From 28551a2e0c2372e81bf2a901e059077c2f7e46ed Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 08:17:13 +0200 Subject: [PATCH 01/34] pearson/ spearman correlation/p-values as list entries --- R/statistical.R | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index 641b87a..e51ba26 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -358,12 +358,19 @@ correlation <- function(x, type = "pearson", use = "pairwise.complete.obs") { correlation_p <- function(x, type = "pearson", use = "pairwise.complete.obs") { ## for pearson/spearman correlation - if (type %in% c("pearson", "spearman")) { + if (type %in% c("spearman")) { cor_list <- 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 <- 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] @@ -702,7 +709,8 @@ 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_corr") + l <- addToList(l, object = pearson[[2]], name = "pearson_p") print("pearson finished.") } @@ -713,7 +721,9 @@ 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_corr") + l <- addToList(l, object = spearman[[2]], name = "spearman_p") print("spearman finished.") } From 7be80f2002657a24e12434d1cda6eb9b4fe19cc3 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 10:15:57 +0200 Subject: [PATCH 02/34] Added documentation on adjacency_list --- R/combine.R | 97 +++++++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 86 insertions(+), 11 deletions(-) diff --git a/R/combine.R b/R/combine.R index 8856329..a28a2dc 100644 --- a/R/combine.R +++ b/R/combine.R @@ -11,6 +11,15 @@ #' in the returned when the sum exceeds the `threshold`. #' \code{combine} returns this consensus matrix supported #' by the structural and statistical adjacency matrices. +#' +#' 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 +#' #' #' @param structural list containing `numeric` structural adjacency matrix in #' the first entry and `character` structural adjanceny matrix in the second @@ -50,13 +59,6 @@ #' 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 @@ -233,8 +235,50 @@ exportNet2gml <- function (x, from, adjacency_list, ...) { } } -#' 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 `from` of the adjacency matrix. +#' +#' +#' @param +#' x is an adjacency matrix, that has previously been generated by the functions +#' `structural`, `statistical`, or `combine` +#' +# +#' @param +#' `from` 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")))) @@ -258,8 +302,7 @@ adjacency_list <- function(x, from){ x[[i]][upper.tri(x[[i]])] <- '' list_corr <- 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]) @@ -314,3 +357,35 @@ sum_mass <- function(adjacency_list){ return(sum_comb) } + + + +# Function to add annotation names +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 From 3d7fde1e18748e7f097d88511521f76b0ef9eddc Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 10:45:40 +0200 Subject: [PATCH 03/34] Documentation on adjacency_list --- NAMESPACE | 4 +--- man/addToList.Rd | 1 + man/adjacency_list.Rd | 42 ++++++++++++++++++++++++++++++++++++++++++ man/combine.Rd | 16 +++++++++++++++- man/correlation_p.Rd | 13 +++++++++++++ man/exportNet2gml.Rd | 27 +++++++++++++++++++++++++++ man/mat_test.Rd | 4 +++- man/mat_test_z.Rd | 4 +++- man/peaklist.Rd | 4 +++- man/statistical.Rd | 2 +- man/sum_mass.Rd | 13 +++++++++++++ man/x_test.Rd | 4 +++- 12 files changed, 125 insertions(+), 9 deletions(-) create mode 100644 man/adjacency_list.Rd create mode 100644 man/correlation_p.Rd create mode 100644 man/exportNet2gml.Rd create mode 100644 man/sum_mass.Rd diff --git a/NAMESPACE b/NAMESPACE index 58fd65e..61a7a39 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +export(adjacency_list) export(aracne) export(bayes) export(clr) @@ -9,9 +10,6 @@ export(getLinks) export(lasso) export(randomForest) export(rtCorrection) -export(statistical) -export(structural) -export(threshold) importFrom(BiocParallel,bplapply) importFrom(GENIE3,GENIE3) importFrom(bnlearn,arcs) 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..6275a30 --- /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}{is an adjacency matrix, that has previously been generated by the functions +`structural`, `statistical`, or `combine`} + +\item{`from`}{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 `from` of the adjacency matrix. +} +\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/combine.Rd b/man/combine.Rd index d7baade..f875790 100644 --- a/man/combine.Rd +++ b/man/combine.Rd @@ -4,7 +4,13 @@ \alias{combine} \title{Combine structural and statistical adjacency matrix} \usage{ -combine(structural, statistical, threshold = 1) +combine( + structural, + statistical, + threshold = 1, + model = "combined", + weighted_statistical +) } \arguments{ \item{structural}{list containing `numeric` structural adjacency matrix in @@ -30,6 +36,14 @@ steps, adds them together and will report a connection between metabolites in the returned when the sum exceeds the `threshold`. \code{combine} returns this consensus matrix supported by the structural and statistical adjacency matrices. + +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 } \details{ The matrices will be added and a unweighted connection will diff --git a/man/correlation_p.Rd b/man/correlation_p.Rd new file mode 100644 index 0000000..52ffe77 --- /dev/null +++ b/man/correlation_p.Rd @@ -0,0 +1,13 @@ +% 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 +} diff --git a/man/exportNet2gml.Rd b/man/exportNet2gml.Rd new file mode 100644 index 0000000..bcb93e7 --- /dev/null +++ b/man/exportNet2gml.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/combine.R +\name{exportNet2gml} +\alias{exportNet2gml} +\title{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"} +\usage{ +exportNet2gml(x, from, adjacency_list, ...) +} +\description{ +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" +} 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..3b9e8f2 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 diff --git a/man/sum_mass.Rd b/man/sum_mass.Rd new file mode 100644 index 0000000..d740570 --- /dev/null +++ b/man/sum_mass.Rd @@ -0,0 +1,13 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/combine.R +\name{sum_mass} +\alias{sum_mass} +\title{sum_mass summarises the adjacency list containing mass difference values, +i.e. either adjacency list from structural or combine may be used} +\usage{ +sum_mass(adjacency_list) +} +\description{ +sum_mass summarises the adjacency list containing mass difference values, +i.e. either adjacency list from structural or combine may be used +} 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, From 6d9b91b841e1f2e48e43e4fb773e21db8e993362 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 10:47:29 +0200 Subject: [PATCH 04/34] document adjacency_list --- .Rbuildignore | 4 +++- .gitignore | 3 ++- DESCRIPTION | 2 +- MetNet.Rproj | 17 +++++++++++++++++ 4 files changed, 23 insertions(+), 3 deletions(-) create mode 100644 MetNet.Rproj 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 From be2e5f4872e849964a56fdbd1228f73914adc2eb Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 10:55:41 +0200 Subject: [PATCH 05/34] update adjacency_list --- R/combine.R | 2 -- 1 file changed, 2 deletions(-) diff --git a/R/combine.R b/R/combine.R index a28a2dc..14fac38 100644 --- a/R/combine.R +++ b/R/combine.R @@ -251,12 +251,10 @@ exportNet2gml <- function (x, from, adjacency_list, ...) { #' The function returns a dataframe containing different column outputs depending #' on the source `from` of the adjacency matrix. #' -#' #' @param #' x is an adjacency matrix, that has previously been generated by the functions #' `structural`, `statistical`, or `combine` #' -# #' @param #' `from` defines the function from which the adjacency matrix originated. #' This can be either `structural`, `statistical`, or `combine` From 2206f5d139114b4c035828602ff37d08eea2c218 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 11:50:24 +0200 Subject: [PATCH 06/34] ducumentation on sum_mas and annotationNames --- NAMESPACE | 2 ++ R/combine.R | 69 +++++++++++++++++++++++++++++++++++++++++-- man/adjacency_list.Rd | 2 +- man/annotaionNames.Rd | 34 +++++++++++++++++++++ man/sum_mass.Rd | 30 +++++++++++++++++-- 5 files changed, 132 insertions(+), 5 deletions(-) create mode 100644 man/annotaionNames.Rd diff --git a/NAMESPACE b/NAMESPACE index 61a7a39..3d5c816 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,6 +1,7 @@ # Generated by roxygen2: do not edit by hand export(adjacency_list) +export(annotaionNames) export(aracne) export(bayes) export(clr) @@ -10,6 +11,7 @@ export(getLinks) export(lasso) export(randomForest) export(rtCorrection) +export(sum_mass) importFrom(BiocParallel,bplapply) importFrom(GENIE3,GENIE3) importFrom(bnlearn,arcs) diff --git a/R/combine.R b/R/combine.R index 14fac38..1aae276 100644 --- a/R/combine.R +++ b/R/combine.R @@ -249,7 +249,7 @@ exportNet2gml <- function (x, from, adjacency_list, ...) { #' 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 `from` of the adjacency matrix. +#' on the source of the adjacency matrix, which is defined by `from`. #' #' @param #' x is an adjacency matrix, that has previously been generated by the functions @@ -330,8 +330,43 @@ adjacency_list <- function(x, from){ } + +#' @name sum_mass +#' +#' @aliases sum_mass +#' +#' @title Create a summary from adjacency list containing mass-differences +#' +#' @description +#' The function `sum_mass` creates a summary from the adjacency list. +#' Individual mass differences are count and summarised over all features. +#' The input may be adjacency lists originating from the function `structural`, +#' or `combine`. +#' +#' @param +#' x is 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")` +#' +#' +#' @details #' sum_mass summarises the adjacency list containing mass difference values, #' i.e. either adjacency list from structural or combine may be used +#' +#' @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")) +#' adj_l <- adjacency_list(x = stat_adj, from = "statistical") +#' sum_mass(adj_l) +#' +#' @export sum_mass <- function(adjacency_list){ if("mass difference" %in% names(adjacency_list)){ @@ -357,7 +392,37 @@ sum_mass <- function(adjacency_list){ - +#' @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 the results of the 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 # Function to add annotation names annotaionNames <- function (list, names) { Var1_names <- c() diff --git a/man/adjacency_list.Rd b/man/adjacency_list.Rd index 6275a30..012d5ab 100644 --- a/man/adjacency_list.Rd +++ b/man/adjacency_list.Rd @@ -22,7 +22,7 @@ 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 `from` of the adjacency matrix. +on the source of the adjacency matrix, which is defined by `from`. } \details{ The function `adjacency_list` includes functionality to generate a dataframe from diff --git a/man/annotaionNames.Rd b/man/annotaionNames.Rd new file mode 100644 index 0000000..6baad15 --- /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 the results of the 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/sum_mass.Rd b/man/sum_mass.Rd index d740570..041032a 100644 --- a/man/sum_mass.Rd +++ b/man/sum_mass.Rd @@ -2,12 +2,38 @@ % Please edit documentation in R/combine.R \name{sum_mass} \alias{sum_mass} -\title{sum_mass summarises the adjacency list containing mass difference values, -i.e. either adjacency list from structural or combine may be used} +\title{Create a summary from adjacency list containing mass-differences} \usage{ sum_mass(adjacency_list) } +\arguments{ +\item{x}{is 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")`} +} +\value{ +`data.frame` containing the listed values each pair of features from +adjacency matrix `x` specified by `from`. +} \description{ +The function `sum_mass` creates a summary from the adjacency list. +Individual mass differences are count and summarised over all features. +The input may be adjacency lists originating from the function `structural`, +or `combine`. +} +\details{ sum_mass summarises the adjacency list containing mass difference values, i.e. either adjacency list from structural or combine may be used } +\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") +sum_mass(adj_l) + +} +\author{ +Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de} +} From e9d02f1e6a53b43fc50240c9d928480a7c025415 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 12:27:05 +0200 Subject: [PATCH 07/34] documentation of structural and statistical --- NAMESPACE | 2 ++ R/statistical.R | 8 ++++---- R/structural.R | 5 ++--- man/statistical.Rd | 4 ++++ man/structural.Rd | 2 ++ 5 files changed, 14 insertions(+), 7 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 3d5c816..e4bfe02 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -11,6 +11,8 @@ export(getLinks) export(lasso) export(randomForest) export(rtCorrection) +export(statistical) +export(structural) export(sum_mass) importFrom(BiocParallel,bplapply) importFrom(GENIE3,GENIE3) diff --git a/R/statistical.R b/R/statistical.R index e51ba26..5acc816 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -538,6 +538,10 @@ 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`. +#' 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 #' #' @param #' x `matrix` that contains intensity values of @@ -585,10 +589,6 @@ 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 diff --git a/R/structural.R b/R/structural.R index e4e939a..4f27d76 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)) diff --git a/man/statistical.Rd b/man/statistical.Rd index 3b9e8f2..843e302 100644 --- a/man/statistical.Rd +++ b/man/statistical.Rd @@ -32,6 +32,10 @@ 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`. +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 } \details{ The function `statistical` includes functionality to calculate adjacency 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 From 02566c65f24a3dc63f50bb16603acee56094ae8e Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 16:11:42 +0200 Subject: [PATCH 08/34] error solved in adjacency_list --- R/combine.R | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/R/combine.R b/R/combine.R index 1aae276..a61820b 100644 --- a/R/combine.R +++ b/R/combine.R @@ -290,7 +290,8 @@ adjacency_list <- function(x, from){ 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$value) %>% as.data.frame() + combine <- merge(list_type, list_mass, by = c("Var1", "Var2")) + colnames(combine) <- c("Var1", "Var2", "value", "mass difference") return(combine) } else if ("statistical" %in% from) { @@ -322,9 +323,11 @@ adjacency_list <- function(x, from){ list_mass <- melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '') list_corr <- melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '') list_p <- melt(x[[4]]) %>% filter(Var1 != Var2) %>% filter(value != '') - listed <- add_column(list_mass, `Correlation Value` = list_corr$value) - listed <- add_column(listed, `p-Value` = list_p$value) - return(listed) + combine <- merge(list_mass, list_corr, by = c("Var1", "Var2")) + colnames(combine) <- c("Var1", "Var2", "value", "Correlation Value") + combine <- merge(combine, list_p, by = c("Var1", "Var2")) + colnames(combine) <- c("Var1", "Var2", "value", "Correlation Value", "p-Value") + return(combine) } From d0246949bbffce39e69effebfda56f278a186928 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 16:19:45 +0200 Subject: [PATCH 09/34] "reshape2::" added to adjacency_list --- R/combine.R | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/R/combine.R b/R/combine.R index a61820b..ab22690 100644 --- a/R/combine.R +++ b/R/combine.R @@ -288,8 +288,8 @@ adjacency_list <- function(x, from){ x[[2]][upper.tri(x[[2]])] <- '' x[[3]][upper.tri(x[[3]])] <- '' - list_type <- melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '') - list_mass <- melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '') + list_type <- reshape2::melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '') + list_mass <- reshape2::melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '') combine <- merge(list_type, list_mass, by = c("Var1", "Var2")) colnames(combine) <- c("Var1", "Var2", "value", "mass difference") return(combine) @@ -306,7 +306,7 @@ adjacency_list <- function(x, from){ if (i != 1){ model = names(x[i]) x[[i]][upper.tri(x[[i]])] <- '' - list_corr2 <- melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '') + list_corr2 <- reshape2::melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '') list_comb <- add_column(list_corr, list_corr2$value) list_comb <- as.data.frame(list_comb) colnames(list_comb)[i+2] <- c(names(x[i])) @@ -320,9 +320,9 @@ adjacency_list <- function(x, from){ 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 != '') + list_mass <- reshape2::melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '') + list_corr <- reshape2::melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '') + list_p <- reshape2::melt(x[[4]]) %>% filter(Var1 != Var2) %>% filter(value != '') combine <- merge(list_mass, list_corr, by = c("Var1", "Var2")) colnames(combine) <- c("Var1", "Var2", "value", "Correlation Value") combine <- merge(combine, list_p, by = c("Var1", "Var2")) From cb06a50280e34bf2704e76983ed6cdc30cc71b10 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 16:32:25 +0200 Subject: [PATCH 10/34] delete plots --- R/combine.R | 5 ----- 1 file changed, 5 deletions(-) diff --git a/R/combine.R b/R/combine.R index ab22690..c2b4df0 100644 --- a/R/combine.R +++ b/R/combine.R @@ -385,11 +385,6 @@ sum_mass <- function(adjacency_list){ colnames(sum_comb) <- c("Type", "Counts") } - - 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) } From 42269107c2405604414ea03b100eca5897939b34 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 16:34:32 +0200 Subject: [PATCH 11/34] added reshape2 input --- R/combine.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/combine.R b/R/combine.R index c2b4df0..a7e3ef6 100644 --- a/R/combine.R +++ b/R/combine.R @@ -299,7 +299,7 @@ adjacency_list <- function(x, from){ for (i in seq_along(x)) { if (i == 1) { 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("Var1", "Var2", names(x[i])) } From b4faa20e736da1d9cfc94eff7162bd7aebe1f2b5 Mon Sep 17 00:00:00 2001 From: Liesa Date: Fri, 11 Sep 2020 16:40:54 +0200 Subject: [PATCH 12/34] comment position changed --- NAMESPACE | 1 + R/statistical.R | 17 +++++++++-------- man/threshold.Rd | 9 +++++++++ 3 files changed, 19 insertions(+), 8 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index e4bfe02..b659922 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -14,6 +14,7 @@ export(rtCorrection) export(statistical) export(structural) export(sum_mass) +export(threshold) importFrom(BiocParallel,bplapply) importFrom(GENIE3,GENIE3) importFrom(bnlearn,arcs) diff --git a/R/statistical.R b/R/statistical.R index 5acc816..04e37f9 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -860,6 +860,15 @@ 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. +#' +#'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') #' #' @return `matrix`, binary adjacency matrix given the links supported by the #' `type` and the `args` @@ -887,14 +896,6 @@ 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"), ...) { diff --git a/man/threshold.Rd b/man/threshold.Rd index eb56e01..f129036 100644 --- a/man/threshold.Rd +++ b/man/threshold.Rd @@ -68,6 +68,15 @@ For `type = "top2"`, the second best (i.e. second lowest) rank in `statistical` is taken. For `type = "mean"`, the average rank in `statistical` is taken. Subsequently the first `n` unique ranks are returned. + +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') } \examples{ data("x_test", package = "MetNet") From aef7988dbc419d254f25fa13c21ee275aab03ace Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 10:57:23 +0200 Subject: [PATCH 13/34] added comment on rcorr in correlation_p --- R/statistical.R | 16 +++------------- 1 file changed, 3 insertions(+), 13 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index 04e37f9..3b672e7 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -355,17 +355,18 @@ 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("spearman")) { - cor_list <- rcorr(x = t(x), type = type) + 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 <- rcorr(x = t(x), type = type) + cor_list <- Hmisc::rcorr(x = t(x), type = type) names(cor_list) <- c("pearson_corr", "pearson_n", "pearson_p") } @@ -910,17 +911,6 @@ threshold <- function(statistical, type, args, 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") - } - } ## check args if (type %in% c("threshold")) { From dd2bf3be3f490aa2fc54a7ce5dbbec471ae1f472 Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 15:57:32 +0200 Subject: [PATCH 14/34] change threshold --- R/statistical.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/statistical.R b/R/statistical.R index 3b672e7..88147e3 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -914,7 +914,9 @@ threshold <- function(statistical, type, args, ## check args if (type %in% c("threshold")) { - if (!(all(names(l) %in% names(args)))) { + if (!(all(names(l) %in% names(args))) || !(any(c("pearson_corr", + "spearman_corr")) + %in% names(l)){ stop("'args' does not contain entries for all 'model's in ", "'statistical'") } From 5a65560ee5847327fb70d31997c7d18f3816b993 Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 16:00:26 +0200 Subject: [PATCH 15/34] threshold changed --- R/statistical.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/statistical.R b/R/statistical.R index 88147e3..4230a89 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -916,7 +916,7 @@ threshold <- function(statistical, type, args, if (type %in% c("threshold")) { if (!(all(names(l) %in% names(args))) || !(any(c("pearson_corr", "spearman_corr")) - %in% names(l)){ + %in% names(l))){ stop("'args' does not contain entries for all 'model's in ", "'statistical'") } From 09e625ceab985b22724307f18c9bd530d55a4344 Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 16:06:19 +0200 Subject: [PATCH 16/34] threshold --- R/statistical.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index 4230a89..2ad8235 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -914,8 +914,8 @@ threshold <- function(statistical, type, args, ## check args if (type %in% c("threshold")) { - if (!(all(names(l) %in% names(args))) || !(any(c("pearson_corr", - "spearman_corr")) + if (!(all(names(l) %in% names(args))) || !(c("pearson_corr", + "spearman_corr") %in% names(l))){ stop("'args' does not contain entries for all 'model's in ", "'statistical'") From 06095226a4363d3686a9d77cdbad7e660296496e Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 16:10:58 +0200 Subject: [PATCH 17/34] change threshold() --- R/statistical.R | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index 2ad8235..bb5942b 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -914,9 +914,7 @@ threshold <- function(statistical, type, args, ## check args if (type %in% c("threshold")) { - if (!(all(names(l) %in% names(args))) || !(c("pearson_corr", - "spearman_corr") - %in% names(l))){ + if (!(all(names(l) %in% names(args)))) { stop("'args' does not contain entries for all 'model's in ", "'statistical'") } @@ -927,10 +925,6 @@ threshold <- function(statistical, type, args, } ## 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'") - } if (!"threshold_p" %in% names(args) && length(args$threshold) != 1) { stop("'args' does not contain entry 'threshold' of length 1") From 331de2f8147230d3cc5487ce7f71c4c757352382 Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 16:17:16 +0200 Subject: [PATCH 18/34] change threshold --- R/statistical.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index bb5942b..503be03 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -968,7 +968,7 @@ threshold <- function(statistical, type, args, else{ ## get corresponding adjacency matrix in l ## corresponds to MetNet function - l_x <- l[[name_x]] + 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 @@ -979,7 +979,7 @@ threshold <- function(statistical, type, args, 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` + l_x <- l[[name_x`_p`]] ## only assign 1 to values that are below the threshold ifelse(l_x < threshold_x, 1, 0) } From 6080df8ce21f94bcd1eaa747e7c7470d0b53b662 Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 16:36:55 +0200 Subject: [PATCH 19/34] test threshold --- R/statistical.