fix errors

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: PSMatch
 Title: Handling and Managing Peptide Spectrum Matches
-Version: 1.9.0
+Version: 1.9.1
 Authors@R:
    c(person(given = "Laurent", family = "Gatto",
             email = "[email protected]",
@@ -54,7 +54,7 @@ Suggests:
 License: Artistic-2.0
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 VignetteBuilder: knitr
 BugReports: https://github.com/RforMassSpectrometry/PSM/issues
 URL: https://github.com/RforMassSpectrometry/PSM

NAMESPACE

@@ -56,7 +56,7 @@ importFrom(igraph,V)
 importFrom(igraph,cluster_louvain)
 importFrom(igraph,components)
 importFrom(igraph,graph_from_adjacency_matrix)
-importFrom(igraph,graph_from_incidence_matrix)
+importFrom(igraph,graph_from_biadjacency_matrix)
 importFrom(igraph,groups)
 importFrom(igraph,layout_nicely)
 importFrom(igraph,modularity)

NEWS.md

@@ -1,3 +1,13 @@
+# PSMatch 1.9
+
+## PSMatch 1.9.1
+
+- Fix check errors.
+
+## PSMatch 1.9.0
+
+- New Bioc devel.
+
 # PSMatch 1.7
 
 ## PSMatch 1.7.2

R/ConnectedComponent-class.R

@@ -353,7 +353,7 @@ prioritiseConnectedComponents <- function(x) {
     ## community metrics
     com_metrics <- t(vapply(cc_x,
                      function(xx) {
-                         g <- graph_from_incidence_matrix(xx)
+                         g <- graph_from_biadjacency_matrix(xx)
                          com <- cluster_louvain(g)
                          c(n_coms = length(com),
                            mod_coms = modularity(com))

R/adjacencyMatrix-plot.R

@@ -1,4 +1,4 @@
-##' @importFrom igraph layout_nicely graph_from_incidence_matrix V V<- plot.igraph set_vertex_attr
+##' @importFrom igraph layout_nicely graph_from_biadjacency_matrix V V<- plot.igraph set_vertex_attr
 ##'
 ##' @export
 ##'
@@ -37,7 +37,7 @@ plotAdjacencyMatrix <- function(m,
                                 protColors = 0,
                                 pepColors = NULL,
                                 layout = igraph::layout_nicely) {
-    g <- graph_from_incidence_matrix(m)
+    g <- graph_from_biadjacency_matrix(m)
     if (is.character(protColors)) {
         ## Expecting a named vector of colour characters
         if (is.null(names(protColors)))

tests/testthat/test_ConnectedComponents.R

@@ -35,35 +35,36 @@ test_that("ConnectedComponents works from PSM", {
 
 
 test_that("prioritiseConnectedComponents() works", {
-    set.seed(1)
     cc <- ConnectedComponents(adj)
+    set.seed(1)
     p1 <- prioritiseConnectedComponents(cc)
+    set.seed(1)
     p2 <- prioritizeConnectedComponents(cc)
     expect_identical(p1, p2)
     ## Check CC 4
     cc_i <- connectedComponents(cc, 4)
     expect_equal(p1["4", "ncol"], ncol(cc_i))
     expect_equal(p1["4", "nrow"], nrow(cc_i))
     expect_identical(p1["4", "n"], sum(cc_i))
-    expect_identical(p1["4", "rs_min"], min(rowSums(cc_i)))
-    expect_identical(p1["4", "rs_max"], max(rowSums(cc_i)))
-    expect_identical(p1["4", "cs_min"], min(colSums(cc_i)))
-    expect_identical(p1["4", "cs_max"], max(colSums(cc_i)))
+    expect_identical(p1["4", "rs_min"], min(Matrix::rowSums(cc_i)))
+    expect_identical(p1["4", "rs_max"], max(Matrix::rowSums(cc_i)))
+    expect_identical(p1["4", "cs_min"], min(Matrix::colSums(cc_i)))
+    expect_identical(p1["4", "cs_max"], max(Matrix::colSums(cc_i)))
     expect_identical(p1["4", "sparsity"], sum(cc_i == 0)/(ncol(cc_i) * nrow(cc_i)))
-    cl <- igraph::cluster_louvain(igraph::graph_from_incidence_matrix(cc_i))
+    cl <- igraph::cluster_louvain(igraph::graph_from_biadjacency_matrix(cc_i))
     expect_identical(p1["4", "n_coms"], as.numeric(length(cl)))
     expect_identical(p1["4", "mod_coms"], igraph::modularity(cl))
     ## Check CC 3
     cc_i <- connectedComponents(cc, 3)
     expect_equal(p1["3", "ncol"], ncol(cc_i))
     expect_equal(p1["3", "nrow"], nrow(cc_i))
     expect_identical(p1["3", "n"], sum(cc_i))
-    expect_identical(p1["3", "rs_min"], min(rowSums(cc_i)))
-    expect_identical(p1["3", "rs_max"], max(rowSums(cc_i)))
-    expect_identical(p1["3", "cs_min"], min(colSums(cc_i)))
-    expect_identical(p1["3", "cs_max"], max(colSums(cc_i)))
+    expect_identical(p1["3", "rs_min"], min(Matrix::rowSums(cc_i)))
+    expect_identical(p1["3", "rs_max"], max(Matrix::rowSums(cc_i)))
+    expect_identical(p1["3", "cs_min"], min(Matrix::colSums(cc_i)))
+    expect_identical(p1["3", "cs_max"], max(Matrix::colSums(cc_i)))
     expect_identical(p1["3", "sparsity"], sum(cc_i == 0)/(ncol(cc_i) * nrow(cc_i)))
-    cl <- igraph::cluster_louvain(igraph::graph_from_incidence_matrix(cc_i))
+    cl <- igraph::cluster_louvain(igraph::graph_from_biadjacency_matrix(cc_i))
     expect_identical(p1["3", "n_coms"], as.numeric(length(cl)))
     expect_identical(p1["3", "mod_coms"], igraph::modularity(cl))
 })