diff --git a/DESCRIPTION b/DESCRIPTION index a293f5c5..e7bfe5d8 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -26,7 +26,8 @@ Imports: dplyr, tidyselect, gplots, - ggplot2 + ggplot2, + rlang Suggests: BiocManager, knitr, @@ -39,6 +40,7 @@ Depends: URL: https://um-applied-algorithms-lab.github.io/PTMsToPathways/ BugReports: https://github.com/UM-Applied-Algorithms-Lab/PTMsToPathways/issues/new LazyData: true +LazyDataCompression: xz Config/testthat/edition: 3 Additional_repositories: https://bioconductor.org/packages/3.21/bioc biocViews: Software, Proteomics, PostTranslationalModifications, Pathways, Network diff --git a/LICENSE.md b/LICENSE.md deleted file mode 100644 index 175443ce..00000000 --- a/LICENSE.md +++ /dev/null @@ -1,595 +0,0 @@ -GNU General Public License -========================== - -_Version 3, 29 June 2007_ -_Copyright © 2007 Free Software Foundation, Inc. <>_ - -Everyone is permitted to copy and distribute verbatim copies of this license -document, but changing it is not allowed. - -## Preamble - -The GNU General Public License is a free, copyleft license for software and other -kinds of works. - -The licenses for most software and other practical works are designed to take away -your freedom to share and change the works. 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If not, see . - -Also add information on how to contact you by electronic and paper mail. - -If the program does terminal interaction, make it output a short notice like this -when it starts in an interactive mode: - - Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type 'show c' for details. - -The hypothetical commands `show w` and `show c` should show the appropriate parts of -the General Public License. Of course, your program's commands might be different; -for a GUI interface, you would use an “about box”. - -You should also get your employer (if you work as a programmer) or school, if any, to -sign a “copyright disclaimer” for the program, if necessary. For more -information on this, and how to apply and follow the GNU GPL, see -<>. - -The GNU General Public License does not permit incorporating your program into -proprietary programs. If your program is a subroutine library, you may consider it -more useful to permit linking proprietary applications with the library. If this is -what you want to do, use the GNU Lesser General Public License instead of this -License. But first, please read -<>. diff --git a/NAMESPACE b/NAMESPACE index 58ba02f6..318aedec 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -43,3 +43,4 @@ export(strip.cy.goo) importFrom(gplots,heatmap.2) importFrom(grDevices,colorRampPalette) importFrom(grDevices,hcl) +importFrom(rlang,.data) diff --git a/R/BRCAHelpers.R b/R/BRCAHelpers.R index d9fc7023..5c5e5c2d 100644 --- a/R/BRCAHelpers.R +++ b/R/BRCAHelpers.R @@ -88,7 +88,7 @@ graph.ptm.by.cluster <- function( safe_dist2 <- function(m, min.shared = 2) { m <- as.matrix(m) nr <- nrow(m) - if (nr <= 1) return(as.dist(matrix(0, nr, nr))) + if (nr <= 1) return(stats::as.dist(matrix(0, nr, nr))) dmat <- matrix(0, nr, nr) rownames(dmat) <- colnames(dmat) <- rownames(m) for (i in seq_len(nr - 1)) { @@ -102,14 +102,14 @@ graph.ptm.by.cluster <- function( fallback <- if (length(fv)) 2 * max(fv) else 1 dmat[!is.finite(dmat)] <- fallback diag(dmat) <- 0 - as.dist(dmat) + stats::as.dist(dmat) } ## row-wise linear slope helper for order.rows = "slope" row_slope <- function(v) { ok <- which(is.finite(v)) if (length(ok) < 2) return(NA_real_) - coef(lm(v[ok] ~ ok))[2] + stats::coef(stats::lm(v[ok] ~ ok))[2] } ## high-contrast qualitative palette for cluster sidebar @@ -154,13 +154,13 @@ graph.ptm.by.cluster <- function( write_cluster_legend <- function(cluster.palette, filename, cex = 0.9) { n <- length(cluster.palette) ncol <- if (n > 18) 2L else 1L - pdf(filename, + grDevices::pdf(filename, width = max(5, 3 * ncol), height = max(4, 0.32 * ceiling(n / ncol) + 1)) - on.exit(dev.off()) - par(mar = c(0.3, 0.3, 0.3, 0.3), xpd = NA) - plot.new() - legend("topleft", + on.exit(grDevices::dev.off()) + graphics::par(mar = c(0.3, 0.3, 0.3, 0.3), xpd = NA) + graphics::plot.new() + graphics::legend("topleft", inset = 0.01, legend = names(cluster.palette), fill = cluster.palette, @@ -180,16 +180,16 @@ graph.ptm.by.cluster <- function( brks <- seq(-zlim, zlim, length.out = n + 1L) z <- matrix(seq_len(n), nrow = 1L, ncol = n) # 1 x n - pdf(filename, width = 1.8, height = 4.5) - on.exit(dev.off()) + grDevices::pdf(filename, width = 1.8, height = 4.5) + on.exit(grDevices::dev.off()) # left margin wide enough for the bar; right margin for axis labels - par(mar = c(2, 1.0, 1.5, 2.8)) + graphics::par(mar = c(2, 1.0, 1.5, 2.8)) # x: 2 breakpoints = 1 column; y: n+1 breakpoints = n rows - image(x = c(0, 1), y = brks, z = z, + graphics::image(x = c(0, 1), y = brks, z = z, col = cols, xaxt = "n", yaxt = "n", xlab = "", ylab = "") - axis(4, at = pretty(c(-zlim, zlim)), las = 2, cex.axis = 0.85) - mtext("Signal", side = 4, line = 1.8, cex = 0.85) - box() + graphics::axis(4, at = pretty(c(-zlim, zlim)), las = 2, cex.axis = 0.85) + graphics::mtext("Signal", side = 4, line = 1.8, cex = 0.85) + graphics::box() } # ---- data prep ----------------------------------------------------- @@ -218,7 +218,7 @@ graph.ptm.by.cluster <- function( mean = subm[order(rowMeans(subm, na.rm=TRUE), decreasing=TRUE, na.last=TRUE), , drop=FALSE], slope = { sl <- apply(subm, 1, row_slope) subm[order(sl, decreasing=TRUE, na.last=TRUE), , drop=FALSE] }, - hclust = subm[hclust(safe_dist2(subm, min.shared))$order, , drop=FALSE], + hclust = subm[stats::hclust(safe_dist2(subm, min.shared))$order, , drop=FALSE], subm # "as.is" ) } @@ -232,7 +232,7 @@ graph.ptm.by.cluster <- function( mat <- do.call(rbind, ordered.blocks) if (cluster.cols && ncol(mat) > 1) { - mat <- mat[, hclust(safe_dist2(t(mat), min.shared))$order, drop=FALSE] + mat <- mat[, stats::hclust(safe_dist2(t(mat), min.shared))$order, drop=FALSE] } cluster.sizes <- sapply(ordered.blocks, nrow) @@ -257,7 +257,7 @@ graph.ptm.by.cluster <- function( # ---- heatmap (key = FALSE → no internal key panel) --------------- - pdf(filename, width = pdf.w, height = pdf.h) + grDevices::pdf(filename, width = pdf.w, height = pdf.h) gplots::heatmap.2( mat, Rowv = FALSE, @@ -285,7 +285,7 @@ graph.ptm.by.cluster <- function( margins = c(10, 6), main = main ) - dev.off() + grDevices::dev.off() message("Heatmap written to: ", filename) # ---- optional legend files ---------------------------------------- diff --git a/R/BuildClusterFilteredNetwork.R b/R/BuildClusterFilteredNetwork.R index ab4c4411..c7af98dc 100644 --- a/R/BuildClusterFilteredNetwork.R +++ b/R/BuildClusterFilteredNetwork.R @@ -18,7 +18,8 @@ #' #' @export #' @examples -#' Example_Output <- BuildClusterFilteredNetwork(ex_gene_cccn_edges, ex_stringdb_edges, ex_genemania_edges) +#' Example_Output <- BuildClusterFilteredNetwork(ex_gene_cccn_edges, +#' ex_stringdb_edges, ex_genemania_edges) #' utils::head(Example_Output[[2]]) BuildClusterFilteredNetwork <- function(gene.cccn.edges, stringdb.edges = NULL, genemania.edges = NULL, kinsub.edges = NULL, db.filepaths = c(NULL)) { # Combine PPIs from different databases diff --git a/R/CytoscapeGraphingFunctions.R b/R/CytoscapeGraphingFunctions.R index 0630a731..50ab8db5 100644 --- a/R/CytoscapeGraphingFunctions.R +++ b/R/CytoscapeGraphingFunctions.R @@ -1,10 +1,7 @@ # @title Graphing and Manipulating Cluster Filtered Network # -# @description Creates a cytoscape graph of the cluster filtered network. Ensure that you have the Cytoscape app open and the RCy3 package downloaded. -# The package RCy3 is required for many of the functions in this script. To download, run: -# - if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") -# - BiocManager::install("RCy3") -# It is also recommended (but not required) that users load RCy3 with library(RCy3) to access its functions directly. +# @description Creates a cytoscape graph of the cluster filtered network. Ensure +# that you have the Cytoscape app open and the RCy3 package downloaded. # # @details Graph Key # - Node Size @@ -312,7 +309,7 @@ strip.cy.goo <- function(test) { #' #' @return Called for its side effects; creates a network in Cytoscape. #' @export -cytoscape.graph.PCN.pathways <- function(PCN = pathway.crosstalk.network, net.name, Jaccard.edges=TRUE) { +cytoscape.graph.PCN.pathways <- function(PCN, net.name, Jaccard.edges=TRUE) { PCN.df <- data.frame(id=unique(c(PCN$source, PCN$target))) if (Jaccard.edges== FALSE) {PCN = PCN[-which (PCN$interaction=="pathway Jaccard similarity"),]} # Get rid of zero weight edges @@ -352,7 +349,7 @@ make.gene.data.from.ptmtable <- function(genes, ptmtable) { gene.data <- subset.ptmtable |> dplyr::group_by(.data$Gene.Name) |> dplyr::summarise( - dplyr::across(where(is.numeric), ~sum(.x, na.rm = TRUE)), + dplyr::across(dplyr::where(is.numeric), ~sum(.x, na.rm = TRUE)), .groups = "drop" ) @@ -374,11 +371,13 @@ make.gene.data.from.ptmtable <- function(genes, ptmtable) { #' summarised PTM values to the node table. Defaults to \code{FALSE}. #' @param include.coclustered.PTMs Logical; if \code{TRUE}, add co-clustered #' PTM nodes and their edges to the output. Defaults to \code{FALSE}. +#' @param ptm.cccn.edges A data frame of PTM co-expression correlation network +#' edges with PTM identifiers as node names, required if \code{include.coclustered.PTMs = TRUE}. #' #' @return A data frame of node attributes with an \code{id} column suitable #' for \code{RCy3::createNetworkFromDataFrames}. #' @export -make.cytoscape.node.file <- function(edge.file, funckey, ptmtable, include.gene.data = FALSE, include.coclustered.PTMs = FALSE) { +make.cytoscape.node.file <- function(edge.file, funckey = PTMsToPathways::function_key, ptmtable, include.gene.data = FALSE, include.coclustered.PTMs = FALSE, ptm.cccn.edges = NULL) { # Step 1: get unique nodes from edge file edge_nodes <- unique(c(as.character(edge.file[, 1]), as.character(edge.file[, 2]))) # check if there are PTMs in edgefile @@ -416,7 +415,7 @@ make.cytoscape.node.file <- function(edge.file, funckey, ptmtable, include.gene. node_file <- cbind(data.frame(id = node_file$Gene.Name), node_file) # Step 4: Optionally merge PTM CCCN and data if (include.coclustered.PTMs == TRUE) { - edge.file.with.ptms <- get.co.clustered.ptms(edge.file, ptm.cccn.edges) + edge.file.with.ptms <- get.co.clustered.ptms(edge.file, ptm.cccn.edges, ptmtable) if(length (peptides) > 0) { edge.file.with.ptms <- unique(rbind(edge.file.with.ptms, edge.file[which(edge.file$interaction == "peptide"), ])) } @@ -470,11 +469,12 @@ make.genepep.edges <- function(peptide.edgefile) { #' @param edge.file A data frame edge list whose nodes are gene names. #' @param ptm.cccn.edges A data frame of PTM co-expression correlation network #' edges with PTM identifiers as node names. +#' @param ptmtable A PTM data frame with PTM identifiers as row names and numeric condition columns. #' #' @return An edge data frame combining the original gene edges, co-clustered #' PTM edges, and gene-to-peptide linking edges. #' @export -get.co.clustered.ptms <- function (edge.file, ptm.cccn.edges) { +get.co.clustered.ptms <- function (edge.file, ptm.cccn.edges, ptmtable) { gene_nodes <- unique(c(as.character(edge.file[, 1]), as.character(edge.file[, 2]))) ptmtable.temp <- ptmtable ptmtable.temp$Gene.Name <- sapply(rownames(ptmtable.temp), function (x) strsplit(x, " ", fixed = TRUE)[[1]][1]) @@ -572,7 +572,7 @@ harmonize_cfs <- function(edge.file.with.ptms, genecf, ptmtable) { pepcf.funcs <- merge( pepcf, - funckey[, annotation_cols, drop = FALSE], + function_key[, annotation_cols, drop = FALSE], by = "Gene.Name", all.x = TRUE ) @@ -658,6 +658,9 @@ mergeEdges <- function(edgefile) { #' #' @param ptms Character vector of PTM site strings (e.g., \"TP53 p S15\") #' @param cfn List or data frame representing the cluster filtered network (default: global cfn.merged) +#' @param ptm.cccn.edges A data frame of PTM co-expression correlation network +#' edges with PTM identifiers as node names +#' @param ptmtable A data frame containing PTM identifiers as row names and numeric condition columns #' @param pepsep Character used to split ambiguous PTM entries (default: \";\") #' #' @return Subnetwork object with co-clustered PTMs and genes @@ -673,10 +676,11 @@ mergeEdges <- function(edgefile) { #' # Suppose filter.edges.0 and get.co.clustered.ptms are also defined and loaded #' # The following returns the gene/PTM subnetwork #' \dontrun{ -#' res <- ptms_to_cfn(ptms, cfn = cfn.merged, pepsep = ";") +#' res <- ptms_to_cfn(ptms, cfn = cfn.merged, +#' ptm.cccn.edges = ptm.cccn.edges, ptmtable = ptmtable, pepsep = ";") #' print(res) #' } -ptms_to_cfn <- function(ptms, cfn = cfn.merged, pepsep = ";") { +ptms_to_cfn <- function(ptms, cfn, ptm.cccn.edges, ptmtable, pepsep = ";") { ambig.ptms <- ptms[grep(";", ptms)] if (length(ambig.ptms) > 0) { @@ -697,8 +701,8 @@ ptms_to_cfn <- function(ptms, cfn = cfn.merged, pepsep = ";") { all_genes <- unique(sapply(ptms, function (x) unlist(strsplit(x, " ", fixed=TRUE))[1])) } - sub.cfn <- filter.edges.0(all_genes, cfn.merged) - sub.cfn.cccn <- get.co.clustered.ptms(sub.cfn, ptm.cccn.edges) + sub.cfn <- filter.edges.0(all_genes, cfn) + sub.cfn.cccn <- get.co.clustered.ptms(sub.cfn, ptm.cccn.edges, ptmtable) return(sub.cfn.cccn) } @@ -717,7 +721,6 @@ ptms_to_cfn <- function(ptms, cfn = cfn.merged, pepsep = ";") { #' @return Called for its side effects in Cytoscape. #' @export setNodeMapping <- function(cf=RCy3::getTableColumns('node')) { - # require(RCy3) RCy3::setBackgroundColorDefault("#949494") # grey 58 RCy3::setNodeShapeDefault("ELLIPSE") RCy3::setNodeColorDefault("#F0FFFF") # azure1 @@ -748,7 +751,6 @@ setNodeMapping <- function(cf=RCy3::getTableColumns('node')) { #' @return Called for its side effects in Cytoscape. #' @export setCorrEdgeAppearance <- function() { - # require(RCy3) RCy3::setEdgeLineWidthDefault(3) RCy3::setEdgeColorDefault("#FFFFFF") # white edgevalues <- RCy3::getTableColumns('edge',c('Weight')) @@ -792,10 +794,9 @@ setCorrEdgeAppearance <- function() { #' @return Called for its side effects in Cytoscape. #' @export setNodeColorToRatios <- function(plotcol){ - require(RCy3) cf <- RCy3::getTableColumns('node') - if(!(plotcol %in% getTableColumnNames('node'))){ - print (getTableColumnNames('node')) + if(!(plotcol %in% RCy3::getTableColumnNames('node'))){ + print (RCy3::getTableColumnNames('node')) cat("\n","\n","\t", "Which attribute will set node size and color?") plotcol <- as.character(readLines(con = stdin(), n = 1)) } @@ -833,9 +834,9 @@ setNodeColorToRatios <- function(plotcol){ #' @return Called for its side effects in Cytoscape. #' @export setNodeColorToRowz <- function(plotcol){ - cf <- getTableColumns('node') - if(!(plotcol %in% getTableColumnNames('node'))){ - print (getTableColumnNames('node')) + cf <- RCy3::getTableColumns('node') + if(!(plotcol %in% RCy3::getTableColumnNames('node'))){ + print (RCy3::getTableColumnNames('node')) cat("\n","\n","\t", "Which attribute will set node size and color?") plotcol <- as.character(readLines(con = stdin(), n = 1)) } @@ -981,9 +982,6 @@ setNodeSizeColorIndependently <- function(sizeplotcol, colorplotcol, ratio=FALSE #' @return Called for its side effects; creates a styled network in Cytoscape. #' @export GraphCfn <- function(cfn.edges, cfn.nodes, Network.title = "CFN", Network.collection = "PTMsToPathways", visual.style.name = "PTMsToPathways.style"){ - if(!requireNamespace("RCy3", quietly = TRUE)){ - stop("In order to use this function, please download RCy3 as described in the vignette, the readme, and the function documentation.") - } tryCatch({ RCy3::cytoscapePing() }, error = function(e){ @@ -1136,6 +1134,4 @@ NodeEdgeKey <- function(visual.style.name = "PTMsToPathways.style") { RCy3::copyVisualStyle("default", visual.style.name) RCy3::setVisualStyle(visual.style.name) message("Check the 'Node & Edge Key' network in Cytoscape: kinase sources PHOSPHORYLATION/pp, all edges labelled, all node-edge types illustrated.") -} - - +} \ No newline