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index 503be03..3898ccc 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -968,7 +968,7 @@ threshold <- function(statistical, type, args, else{ ## get corresponding adjacency matrix in l ## corresponds to MetNet function - l_x <- l[[^name_x]] + 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 @@ -979,7 +979,7 @@ threshold <- function(statistical, type, args, 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`]] + l_x <- l[["'name_x'_p"]] ## only assign 1 to values that are below the threshold ifelse(l_x < threshold_x, 1, 0) } From 2b1c23e48d58905b2ab0c410232b558ff6739ebc Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 16:58:08 +0200 Subject: [PATCH 20/34] test threshold --- R/statistical.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index 3898ccc..8217a08 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -968,7 +968,7 @@ threshold <- function(statistical, type, args, else{ ## get corresponding adjacency matrix in l ## corresponds to MetNet function - l_x <- l[[name_x]] + 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 @@ -979,7 +979,8 @@ threshold <- function(statistical, type, args, 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"]] + name_x <- name_x[grep("_p", names(name_x))] + l_x <- l[[name_x]] ## only assign 1 to values that are below the threshold ifelse(l_x < threshold_x, 1, 0) } From e67abcb6746f53a82674a55f5d2e9679d3616df5 Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 14 Sep 2020 16:59:59 +0200 Subject: [PATCH 21/34] test threshold --- R/statistical.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/statistical.R b/R/statistical.R index 8217a08..787f0f9 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -968,7 +968,7 @@ threshold <- function(statistical, type, args, else{ ## get corresponding adjacency matrix in l ## corresponds to MetNet function - l_x <- l[[^name_x]] + 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 From 6b10b65ba799bc5b7a3bd1419b42376cacd6865f Mon Sep 17 00:00:00 2001 From: Liesa Date: Tue, 15 Sep 2020 14:30:08 +0200 Subject: [PATCH 22/34] added summary_mz function --- NAMESPACE | 1 + R/combine.R | 52 ++++++++++++++++++++++++++++++++++++++++++++ R/statistical.R | 4 ++++ man/correlation_p.Rd | 3 +++ 4 files changed, 60 insertions(+) diff --git a/NAMESPACE b/NAMESPACE index b659922..c8150d3 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -14,6 +14,7 @@ export(rtCorrection) export(statistical) export(structural) export(sum_mass) +export(summary_mz) export(threshold) importFrom(BiocParallel,bplapply) importFrom(GENIE3,GENIE3) diff --git a/R/combine.R b/R/combine.R index a7e3ef6..cee2347 100644 --- a/R/combine.R +++ b/R/combine.R @@ -388,6 +388,58 @@ sum_mass <- function(adjacency_list){ return(sum_comb) } +#' +#' +#'@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() + sum_comb <- adjacency_list %>% group_by(`value`) %>% summarise(count=n()) %>% + as.data.frame() %>% add_column(sum_mass$`mass difference`) + colnames(sum_comb) <- c("Type", "Counts", "Mass Difference") + sum_comb <- sum_comb %>% select(Type, `Mass Difference`, Counts)} + else{ + sum_comb <- adjacency_list %>% group_by(`value`) %>% summarise(count=n()) %>% + as.data.frame() + colnames(sum_comb) <- c("Type", "Counts") + + } + + + if (F == filter) { + 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 (F != filter) { + sum_f <- filter(sum_comb,sum_combf$Counts >= filter) + plot_list_f <- ggplot(sum_f, aes(x=Type, y=Counts)) + + geom_bar(stat = "identity") + + theme_minimal() + + coord_flip() + + labs(title = "Numbers of determined mass differences", + subtitle = "(filtered)") + + plot(plot_list_f) + + return(sum_f) + + } + + +} + + + + #' @name annotaionNames diff --git a/R/statistical.R b/R/statistical.R index 787f0f9..6bbad53 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -728,6 +728,10 @@ statistical <- function(x, model, p = FALSE, ...) { print("spearman finished.") } + if (TRUE %in% p & model != "spearman" || model != "pearson") { + stop("'model' not implemented in statistical(p = TRUE)") + } + ## add entry for bayes if "bayes" is in model if ("bayes" %in% model) { diff --git a/man/correlation_p.Rd b/man/correlation_p.Rd index 52ffe77..1adc38d 100644 --- a/man/correlation_p.Rd +++ b/man/correlation_p.Rd @@ -11,3 +11,6 @@ correlation_p(x, type = "pearson", use = "pairwise.complete.obs") correlation_p is an additional function (complementary to correlation()), and needed for positive/negative pearson/spearman correlation value and p-value calculation } +\details{ +dependencies: Hmisc package +} From 7769771da68429c0b2e7d4fa97e4190a7f4cc008 Mon Sep 17 00:00:00 2001 From: Liesa Date: Tue, 15 Sep 2020 14:56:54 +0200 Subject: [PATCH 23/34] check arguments in statistical --- R/combine.R | 6 +++--- R/statistical.R | 12 ++++++++---- 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/R/combine.R b/R/combine.R index cee2347..277c0f1 100644 --- a/R/combine.R +++ b/R/combine.R @@ -407,7 +407,7 @@ summary_mz <- function(adjacency_list, filter = F, ...){ } - if (F == filter) { + if (filter == F) { plot_list <- ggplot(sum_comb, aes(x=Type, y=Counts)) + geom_bar(stat = "identity") + theme_minimal() + @@ -419,8 +419,8 @@ summary_mz <- function(adjacency_list, filter = F, ...){ return(sum_comb) } - else if (F != filter) { - sum_f <- filter(sum_comb,sum_combf$Counts >= filter) + 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() + diff --git a/R/statistical.R b/R/statistical.R index 6bbad53..56cf380 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -602,6 +602,14 @@ 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) @@ -728,10 +736,6 @@ statistical <- function(x, model, p = FALSE, ...) { print("spearman finished.") } - if (TRUE %in% p & model != "spearman" || model != "pearson") { - stop("'model' not implemented in statistical(p = TRUE)") - } - ## add entry for bayes if "bayes" is in model if ("bayes" %in% model) { From 631a0662582fd7c3f66b913c58ed5f10437a0879 Mon Sep 17 00:00:00 2001 From: Liesa Date: Wed, 16 Sep 2020 10:49:10 +0200 Subject: [PATCH 24/34] Delete export2gml because is now in MetNetClassR, change sum_mass with summary_mz function --- NAMESPACE | 1 - R/combine.R | 139 ++++++++++-------------------------------- man/adjacency_list.Rd | 19 ++++-- man/exportNet2gml.Rd | 27 -------- man/sum_mass.Rd | 39 ------------ man/summary_mz.Rd | 50 +++++++++++++++ 6 files changed, 98 insertions(+), 177 deletions(-) delete mode 100644 man/exportNet2gml.Rd delete mode 100644 man/sum_mass.Rd create mode 100644 man/summary_mz.Rd diff --git a/NAMESPACE b/NAMESPACE index c8150d3..753e356 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -13,7 +13,6 @@ export(randomForest) export(rtCorrection) export(statistical) export(structural) -export(sum_mass) export(summary_mz) export(threshold) importFrom(BiocParallel,bplapply) diff --git a/R/combine.R b/R/combine.R index 277c0f1..6c8b806 100644 --- a/R/combine.R +++ b/R/combine.R @@ -170,71 +170,6 @@ combine <- function(structural, statistical, threshold = 1, model = "combined", #' (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")) - - - } -} - @@ -251,13 +186,13 @@ exportNet2gml <- function (x, from, adjacency_list, ...) { #' The function returns a dataframe containing different column outputs depending #' on the source of the adjacency matrix, which is defined by `from`. #' -#' @param -#' x is an adjacency matrix, that has previously been generated by the functions -#' `structural`, `statistical`, or `combine` +#' @param x +#' `data.frame`, adjacency matrix, that has previously been generated +#' by the functions `structural`, `statistical`, or `combine` #' -#' @param -#' `from` defines the function from which the adjacency matrix originated. -#' This can be either `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 @@ -334,30 +269,41 @@ adjacency_list <- function(x, from){ } -#' @name sum_mass +#' @name summary_mz #' -#' @aliases sum_mass +#' @aliases summary_mz #' -#' @title Create a summary from adjacency list containing mass-differences +#' @title Create a summary from adjacency list containing mass-differences with possible filter #' #' @description -#' The function `sum_mass` creates a summary from the adjacency list. -#' Individual mass differences are count and summarised over all features. -#' The input may be adjacency lists originating from the function `structural`, -#' or `combine`. +#' 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 -#' x is a list of the mass-difference adjacency mateix, that has previously been generated -#' by the function `adjacency_list(x, from = "statistical")`or +#' @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 -#' sum_mass summarises the adjacency list containing mass difference values, -#' i.e. either adjacency list from structural or combine may be used +#' 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. #' -#' @return `data.frame` containing the listed values each pair of features from -#' adjacency matrix `x` specified by `from`. +#' @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} #' @@ -367,27 +313,8 @@ adjacency_list <- function(x, from){ #' x <- as.matrix(x) #' stat_adj <- statistical(x = x, model = c("pearson", "spearman")) #' adj_l <- adjacency_list(x = stat_adj, from = "statistical") -#' sum_mass(adj_l) -#' -#' @export -sum_mass <- function(adjacency_list){ - - if("mass difference" %in% names(adjacency_list)){ - sum_mass <- adjacency_list %>% group_by(`mass difference`) %>% summarise(count=n()) %>% - as.data.frame() - sum_comb <- adjacency_list %>% group_by(`value`) %>% summarise(count=n()) %>% - as.data.frame() %>% add_column(sum_mass$`mass difference`) - colnames(sum_comb) <- c("Type", "Counts", "Mass Difference") - sum_comb <- sum_comb %>% select(Type, `Mass Difference`, Counts)} - else{ - sum_comb <- adjacency_list %>% group_by(`value`) %>% summarise(count=n()) %>% - as.data.frame() - colnames(sum_comb) <- c("Type", "Counts") - - } - return(sum_comb) -} - +#' summary_mz(adjacency_list = adj_l) +#' summary_mz(adjacency_list = adj_l, filter = 100) # filtered #' #' #'@export diff --git a/man/adjacency_list.Rd b/man/adjacency_list.Rd index 012d5ab..380d9c0 100644 --- a/man/adjacency_list.Rd +++ b/man/adjacency_list.Rd @@ -7,11 +7,11 @@ adjacency_list(x, from) } \arguments{ -\item{x}{is an adjacency matrix, that has previously been generated by the functions -`structural`, `statistical`, or `combine`} +\item{x}{`data.frame`, adjacency matrix, that has previously been generated +by the functions `structural`, `statistical`, or `combine`} -\item{`from`}{defines the function from which the adjacency matrix originated. -This can be either `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 @@ -25,6 +25,17 @@ The function returns a dataframe containing different column outputs depending on the source of the adjacency matrix, which is defined by `from`. } \details{ +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" + + 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. diff --git a/man/exportNet2gml.Rd b/man/exportNet2gml.Rd deleted file mode 100644 index bcb93e7..0000000 --- a/man/exportNet2gml.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/combine.R -\name{exportNet2gml} -\alias{exportNet2gml} -\title{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"} -\usage{ -exportNet2gml(x, from, adjacency_list, ...) -} -\description{ -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" -} diff --git a/man/sum_mass.Rd b/man/sum_mass.Rd deleted file mode 100644 index 041032a..0000000 --- a/man/sum_mass.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/combine.R -\name{sum_mass} -\alias{sum_mass} -\title{Create a summary from adjacency list containing mass-differences} -\usage{ -sum_mass(adjacency_list) -} -\arguments{ -\item{x}{is 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")`} -} -\value{ -`data.frame` containing the listed values each pair of features from -adjacency matrix `x` specified by `from`. -} -\description{ -The function `sum_mass` creates a summary from the adjacency list. -Individual mass differences are count and summarised over all features. -The input may be adjacency lists originating from the function `structural`, -or `combine`. -} -\details{ -sum_mass summarises the adjacency list containing mass difference values, -i.e. either adjacency list from structural or combine may be used -} -\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") -sum_mass(adj_l) - -} -\author{ -Liesa Salzer, \email{liesa.salzer@helmholtz-muenchen.de} -} diff --git a/man/summary_mz.Rd b/man/summary_mz.Rd new file mode 100644 index 0000000..670215a --- /dev/null +++ b/man/summary_mz.Rd @@ -0,0 +1,50 @@ +% 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. +} +\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} +} From 6cde52a655a1464b2728d05ff51a409256916046 Mon Sep 17 00:00:00 2001 From: Liesa Date: Wed, 16 Sep 2020 16:06:51 +0200 Subject: [PATCH 25/34] change issue in adjacency_list --- R/combine.R | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/R/combine.R b/R/combine.R index 6c8b806..486d605 100644 --- a/R/combine.R +++ b/R/combine.R @@ -242,8 +242,9 @@ adjacency_list <- function(x, from){ model = names(x[i]) x[[i]][upper.tri(x[[i]])] <- '' list_corr2 <- reshape2::melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '') - list_comb <- add_column(list_corr, list_corr2$value) - list_comb <- as.data.frame(list_comb) + list_comb <- merge(list_corr, list_corr2, by = c("Var1", "Var2") ) + #list_comb <- add_column(list_corr, list_corr2$value) + #list_comb <- as.data.frame(list_comb) colnames(list_comb)[i+2] <- c(names(x[i])) list_corr <- list_comb } From 26645f6aa8a98e384fb1d620c66b9b3f01452cb6 Mon Sep 17 00:00:00 2001 From: Liesa Date: Wed, 16 Sep 2020 16:06:58 +0200 Subject: [PATCH 26/34] change issue in adjacency_list --- R/combine.R | 2 -- 1 file changed, 2 deletions(-) diff --git a/R/combine.R b/R/combine.R index 486d605..616b5ae 100644 --- a/R/combine.R +++ b/R/combine.R @@ -243,8 +243,6 @@ adjacency_list <- function(x, from){ x[[i]][upper.tri(x[[i]])] <- '' list_corr2 <- reshape2::melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '') list_comb <- merge(list_corr, list_corr2, by = c("Var1", "Var2") ) - #list_comb <- add_column(list_corr, list_corr2$value) - #list_comb <- as.data.frame(list_comb) colnames(list_comb)[i+2] <- c(names(x[i])) list_corr <- list_comb } From d790187815ec76ea828d21e5f3e1df9d32624aef Mon Sep 17 00:00:00 2001 From: Liesa Date: Mon, 21 Sep 2020 15:34:52 +0200 Subject: [PATCH 27/34] change threshold --- R/statistical.R | 257 +++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 254 insertions(+), 3 deletions(-) diff --git a/R/statistical.R b/R/statistical.R index 56cf380..6001974 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -718,7 +718,7 @@ statistical <- function(x, model, p = FALSE, ...) { #diag(pearson) <- NaN diag(pearson[[1]]) <- NaN diag(pearson[[2]]) <- NaN - l <- addToList(l, object = pearson[[1]], name = "pearson_corr") + l <- addToList(l, object = pearson[[1]], name = "pearson") l <- addToList(l, object = pearson[[2]], name = "pearson_p") print("pearson finished.") } @@ -731,7 +731,7 @@ statistical <- function(x, model, p = FALSE, ...) { diag(spearman[[1]]) <- NaN diag(spearman[[2]]) <- NaN - l <- addToList(l, object = spearman[[1]], name = "spearman_corr") + l <- addToList(l, object = spearman[[1]], name = "spearman") l <- addToList(l, object = spearman[[2]], name = "spearman_p") print("spearman finished.") } @@ -905,7 +905,258 @@ getLinks <- function(mat, exclude = "== 1") { #' ## type = "mean" #' threshold(statistical = l, type = "mean", args = args) #' @export -threshold <- function(statistical, type, args, +threshold <- function(statistical, type, args, + values = c("all", "min", "max"), ...){ + + if( TRUE %in% any(endsWith(names(statistical), "_p" && type != "threshold_p"))) { + l <- statistical[!endsWith(names(statistical), "_p")] + } + + else{l <- statistical} + ## args, either N for tops + ## or a list of threshold + if (type != "threshold_p"){ + + 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'") + } + + 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" || 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( TRUE %in% any(endsWith(names(l), "_p"))) { + ## get corresponding adjacency matrix of Correlation Values in l + ## is used when statistical was calculated with p=TRUE + li <- l[endsWith(names(l), "corr")] + l_x <- li[[name_x]] + + ## 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 + name_x <- name_x[grep("_p", names(name_x))] + l_x <- l[[name_x]] + ## only assign 1 to values that are below the threshold + ifelse(l_x < threshold_x, 1, 0) + } + + }) + 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 <- 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$n + top_n <- sort(unique(cons_val))[1:n] + ranks_top <- ranks[cons_val %in% top_n, ] + + ## write links in ranks_top to binary adjacency matrix cons + cons <- matrix(0, nrow = ncol(l[[1]]), ncol = ncol(l[[1]])) + rownames(cons) <- colnames(cons) <- colnames(l[[1]]) + cons[as.numeric(rownames(ranks_top))] <- 1 + + } + + return(cons) + + } + else { ## if type is in "top1", "top2" or "mean" + l_df <- lapply(seq_along(l), function(x) { + + ## find corresponding model in l + name_x <- names(l)[x] + + ## get corresponding adjacency matrix in l + l_x <- l[[name_x]] + + ## take the respective minimum or maximum depending on `values`, + ## do not do anything if `values` is equal to `all` + if (values %in% c("min", "max")) { + + ## get values from the lower triangle + lower_tri <- l_x[lower.tri(l_x)] + + ## get values from the upper triangle (requires transposing) + l_x_t <- t(l_x) + upper_tri <- l_x_t[lower.tri(l_x_t)] + + ## get min of lower_tri and upper_tri + if (values == "min") { + values <- apply(rbind(lower_tri, upper_tri), 2, min) + } else { + values <- apply(rbind(lower_tri, upper_tri), 2, max) + } + + ## write back to the matrix + l_x[lower.tri(l_x)] <- values + l_x <- t(l_x) + l_x[lower.tri(l_x)] <- values + } + + ## for pearson/spearman correlation (incl. partial and + ## semi-partial), lasso, randomForest, clr, aracne and bayes + ## higher values corresond to higher confidence + if (grepl(name_x, pattern = "lasso|randomForest|bayes")) { + ## set values that are equal to 0 to NaN (values that are 0) + ## do not explain the variability + res <- getLinks(l_x, exclude = "== 0") + } + if (grepl(name_x, pattern = "pearson|spearman|clr|aracne")) { + res <- getLinks(l_x, exclude = NULL) + } + + res + }) + + names(l_df) <- names(l) + + ## bind together the ranks of the models, stored in l_df + ranks <- lapply(l_df, function(x) x$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 + + } + + ## 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) + } + + + + +threshold_new <- function(statistical, type, args, values = c("all", "min", "max"), ...) { l <- statistical From cf2178c2a964f136b8e6d6f665f7440468f61177 Mon Sep 17 00:00:00 2001 From: Liesa Date: Tue, 22 Sep 2020 14:59:01 +0200 Subject: [PATCH 28/34] functions adapted & tested: combine() threshold() threshold_p() --- NAMESPACE | 1 + R/combine.R | 144 ++++++------------- R/statistical.R | 371 ++++++++++-------------------------------------- man/combine.Rd | 8 +- 4 files changed, 125 insertions(+), 399 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 753e356..abacebb 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -15,6 +15,7 @@ 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 616b5ae..846d9e5 100644 --- a/R/combine.R +++ b/R/combine.R @@ -59,105 +59,51 @@ #' combine(struct_adj, stat_adj) #' #' @export -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) { + + + 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() + + ## 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[["Consensus"]] + 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) } diff --git a/R/statistical.R b/R/statistical.R index 6001974..3f67bd6 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -905,18 +905,19 @@ getLinks <- function(mat, exclude = "== 1") { #' ## type = "mean" #' threshold(statistical = l, type = "mean", args = args) #' @export -threshold <- function(statistical, type, args, - values = c("all", "min", "max"), ...){ - - if( TRUE %in% any(endsWith(names(statistical), "_p" && type != "threshold_p"))) { - l <- statistical[!endsWith(names(statistical), "_p")] - } +threshold <- function(statistical, type, args, + values = c("all", "min", "max"), ...) { + + if ( TRUE == any(endsWith(names(statistical), "_p"))) { + l <- statistical[!endsWith(names(statistical), "_p")] + } + + else {l <- statistical} - else{l <- statistical} + + ## args, either N for tops ## args, either N for tops ## or a list of threshold - if (type != "threshold_p"){ - if (any(duplicated(names(args)))) { stop("names(args) contain duplicated entries") } @@ -939,136 +940,59 @@ threshold <- function(statistical, type, args, ## 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" || type == "threshold_p") { + 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]]] - ## Changed to MetNet - if ("threshold" %in% type) { - - if( TRUE %in% any(endsWith(names(l), "_p"))) { - ## get corresponding adjacency matrix of Correlation Values in l - ## is used when statistical was calculated with p=TRUE - li <- l[endsWith(names(l), "corr")] - l_x <- li[[name_x]] - - ## 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 + if( TRUE %in% any(endsWith(names(l), "_p"))) { + ## get corresponding adjacency matrix of Correlation Values in l ## is used when statistical was calculated with p=TRUE - name_x <- name_x[grep("_p", names(name_x))] - l_x <- l[[name_x]] - ## only assign 1 to values that are below the threshold - ifelse(l_x < threshold_x, 1, 0) - } - - }) - 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) - } + li <- l[endsWith(names(l), "corr")] + l_x <- li[[name_x]] - ## 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) + ## 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) } - 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 <- 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) + ## allow for compatibility of arguments + ## calculate consenses from the binary matrices + cons <- threeDotsCall(sna::consensus, dat = l, ...) - ## get the top N features - n <- args$n - top_n <- sort(unique(cons_val))[1:n] - ranks_top <- ranks[cons_val %in% top_n, ] + ## threshold consensus that it is a binary matrix + cons <- ifelse(cons >= args$threshold, 1, 0) - ## 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 } - return(cons) - - } else { ## if type is in "top1", "top2" or "mean" l_df <- lapply(seq_along(l), function(x) { @@ -1127,7 +1051,7 @@ threshold <- function(statistical, type, args, ## 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) + cons_val <- topKnet(ranks, type) ## bind row and col information with cons information row_col <- l_df[[1]][, c("row", "col")] @@ -1145,198 +1069,59 @@ threshold <- function(statistical, type, args, } - ## 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) + names(l) <- names(statistical[!endsWith(names(statistical), "_p")]) l[["Consensus"]] <- cons - class(l[[3]]) <- "numeric" + #class(l[[3]]) <- "numeric" return(l) - } +} +#' +#' +#' +#' @export +threshold_p <- function(statistical, args, ...) { + #if (FALSE == any(endsWith(names(statistical), "_p")) ) + # stop{"no p-Values available"} -threshold_new <- function(statistical, type, args, - values = c("all", "min", "max"), ...) { - - l <- statistical - ## args, either N for tops - ## or a list of threshold - if (any(duplicated(names(args)))) { - stop("names(args) contain duplicated entries") - } - - ##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$threshold) != 1) { - stop("'args' does not contain entry 'threshold' of length 1") - } - } - ## complementary to "threshold": - if (type %in% c("threshold_p")) { - - if (!"threshold_p" %in% names(args) && length(args$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") - } + + # else + l <- statistical[endsWith(names(statistical), "_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]]] - 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]]$`Correlation Value` - - ## only assign 1 to values that are above the threshold - ifelse(l_x > threshold_x, 1, 0) - } - else{ - ## get corresponding adjacency matrix in l - ## corresponds to MetNet function - l_x <- l[[name_x]] - ## for pearson/spearman correlation models (incl. partial and - ## semi-partial), lasso, randomForest, clr, aracne and bayes higher - ## values corresond to higher confidence - ## only assign 1 to values that are above the threshold - ifelse(l_x > threshold_x, 1, 0) - } - } - else if("threshold_p" %in% type){ - ## get corresponding adjacency matrix of p-Values in l - ## is used when statistical was calculated with p=TRUE - name_x <- name_x[grep("_p", names(name_x))] - l_x <- l[[name_x]] - ## only assign 1 to values that are below the threshold - ifelse(l_x < threshold_x, 1, 0) - } - - }) - - ## allow for compatibility of arguments - ## calculate consenses from the binary matrices - cons <- threeDotsCall(sna::consensus, dat = l, ...) - - ## threshold consensus that it is a binary matrix - cons <- ifelse(cons >= args$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 - - } + l_x <- l[[name_x]] - ## 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) + ## 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]]) + + + + names(l) <- names(statistical[!endsWith(names(statistical), "_p")]) + l[["Consensus"]] <- cons + + return(l) } #' @name topKnet diff --git a/man/combine.Rd b/man/combine.Rd index f875790..60879d5 100644 --- a/man/combine.Rd +++ b/man/combine.Rd @@ -4,13 +4,7 @@ \alias{combine} \title{Combine structural and statistical adjacency matrix} \usage{ -combine( - structural, - statistical, - threshold = 1, - model = "combined", - weighted_statistical -) +combine(structural, statistical, threshold = 1) } \arguments{ \item{structural}{list containing `numeric` structural adjacency matrix in From fe137d7228af36983abe8e1c75a29f47b5995b8b Mon Sep 17 00:00:00 2001 From: Liesa Date: Tue, 22 Sep 2020 16:02:39 +0200 Subject: [PATCH 29/34] Added documentation for functions: threshold threshold_p statistical --- R/statistical.R | 80 +++++++++++++++++++++++++++++++++++++--------- man/statistical.Rd | 14 +++++--- man/threshold.Rd | 12 ++----- man/threshold_p.Rd | 57 +++++++++++++++++++++++++++++++++ 4 files changed, 133 insertions(+), 30 deletions(-) create mode 100644 man/threshold_p.Rd diff --git a/R/statistical.R b/R/statistical.R index 3f67bd6..11ef82c 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -539,10 +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`. -#' 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 +#' 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 @@ -553,6 +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`, @@ -579,9 +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") @@ -870,19 +875,13 @@ getLinks <- function(mat, exclude = "== 1") { #' For `type = "mean"`, the average rank in `statistical` is taken. #' Subsequently the first `n` unique ranks are returned. #' -#'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') #' #' @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)] @@ -908,6 +907,7 @@ getLinks <- function(mat, exclude = "== 1") { 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")] } @@ -1076,8 +1076,58 @@ threshold <- function(statistical, type, args, } - +#' @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 +#' +#' +#' @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`) +#' or a numerical +#' vector of length 1 that denotes the number of top ranks written to the +#' consensus matrix (a named list with entry `n`) +#' +#' +#' @param ... parameters passed to the function `consensus` in the +#' `sna` package (only for `type = "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. For +#' `method = "central.graph"` threshold should be set to 1 by default. For other +#' values of `method`, 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 diff --git a/man/statistical.Rd b/man/statistical.Rd index 843e302..d911df9 100644 --- a/man/statistical.Rd +++ b/man/statistical.Rd @@ -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 @@ -32,10 +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`. -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 +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 @@ -66,4 +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/threshold.Rd b/man/threshold.Rd index f129036..17656d3 100644 --- a/man/threshold.Rd +++ b/man/threshold.Rd @@ -68,15 +68,6 @@ For `type = "top2"`, the second best (i.e. second lowest) rank in `statistical` is taken. For `type = "mean"`, the average rank in `statistical` is taken. Subsequently the first `n` unique ranks are returned. - -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') } \examples{ data("x_test", package = "MetNet") @@ -101,5 +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..312ae85 --- /dev/null +++ b/man/threshold_p.Rd @@ -0,0 +1,57 @@ +% 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} + +\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`) +or a numerical +vector of length 1 that denotes the number of top ranks written to the +consensus matrix (a named list with entry `n`)} + +\item{...}{parameters passed to the function `consensus` in the +`sna` package (only for `type = "threshold"`)} +} +\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. For +`method = "central.graph"` threshold should be set to 1 by default. For other +values of `method`, 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} +} From 35483a3cb4d0eddf226edb60f2d830345cffe9a0 Mon Sep 17 00:00:00 2001 From: Liesa Date: Wed, 23 Sep 2020 11:08:13 +0200 Subject: [PATCH 30/34] Documentation on combine function --- R/combine.R | 9 +-------- man/combine.Rd | 8 -------- 2 files changed, 1 insertion(+), 16 deletions(-) diff --git a/R/combine.R b/R/combine.R index 846d9e5..7b55234 100644 --- a/R/combine.R +++ b/R/combine.R @@ -12,14 +12,6 @@ #' \code{combine} returns this consensus matrix supported #' by the structural and statistical adjacency matrices. #' -#' 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 -#' #' #' @param structural list containing `numeric` structural adjacency matrix in #' the first entry and `character` structural adjanceny matrix in the second @@ -40,6 +32,7 @@ #' `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") diff --git a/man/combine.Rd b/man/combine.Rd index 60879d5..d7baade 100644 --- a/man/combine.Rd +++ b/man/combine.Rd @@ -30,14 +30,6 @@ steps, adds them together and will report a connection between metabolites in the returned when the sum exceeds the `threshold`. \code{combine} returns this consensus matrix supported by the structural and statistical adjacency matrices. - -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 } \details{ The matrices will be added and a unweighted connection will From b7dd3241c047111708df3f2e3d3823b0bace126a Mon Sep 17 00:00:00 2001 From: Liesa Date: Thu, 4 Feb 2021 08:00:17 +0100 Subject: [PATCH 31/34] Adjust MetNet functionalities and documentation. Threshold_p function is used to allow thresholding smaller than a defined p-value. combine allows the selection of single model that is used for creation of the consensus matrix. summary_mz summarises and plots adjacency lists containing mass-difference informations --- R/combine.R | 43 +++++++---------- R/statistical.R | 105 ++++++++++++++++++++++-------------------- man/adjacency_list.Rd | 11 ----- man/annotaionNames.Rd | 2 +- man/combine.Rd | 8 +++- man/summary_mz.Rd | 3 +- man/threshold_p.Rd | 14 ++---- 7 files changed, 85 insertions(+), 101 deletions(-) diff --git a/R/combine.R b/R/combine.R index 7b55234..24f5633 100644 --- a/R/combine.R +++ b/R/combine.R @@ -21,6 +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. @@ -52,7 +57,7 @@ #' combine(struct_adj, stat_adj) #' #' @export -combine <- function(structural, statistical, threshold = 1) { +combine <- function(structural, statistical, threshold = 1, model = "Consensus") { if (!is.matrix(structural[[1]]) | !is.numeric(structural[[1]])) @@ -81,10 +86,12 @@ combine <- function(structural, statistical, threshold = 1) { ## create list to store results res <- list() + + ## 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[["Consensus"]] + cons_num <- structural[[1]] + statistical[[model]] cons_num <- ifelse(cons_num > threshold, 1, 0) ## create the second entry of the list @@ -100,18 +107,6 @@ combine <- function(structural, statistical, threshold = 1) { } -#' 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" - - - #' @name adjacency_list #' #' @aliases adjacency_list @@ -165,7 +160,7 @@ adjacency_list <- function(x, from){ 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") + colnames(combine) <- c("Var1", "Var2", "value", "mass-difference") return(combine) } else if ("statistical" %in% from) { @@ -190,17 +185,11 @@ adjacency_list <- function(x, from){ } else if ("combine" %in% from){ x[[2]][upper.tri(x[[2]])] <- '' - x[[3]][upper.tri(x[[3]])] <- '' - x[[4]][upper.tri(x[[4]])] <- '' list_mass <- reshape2::melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '') - list_corr <- reshape2::melt(x[[3]]) %>% filter(Var1 != Var2) %>% filter(value != '') - list_p <- reshape2::melt(x[[4]]) %>% filter(Var1 != Var2) %>% filter(value != '') - combine <- merge(list_mass, list_corr, by = c("Var1", "Var2")) - colnames(combine) <- c("Var1", "Var2", "value", "Correlation Value") - combine <- merge(combine, list_p, by = c("Var1", "Var2")) - colnames(combine) <- c("Var1", "Var2", "value", "Correlation Value", "p-Value") - return(combine) + colnames(list_mass) <- c("Var1", "Var2", "value") + + return(list_mass) } @@ -238,7 +227,8 @@ adjacency_list <- function(x, from){ #' 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. @@ -323,7 +313,7 @@ summary_mz <- function(adjacency_list, filter = F, ...){ #' #' #' @details -#' annotaionNames adds annotation to the results of the adjacency list as additional column. +#' annotaionNames adds annotation to an adjacency list as additional column. #' #' @return `data.frame` containing the the `list`-values and additional column with annotations #' @@ -338,7 +328,6 @@ summary_mz <- function(adjacency_list, filter = F, ...){ #' annotationNames(list = adj_l, names = annotationFile) #' #' @export -# Function to add annotation names annotaionNames <- function (list, names) { Var1_names <- c() diff --git a/R/statistical.R b/R/statistical.R index 11ef82c..0c5b773 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -960,16 +960,7 @@ threshold <- function(statistical, type, args, ## get corresponding threshold in args threshold_x <- args[[names(l)[x]]] - if( TRUE %in% any(endsWith(names(l), "_p"))) { - ## get corresponding adjacency matrix of Correlation Values in l - ## is used when statistical was calculated with p=TRUE - li <- l[endsWith(names(l), "corr")] - l_x <- li[[name_x]] - - ## 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]] @@ -978,7 +969,7 @@ threshold <- function(statistical, type, args, ## values corresond to higher confidence ## only assign 1 to values that are above the threshold ifelse(l_x > threshold_x, 1, 0) - } + }) @@ -1087,19 +1078,15 @@ threshold <- function(statistical, type, args, #' 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 -#' +#' @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`) -#' or a numerical -#' vector of length 1 that denotes the number of top ranks written to the -#' consensus matrix (a named list with entry `n`) +#' For example "pearson_p" is the corresponding name for pearson p-values #' -#' #' @param ... parameters passed to the function `consensus` in the -#' `sna` package (only for `type = "threshold"`) +#' `sna` package #' #' @details #' `args` has to contain numeric vector of @@ -1109,10 +1096,8 @@ threshold <- function(statistical, type, args, #' 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. For -#' `method = "central.graph"` threshold should be set to 1 by default. For other -#' values of `method`, the value should be carefully defined by the user. +#' 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` @@ -1131,49 +1116,67 @@ threshold <- function(statistical, type, args, #' #' #' @export -threshold_p <- function(statistical, args, ...) { - #if (FALSE == any(endsWith(names(statistical), "_p")) ) - # stop{"no p-Values available"} +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")] + 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] + + ## 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) + + + }) - ## get corresponding threshold in args - threshold_x <- args[[names(l)[x]]] + ## 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) - l_x <- l[[name_x]] + rownames(cons) <- colnames(cons) <- colnames(l[[1]]) - ## 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]]) - 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 diff --git a/man/adjacency_list.Rd b/man/adjacency_list.Rd index 380d9c0..87de249 100644 --- a/man/adjacency_list.Rd +++ b/man/adjacency_list.Rd @@ -25,17 +25,6 @@ The function returns a dataframe containing different column outputs depending on the source of the adjacency matrix, which is defined by `from`. } \details{ -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" - - 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. diff --git a/man/annotaionNames.Rd b/man/annotaionNames.Rd index 6baad15..b3adedc 100644 --- a/man/annotaionNames.Rd +++ b/man/annotaionNames.Rd @@ -18,7 +18,7 @@ The function `annotationNames` adds available annotation values for all features to the adjacency list. } \details{ -annotaionNames adds annotation to the results of the adjacency list as additional column. +annotaionNames adds annotation to an adjacency list as additional column. } \examples{ data("x_test", package = "MetNet") 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/summary_mz.Rd b/man/summary_mz.Rd index 670215a..cdd8d7e 100644 --- a/man/summary_mz.Rd +++ b/man/summary_mz.Rd @@ -32,7 +32,8 @@ 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. +this threshold will be displayed. +The function can be applied for adjacency lists from `structural` and `combine` } \examples{ data("x_test", package = "MetNet") diff --git a/man/threshold_p.Rd b/man/threshold_p.Rd index 312ae85..6ad1f72 100644 --- a/man/threshold_p.Rd +++ b/man/threshold_p.Rd @@ -7,17 +7,15 @@ threshold_p(statistical, args, ...) } \arguments{ -\item{statistical}{`list` containing adjacency matrices} +\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`) -or a numerical -vector of length 1 that denotes the number of top ranks written to the -consensus matrix (a named list with entry `n`)} +For example "pearson_p" is the corresponding name for pearson p-values} \item{...}{parameters passed to the function `consensus` in the -`sna` package (only for `type = "threshold"`)} +`sna` package} } \value{ `matrix`, binary adjacency matrix given the links supported by the `args` @@ -35,10 +33,8 @@ 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. For -`method = "central.graph"` threshold should be set to 1 by default. For other -values of `method`, the value should be carefully defined by the user. +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") From 2702540077afbba5d1c12ea15afa4dd9aacf99d1 Mon Sep 17 00:00:00 2001 From: Liesa Date: Thu, 4 Feb 2021 08:15:16 +0100 Subject: [PATCH 32/34] Remove some sanity check from rtCorrection --- R/structural.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/structural.R b/R/structural.R index 4f27d76..b4f0d1c 100644 --- a/R/structural.R +++ b/R/structural.R @@ -223,7 +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]] From 245fa4f76aa777c851191693f0df100f0ae297db Mon Sep 17 00:00:00 2001 From: Liesa Date: Wed, 24 Mar 2021 14:24:18 +0100 Subject: [PATCH 33/34] Add examples to MetNet vignette --- R/combine.R | 24 ++++++++++++++-- R/statistical.R | 4 +-- data/.peaklist_example.RData.icloud | Bin 0 -> 174 bytes data/peaklist_example.RData | Bin 3516660 -> 0 bytes vignettes/MetNet.Rmd | 41 ++++++++++++++++++++++++++-- 5 files changed, 63 insertions(+), 6 deletions(-) create mode 100644 data/.peaklist_example.RData.icloud delete mode 100644 data/peaklist_example.RData diff --git a/R/combine.R b/R/combine.R index 24f5633..f44036e 100644 --- a/R/combine.R +++ b/R/combine.R @@ -153,28 +153,44 @@ 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 <- 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 <- reshape2::melt(x[[i]]) %>% filter(Var1 != Var2) %>% filter(value != '') %>% select(Var1, Var2, value) colnames(list_corr) <- c("Var1", "Var2", names(x[i])) } if (i != 1){ model = names(x[i]) - x[[i]][upper.tri(x[[i]])] <- '' + + 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])) @@ -184,7 +200,11 @@ 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]])] <- '' + } + list_mass <- reshape2::melt(x[[2]]) %>% filter(Var1 != Var2) %>% filter(value != '') colnames(list_mass) <- c("Var1", "Var2", "value") diff --git a/R/statistical.R b/R/statistical.R index 0c5b773..afcc464 100644 --- a/R/statistical.R +++ b/R/statistical.R @@ -968,7 +968,7 @@ threshold <- function(statistical, type, args, ## 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) + ifelse(abs(l_x) > threshold_x, 1, 0) }) @@ -1310,4 +1310,4 @@ threeDotsCall <- function(fun, ...) { ## call the function res <- do.call(fun, input) return(res) -} \ No newline at end of file +} diff --git 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