Fix all devtools check issues

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

NAMESPACE

@@ -43,3 +43,4 @@ export(strip.cy.goo)
 importFrom(gplots,heatmap.2)
 importFrom(grDevices,colorRampPalette)
 importFrom(grDevices,hcl)
+importFrom(rlang,.data)

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 ----------------------------------------

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

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.")
-}
-
-
+}

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)

R/imports.R

@@ -0,0 +1,2 @@
+#' @importFrom rlang .data
+NULL

data-raw/function-key.R

@@ -9,4 +9,4 @@ function_key <- read.table(
   fill = TRUE
 )
 
-usethis::use_data(function_key, overwrite = TRUE)
+usethis::use_data(function_key, overwrite = TRUE, internal = TRUE)