at end of file diff --git a/R/MakeCorrelationNetwork.R b/R/MakeCorrelationNetwork.R index db3301c3..aade533e 100644 --- a/R/MakeCorrelationNetwork.R +++ b/R/MakeCorrelationNetwork.R @@ -87,7 +87,7 @@ MakeCorrelationNetwork <- function(adj.consensus.matrix, ptm.correlation.matrix) # Sum correlations in one dimension, then the other dimension gene.cccn2 <- dplyr::summarise( - dplyr::group_by(gene.cccn, Gene.Name), + dplyr::group_by(gene.cccn, .data$Gene.Name), dplyr::across( tidyselect::where(is.numeric), ~ sum(.x, na.rm = TRUE) diff --git a/R/imports.R b/R/imports.R new file mode 100644 index 00000000..ad23b8cc --- /dev/null +++ b/R/imports.R @@ -0,0 +1,2 @@ +#' @importFrom rlang .data +NULL \ No newline at end of file diff --git a/R/sysdata.rda b/R/sysdata.rda new file mode 100644 index 00000000..a3d0bd95 Binary files /dev/null and b/R/sysdata.rda differ diff --git a/data-raw/function-key.R b/data-raw/function-key.R index d3b9ed46..4eb1ff9f 100644 --- a/data-raw/function-key.R +++ b/data-raw/function-key.R @@ -9,4 +9,4 @@ function_key <- read.table( fill = TRUE ) -usethis::use_data(function_key, overwrite = TRUE) \ No newline at end of file +usethis::use_data(function_key, overwrite = TRUE, internal = TRUE) \ No newline at end of file diff --git a/man/BuildClusterFilteredNetwork.Rd b/man/BuildClusterFilteredNetwork.Rd index 38e752e4..64a750a3 100644 --- a/man/BuildClusterFilteredNetwork.Rd +++ b/man/BuildClusterFilteredNetwork.Rd @@ -36,6 +36,7 @@ If either of these conditions are not met, then it will be removed from the list This new, cluster filtered network is then assigned to the global namespace. } \examples{ -Example_Output <- BuildClusterFilteredNetwork(ex_gene_cccn_edges, ex_stringdb_edges, ex_genemania_edges) +Example_Output <- BuildClusterFilteredNetwork(ex_gene_cccn_edges, + ex_stringdb_edges, ex_genemania_edges) utils::head(Example_Output[[2]]) } diff --git a/man/cytoscape.graph.PCN.pathways.Rd b/man/cytoscape.graph.PCN.pathways.Rd index 57a1c87a..3c1a9999 100644 --- a/man/cytoscape.graph.PCN.pathways.Rd +++ b/man/cytoscape.graph.PCN.pathways.Rd @@ -4,11 +4,7 @@ \alias{cytoscape.graph.PCN.pathways} \title{Graph a Pathway Crosstalk Network in Cytoscape} \usage{ -cytoscape.graph.PCN.pathways( - PCN = pathway.crosstalk.network, - net.name, - Jaccard.edges = TRUE -) +cytoscape.graph.PCN.pathways(PCN, net.name, Jaccard.edges = TRUE) } \arguments{ \item{PCN}{A data frame representing the pathway crosstalk network with columns diff --git a/man/get.co.clustered.ptms.Rd b/man/get.co.clustered.ptms.Rd index 62794ae6..5d27acfb 100644 --- a/man/get.co.clustered.ptms.Rd +++ b/man/get.co.clustered.ptms.Rd @@ -4,13 +4,15 @@ \alias{get.co.clustered.ptms} \title{Retrieve co-clustered PTM CCCN edges for genes in an edge file} \usage{ -get.co.clustered.ptms(edge.file, ptm.cccn.edges) +get.co.clustered.ptms(edge.file, ptm.cccn.edges, ptmtable) } \arguments{ \item{edge.file}{A data frame edge list whose nodes are gene names.} \item{ptm.cccn.edges}{A data frame of PTM co-expression correlation network edges with PTM identifiers as node names.} + +\item{ptmtable}{A PTM data frame with PTM identifiers as row names and numeric condition columns.