item{n}{Number of top features to show.}

+

+\item{conf.level}{Numeric, cut off for significance. Default is 0.05.}

+

+\item{adjust}{Boolean, whether to adjust the p.value with

+Benjamini-Hochberg FDR method. Default is TRUE.}

+

+\item{ratio}{Boolean, whether to plot the ratio of significant paths with

+respect to the total paths in the pathway. Default is FALSE.}

+

+\item{colors}{String with the color scheme or vector of colors to be used.

+See  \code{define_colors} for available options. Default is "hiro".}

+

+\item{order.by}{String, how to order table of results. Available options

+include \code{ratio} (default, uses the ratio of significant paths with

+respect to the total paths in the pathway) and \code{number} (uses the number

+of significant paths in the pathway).}

+}

+\value{

+Plot and tibble including top \code{n} altered pathways.

+}

+\description{

+Lists and plots the top \code{n} altered pathways, taking into account the

+number of altered .

+}

+\examples{

+data(DAdata)

+DAsummary(DAdata)

+

+}

new file mode 100644

@@ -0,0 +1,35 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/devel.R

+\name{DAtop}

+\alias{DAtop}

+\title{Lists and plots the top \code{n} altered nodes, paths and functions (Uniprot

+keywords and/or GO terms, if present).}

+\usage{

+DAtop(DAdata, n = 10, conf.level = 0.05, adjust = TRUE, colors = "hiro")

+}

+\arguments{

+\item{DAdata}{List of comparison results, returned by function \code{DAcomp}.}

+

+\item{n}{Number of top features to show.}

+

+\item{conf.level}{Numeric, cut off for significance. Default is 0.05.}

+

+\item{adjust}{Boolean, whether to adjust the p.value with

+Benjamini-Hochberg FDR method. Default is TRUE.}

+

+\item{colors}{String with the color scheme or vector of colors to be used.

+See  \code{define_colors} for available options. Default is "hiro".}

+}

+\value{

+Plot and list of tables including top \code{n} altered features for

+nodes, paths and functions if present.

+}

+\description{

+Lists and plots the top \code{n} altered nodes, paths and functions (Uniprot

+keywords and/or GO terms, if present).

+}

+\examples{

+data(DAdata)

+topDA(DAdata)

+

+}

@@ -4,12 +4,14 @@

 \name{brca}

 \alias{brca}

 \title{BRCA gene expression dataset as SummarizedExperiment}

-\format{SummarizedExperiment. The assay is a matrix with 40 columns and

+\format{

+SummarizedExperiment. The assay is a matrix with 40 columns and

 18638 rows. Row names are Entrez IDs and column names are the TCGA

 identifyers of the samples. The colData() is a data.frame with 1 column and

 40 rows, including the experimental design of the 40 samples from the BRCA-US

 project from TCGA. Field \code{group} is the type of sample, either "Tumor"

-or "Normal".}

+or "Normal".

+}

 \source{

 \url{https://cancergenome.nih.gov/}

 }

@@ -4,8 +4,10 @@

 \name{brca_data}

 \alias{brca_data}

 \title{BRCA gene expression dataset}

-\format{Matrix with 40 columns and 18638 rows. Row names are Entrez IDs

-and column names are the  TCGA identifyers of the samples.}

+\format{

+Matrix with 40 columns and 18638 rows. Row names are Entrez IDs

+and column names are the  TCGA identifyers of the samples.

+}

 \source{

 \url{https://cancergenome.nih.gov/}

 }

@@ -4,9 +4,11 @@

 \name{brca_design}

 \alias{brca_design}

 \title{BRCA experimental design}

-\format{Dataframe with 1 column and 40 rows, including the experimental

+\format{

+Dataframe with 1 column and 40 rows, including the experimental

 design of the 40 samples from the BRCA-US project from TCGA. Field

-\code{group} is the type of sample, either "Tumor" or "Normal".}

+\code{group} is the type of sample, either "Tumor" or "Normal".

+}

 \source{

 \url{https://cancergenome.nih.gov/}

 }

@@ -4,7 +4,9 @@

 \name{comp}

 \alias{comp}

 \title{Wilcoxon comparison of pathways object}

-\format{Table with 1868 rows and 5 columns}

+\format{

+Table with 1868 rows and 5 columns

+}

 \usage{

 data(comp)

 }

new file mode 100644

@@ -0,0 +1,27 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/devel.R

+\name{define_colors}

+\alias{define_colors}

+\title{Color palettes to be used in plots.}

+\usage{

+define_colors(colors, no.col = NULL)

+}

+\arguments{

+\item{colors}{String with the color scheme or vector of colors to be used.

+Available predefined options include: \code{hipathia}, \code{classic},

+\code{soft}, \code{okee}, \code{hiro}, \code{new}, \code{vg}, \code{orchid}.}

+

+\item{no.col}{String with the color given to non-significant nodes, if not

+given in parameter \code{colors}.}

+}

+\value{

+Plot and list of tables including top \code{n} altered features for

+nodes, paths and functions if present.

+}

+\description{

+Color palettes to be used in plots.

+}

+\examples{

+define_colors("hiro")

+

+}

@@ -4,8 +4,10 @@

 \name{exp_data}

 \alias{exp_data}

 \title{Normalized BRCA gene expression dataset}

-\format{Matrix with 40 columns and 3184 rows. Row names are Entrez IDs

-and column names are the  TCGA identifyers of the samples.}

+\format{

+Matrix with 40 columns and 3184 rows. Row names are Entrez IDs

+and column names are the  TCGA identifyers of the samples.

+}

 \usage{

 data(exp_data)

 }

@@ -4,7 +4,7 @@

 \alias{get_go_names}

 \title{Tranlates GO IDs to GO names}

 \usage{

-get_go_names(names, species, maxchar = NULL)

+get_go_names(names, species, maxchar = NULL, disambiguate = FALSE)

 }

 \arguments{

 \item{names}{Character vector with the GO IDs to be translated.}

@@ -9,11 +9,11 @@ get_nodes_data(results, matrix = FALSE)

 \arguments{

 \item{results}{Results object as returned by \code{hipathia}.}

-\item{matrix}{Boolean, if TRUE the function returns a matrix object, if 

+\item{matrix}{Boolean, if TRUE the function returns a matrix object, if

 FALSE (as default) returns a SummarizedExperiment object.}

 }

 \value{

-Object, either a SummarizedExperiment or a matrix, with the levels 

+Object, either a SummarizedExperiment or a matrix, with the levels

 of activation of each decomposed subpathway for each sample.

 }

 \description{

@@ -9,11 +9,11 @@ get_paths_data(results, matrix = FALSE)

 \arguments{

 \item{results}{Results object as returned by \code{hipathia}.}

-\item{matrix}{Boolean, if TRUE the function returns a matrix object, if 

+\item{matrix}{Boolean, if TRUE the function returns a matrix object, if

 FALSE (as default) returns a SummarizedExperiment object.}

 }

 \value{

-Object, either a SummarizedExperiment or a matrix, with the levels 

+Object, either a SummarizedExperiment or a matrix, with the levels

 of activation of each decomposed subpathway for each sample.

 }

 \description{

@@ -4,8 +4,10 @@

 \name{go_vals}

 \alias{go_vals}

 \title{Gene Ontology matrix of the BRCA gene expression dataset}

-\format{Matrix with 40 columns and 1654 rows. Row names are Gene Ontology

-terms and column names are the TCGA identifyers of the samples.}

+\format{

+Matrix with 40 columns and 1654 rows. Row names are Gene Ontology

+terms and column names are the TCGA identifyers of the samples.

+}

 \usage{

 data(go_vals)

 }

@@ -11,7 +11,7 @@ hhead(mat, n = 5, sel_assay = 1)

 \item{n}{Number of rows and columns}

-\item{sel_assay}{Character or integer, indicating the assay to be translated 

+\item{sel_assay}{Character or integer, indicating the assay to be translated

 in the SummarizedExperiment. Default is 1.}

 }

 \value{

@@ -2,14 +2,17 @@

 % Please edit documentation in R/main.R

 \name{hipathia}

 \alias{hipathia}

-\title{Computes the level of activation of the subpathways for each 

+\title{Computes the level of activation of the subpathways for each

 of the samples}

 \usage{

 hipathia(

   genes_vals,