resolve build check warnings/errors in latest R-devel

MarioniLab · Apr 29, 2024 · b00d66c · b00d66c
1 parent 69fd5cd
commit b00d66c
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Showing 14 changed files with 44 additions and 2,364 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: miloR
 Type: Package
 Title: Differential neighbourhood abundance testing on a graph
-Version: 1.99.14
+Version: 1.99.15
 Authors@R:
     c(person("Mike", "Morgan", role=c("aut", "cre"), email="michael.morgan@abdn.ac.uk",
      comment=c(ORCID="0000-0003-0757-0711")),
@@ -68,7 +68,7 @@ Suggests:
     curl,
     scRNAseq,
     graphics
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 NeedsCompilation: no
 Packaged: 2020-07-31 14:15:28 UTC; morgan02
 Collate: 

diff --git a/NAMESPACE b/NAMESPACE
@@ -99,6 +99,7 @@ importFrom(Matrix,Matrix)
 importFrom(Matrix,colSums)
 importFrom(Matrix,crossprod)
 importFrom(Matrix,diag)
+importFrom(Matrix,kronecker)
 importFrom(Matrix,rowMeans)
 importFrom(Matrix,rowSums)
 importFrom(Matrix,solve)
@@ -156,7 +157,6 @@ importFrom(igraph,as_ids)
 importFrom(igraph,cluster_louvain)
 importFrom(igraph,components)
 importFrom(igraph,count_triangles)
-importFrom(igraph,graph.adjacency)
 importFrom(igraph,graph_from_adjacency_matrix)
 importFrom(igraph,induced_subgraph)
 importFrom(igraph,is_igraph)

diff --git a/R/buildFromAdjacency.R b/R/buildFromAdjacency.R
@@ -70,11 +70,11 @@ buildFromAdjacency <- function(x, k=NULL, is.binary=NULL, ...){
     if(is.square){
         if(!is.binary){
             bin.x <- as(matrix(as.numeric(x > 0), nrow=nrow(x)), "dgCMatrix")
-            nn.graph <- graph_from_adjacency_matrix(bin.x, mode="undirected",
+            nn.graph <- graph_from_adjacency_matrix(bin.x, mode="max",
                                                     weighted=NULL,
                                                     diag=FALSE)
         } else{
-            nn.graph <- graph_from_adjacency_matrix(x, mode="undirected",
+            nn.graph <- graph_from_adjacency_matrix(x, mode="max",
                                                     weighted=NULL,
                                                     diag=FALSE)
         }

diff --git a/R/buildNhoodGraph.R b/R/buildNhoodGraph.R
@@ -21,7 +21,7 @@
 #'
 #' NULL
 #'
-#' @importFrom igraph graph.adjacency set_vertex_attr vertex.attributes
+#' @importFrom igraph graph_from_adjacency_matrix set_vertex_attr vertex.attributes
 #' @export
 #' @rdname buildNhoodGraph
 buildNhoodGraph <- function(x, overlap=1){
@@ -42,7 +42,7 @@ buildNhoodGraph <- function(x, overlap=1){
     nhoodAdjacency(x) <- nh_intersect_mat
 
     ## Make igraph object
-    ig <- graph.adjacency(nh_intersect_mat, mode="undirected", weighted=TRUE)
+    ig <- graph_from_adjacency_matrix(nh_intersect_mat, mode="undirected", weighted=TRUE)
     nhood_sizes <- colSums(nhoods(x))
     ig <- set_vertex_attr(ig, name = 'size', value = nhood_sizes[vertex.attributes(ig)$name])
     ## Add to nhoodGraph slot in milo object

diff --git a/R/miloR.R b/R/miloR.R
@@ -4,7 +4,7 @@
 #' as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a
 #' negative bionomial generalized linear model.
 #'
-#' @docType package
+#' @aliases miloR-package
 #' @importFrom Rcpp evalCpp
 #' @useDynLib miloR
 #' @name miloR

diff --git a/man/miloR.Rd b/man/miloR.Rd
diff --git a/src/Makevars b/src/Makevars
@@ -1,5 +1,5 @@
 #PKG_CXXFLAGS = $(CXX11STD) -Wall -pedantic
 CXX11STD = CXX11
 PKG_CXXFLAGS = $(CFLAGS) $(CXX11STD) $(SHLIB_OPENMP_CXXFLAGS)
-PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)
+PKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) $(SHLIB_OPENMP_CXXFLAGS)
 ALL_CXXFLAGS = $(R_XTRA_CXXFLAGS) $(PKG_CXXFLAGS) $(CXXPICFLAGS) $(SHLIB_CXXFLAGS) $(CXXFLAGS)
diff --git a/tests/testthat/test_buildGraph.R b/tests/testthat/test_buildGraph.R
@@ -97,7 +97,7 @@ test_that("buildGraph can take different inputs", {
     expect_true(igraph::identical_graphs(miloR::graph(buildGraph(sim1.pca$x, transposed=TRUE, k=21)), sim1.graph))
 
     # input are expression values
-    graph.diff <- graph.difference(miloR::graph(buildGraph(sim1.gex, transposed=FALSE, k=21)), sim1.graph)
+    graph.diff <- difference(miloR::graph(buildGraph(sim1.gex, transposed=FALSE, k=21)), sim1.graph)
 
     expect_true(length(E(graph.diff)) == 0)
     expect_true(length(V(graph.diff)) == length(V(sim1.graph)))

diff --git a/tests/testthat/test_buildNhoodGraph.R b/tests/testthat/test_buildNhoodGraph.R
@@ -55,7 +55,7 @@ test_that("Code produced identical output", {
     rownames(nh_intersect_mat) <- colnames(nhoods)
     colnames(nh_intersect_mat) <- colnames(nhoods)
 
-    ig <- graph.adjacency(nh_intersect_mat, mode="undirected", weighted=TRUE)
+    ig <- graph_from_adjacency_matrix(nh_intersect_mat, mode="undirected", weighted=TRUE)
     nhood_sizes <- sapply(nhoods(traj_milo), length)
     ig <- set_vertex_attr(ig, name = 'size', value = nhood_sizes[vertex.attributes(ig)$name])
 

diff --git a/tests/testthat/test_testNhoods.R b/tests/testthat/test_testNhoods.R
@@ -212,11 +212,11 @@ test_that("Model contrasts provide expected results", {
                                                     design.df=sim1.meta[colnames(nhoodCounts(sim1.mylo)), ]))
 
     # test for each column of output
-    expect_equal(cont.ref$Pvalue, form.ref$Pvalue)
-    expect_equal(cont.ref$SpatialFDR, form.ref$SpatialFDR)
-    expect_equal(cont.ref$logFC, form.ref$logFC)
-    expect_equal(cont.ref$`F`, form.ref$`F`)
-    expect_equal(cont.ref$FDR, form.ref$FDR)
+    expect_equal(cont.ref$Pvalue, form.ref$Pvalue, tolerance=1e-6)
+    expect_equal(cont.ref$SpatialFDR, form.ref$SpatialFDR, tolerance=1e-6)
+    expect_equal(cont.ref$logFC, form.ref$logFC, tolerance=1e-6)
+    expect_equal(cont.ref$`F`, form.ref$`F`, tolerance=1e-6)
+    expect_equal(cont.ref$FDR, form.ref$FDR, tolerance=1e-6)
 })
 
 test_that("Providing a subset model.matrix is reproducible", {

diff --git a/vignettes/milo_contrasts.Rmd b/vignettes/milo_contrasts.Rmd
@@ -65,12 +65,17 @@ we build the KNN graph and define neighbourhoods to quantify cell abundance acro
 
 ```{r}
 # we build KNN graph
-thy.milo <- buildGraph(thy.milo, k = 10, d = 30)
-thy.milo <- makeNhoods(thy.milo, prop = 0.1, k = 10, d=30, refined = TRUE, refinement_scheme="graph") # make nhoods using graph-only as this is faster
+thy.milo <- buildGraph(thy.milo, k = 11, d = 40)
+thy.milo <- makeNhoods(thy.milo, prop = 0.2, k = 11, d=40, refined = TRUE, refinement_scheme="graph") # make nhoods using graph-only as this is faster
 colData(thy.milo)$Sample <- paste(colData(thy.milo)$SortDay, colData(thy.milo)$Age, sep="_")
 thy.milo <- countCells(thy.milo, meta.data = data.frame(colData(thy.milo)), samples="Sample") # make the nhood X sample counts matrix
 ``` 
 
+
+```{r}
+plotNhoodSizeHist(thy.milo)
+```
+
 # Differential abundance testing with contrasts
 
 Now we have the pieces in place for DA testing to demonstrate how to use contrasts. We will use these contrasts to explicitly define which groups will be 
@@ -93,7 +98,8 @@ rownames(thy.design) <- thy.design$Sample
 contrast.1 <- c("Age1wk - Age4wk") # the syntax is <VariableName><ConditionLevel> - <VariableName><ControlLevel>
 
 # we need to use the ~ 0 + Variable expression here so that we have all of the levels of our variable as separate columns in our model matrix
-da_results <- testNhoods(thy.milo, design = ~ 0 + Age, design.df = thy.design, model.contrasts = contrast.1, fdr.weighting="graph-overlap")
+da_results <- testNhoods(thy.milo, design = ~ 0 + Age, design.df = thy.design, model.contrasts = contrast.1,
+                         fdr.weighting="graph-overlap")
 table(da_results$SpatialFDR < 0.1)
 ```
 

diff --git a/vignettes/milo_gastrulation.Rmd b/vignettes/milo_gastrulation.Rmd
@@ -328,7 +328,7 @@ In these examples we settle for `overlap=5` and `max.lfc.delta=5`, as we need at
 
 ```{r, dev="jpeg"}
 set.seed(42)
-da_results <- groupNhoods(embryo_milo, da_results, max.lfc.delta = 5, overlap=5)
+da_results <- groupNhoods(embryo_milo, da_results, max.lfc.delta = 1, overlap=3)
 plotNhoodGroups(embryo_milo, da_results, layout="umap")
 plotDAbeeswarm(da_results, group.by = "NhoodGroup")
 ```
@@ -348,6 +348,14 @@ embryo_milo <- embryo_milo[keep.rows, ]
 set.seed(101)
 dec <- modelGeneVar(embryo_milo)
 hvgs <- getTopHVGs(dec, n=2000)
+
+# this vignette randomly fails to identify HVGs for some reason
+if(!length(hvgs)){
+    set.seed(42)
+    dec <- modelGeneVar(embryo_milo)
+    hvgs <- getTopHVGs(dec, n=2000)
+}
+
 head(hvgs)
 ```
 

diff --git a/vignettes/milo_glmm.Rmd b/vignettes/milo_glmm.Rmd
@@ -65,10 +65,17 @@ register(bpparam)
 
 pbmc.sce <- KotliarovPBMCData(mode="rna", ensembl=TRUE) # download the PBMC data from Kotliarov _et al._
 
+# downsample cells to reduce compute time
+prop.cells <- 0.75
+n.cells <- floor(ncol(pbmc.sce) * prop.cells)
+set.seed(42)
+keep.cells <- sample(colnames(pbmc.sce), size=n.cells)
+pbmc.sce <- pbmc.sce[, colnames(pbmc.sce) %in% keep.cells]
+
+
 # downsample the number of samples
 colData(pbmc.sce)$ObsID <- paste(colData(pbmc.sce)$tenx_lane, colData(pbmc.sce)$sampleid, sep="_")
-n.samps <- 90
-set.seed(42)
+n.samps <- 80
 keep.samps <- sample(unique(colData(pbmc.sce)$ObsID), size=n.samps)
 keep.cells <- rownames(colData(pbmc.sce))[colData(pbmc.sce)$ObsID %in% keep.samps]
 pbmc.sce <- pbmc.sce[, colnames(pbmc.sce) %in% keep.cells]

diff --git a/vignettes/milo_glmm.html b/vignettes/milo_glmm.html