} } \value{ An edge data frame combining the original gene edges, co-clustered diff --git a/man/make.cytoscape.node.file.Rd b/man/make.cytoscape.node.file.Rd index ae498cb0..c884eb70 100644 --- a/man/make.cytoscape.node.file.Rd +++ b/man/make.cytoscape.node.file.Rd @@ -6,10 +6,11 @@ \usage{ make.cytoscape.node.file( edge.file, - funckey, + funckey = PTMsToPathways::function_key, ptmtable, include.gene.data = FALSE, - include.coclustered.PTMs = FALSE + include.coclustered.PTMs = FALSE, + ptm.cccn.edges = NULL ) } \arguments{ @@ -27,6 +28,9 @@ summarised PTM values to the node table. Defaults to \code{FALSE}.} \item{include.coclustered.PTMs}{Logical; if \code{TRUE}, add co-clustered PTM nodes and their edges to the output. Defaults to \code{FALSE}.} + +\item{ptm.cccn.edges}{A data frame of PTM co-expression correlation network +edges with PTM identifiers as node names, required if \code{include.coclustered.PTMs = TRUE}.} } \value{ A data frame of node attributes with an \code{id} column suitable diff --git a/man/ptms_to_cfn.Rd b/man/ptms_to_cfn.Rd index 10c0eda8..fd716d20 100644 --- a/man/ptms_to_cfn.Rd +++ b/man/ptms_to_cfn.Rd @@ -4,13 +4,18 @@ \alias{ptms_to_cfn} \title{Extracts co-clustered PTM and gene network from a cluster filtered network object.} \usage{ -ptms_to_cfn(ptms, cfn = cfn.merged, pepsep = ";") +ptms_to_cfn(ptms, cfn, ptm.cccn.edges, ptmtable, pepsep = ";") } \arguments{ \item{ptms}{Character vector of PTM site strings (e.g., \"TP53 p S15\")} \item{cfn}{List or data frame representing the cluster filtered network (default: global cfn.merged)} +\item{ptm.cccn.edges}{A data frame of PTM co-expression correlation network +edges with PTM identifiers as node names} + +\item{ptmtable}{A data frame containing PTM identifiers as row names and numeric condition columns} + \item{pepsep}{Character used to split ambiguous PTM entries (default: \";\")} } \value{ @@ -31,7 +36,8 @@ cfn.merged <- data.frame(source = c("TP53", "BRCA1", "BRCA1", "MDM2"), # Suppose filter.edges.0 and get.co.clustered.ptms are also defined and loaded # The following returns the gene/PTM subnetwork \dontrun{ -res <- ptms_to_cfn(ptms, cfn = cfn.merged, pepsep = ";") +res <- ptms_to_cfn(ptms, cfn = cfn.merged, + ptm.cccn.edges = ptm.cccn.edges, ptmtable = ptmtable, pepsep = ";") print(res) } } diff --git a/vignettes/BRCANetworks.Rmd b/vignettes/BRCANetworks.Rmd index 9afb285e..edc18f27 100644 --- a/vignettes/BRCANetworks.Rmd +++ b/vignettes/BRCANetworks.Rmd @@ -18,7 +18,7 @@ output: toc: true toc_depth: '4' vignette: > - %\VignetteIndexEntry{PTMsToPathways} + %\VignetteIndexEntry{BRCANetworks} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} editor_options: @@ -417,10 +417,11 @@ Cytoscape. This code graphs the CFN/CCCN from all these PTMs: ```{r eval = TRUE} funckey <- function_key -cfn.cccn <- ptms_to_cfn(mod63.clust.ptms, cfn = cfn.merged, pepsep = ";") +cfn.cccn <- ptms_to_cfn(mod63.clust.ptms, cfn = cfn.merged, ptm.cccn.edges = ptm.cccn.edges, ptmtable = ptmtable, pepsep = ";") cfn_cccn.nodes <- make.cytoscape.node.file(cfn.cccn, funckey, ptmtable, include.gene.data = TRUE, - include.coclustered.PTMs = TRUE) + include.coclustered.PTMs = TRUE, + ptm.cccn.edges = ptm.cccn.edges) ``` To graph in Cytoscape: