Merge branch 'master' of https://github.com/mixOmicsTeam/mixOmics

.github/workflows/actions.yml

@@ -5,6 +5,7 @@ on:
   pull_request:
     branches:
       - master
+  workflow_dispatch:  # Allow manual triggers
   schedule:
     - cron: '0 8 * * 4'
 
@@ -180,7 +181,7 @@ jobs:
         shell: Rscript {0}
 
       - name: Check --as-cran ✅ ✅ 
-        if: github.event_name == 'schedule' && github.repository == 'mixOmicsTeam/mixOmics'
+        if: (github.event_name == 'schedule' && github.repository == 'mixOmicsTeam/mixOmics') || (github.event_name == 'workflow_dispatch' && github.repository == 'mixOmicsTeam/mixOmics')
         env:
           _R_CHECK_CRAN_INCOMING_: false
         run: |
@@ -208,7 +209,7 @@ jobs:
         shell: bash
 
       - name: Push to DockerHub 📦
-        if: needs.versions.outputs.dockerise == 'true' && !contains(github.event.head_commit.message, 'skip-docker')
+        if: runner.os == 'Linux' && needs.versions.outputs.dockerise == 'true' && !contains(github.event.head_commit.message, 'skip-docker')
         uses: docker/build-push-action@v1
         with:
           username: ${{ secrets.DOCKER_USERNAME }}
@@ -218,7 +219,7 @@ jobs:
           build_args: BIOC_VERSION=${{ needs.versions.outputs.bioc }} 
 
       - name: Test coverage 🔍
-        if: github.event_name == 'schedule' && github.repository == 'mixOmicsTeam/mixOmics'
+        if: (github.event_name == 'schedule' && github.repository == 'mixOmicsTeam/mixOmics') || (github.event_name == 'workflow_dispatch' && github.repository == 'mixOmicsTeam/mixOmics')
         # the commands between 'set +e' and 'set -e' are run without throwing any error
         run: |
           set +e

DESCRIPTION

@@ -25,13 +25,13 @@ Imports:
     ggrepel,
     BiocParallel,
     utils,
-    gsignal
+    gsignal,
+    rgl
 Suggests: 
     BiocStyle,
     knitr,
     rmarkdown,
     testthat,
-    rgl,
     microbenchmark,
     magick
 Authors@R: 

R/S3methods-plot.R

@@ -54,10 +54,10 @@ plot.pca  <- function(x,
 #' nutri.res <- rcc(X, Y, lambda1 = 0.064, lambda2 = 0.008)
 #' 
 #' ## 'pointplot' type scree
-#' plot(nutri.res) #(default)
+#' plot(nutri.res, type = "point") #(default)
 #' 
 #' \dontrun{
-#' plot(nutri.res, pch = 19, cex = 1.2,
+#' plot(nutri.res, type = "point", pch = 19, cex = 1.2,
 #' col = c(rep("red", 3), rep("darkblue", 18)))
 #' 
 #' ## 'barplot' type scree

R/check_entry.R

@@ -202,8 +202,8 @@ Check.entry.pls = function(X, Y, ncomp, keepX, keepY, test.keepX, test.keepY,
     if (!(logratio %in% c("none", "CLR")))
         stop("Choose one of the two following logratio transformation: none or CLR")
 
-    # if DA and the unmapped Y has rows without associated class
-    if (DA) {
+    # if DA and the unmapped Y has rows without associated class, only run this check if Y is not null (ie multilevel)
+    if (DA && !is.null(Y)) {
       if (length(which(rowSums(Y)==0)) != 0) {
         stop("Unmapped Y contains samples with no associated class. May be caused by NAs in input Y vector")
       }

R/plotLoadings.R

@@ -466,9 +466,8 @@ plotLoadings.mixo_plsda <-
                                title = paste(legend.title),
                                cex = size.legend)
                     }
-
-                    contrib.df <- c(contrib.df, list(df))
                 } # end if plot
+                contrib.df <- c(contrib.df, list(df))
             }
 
             names(contrib.df) <- block

R/utils.R

@@ -472,9 +472,9 @@ nearZeroVar = function (x, freqCut = 95/5, uniqueCut = 10)
     if (!all(is.list(X) && length(unique(names(X))) == length(X)))
         stop("'X' must be a named list. See documentation.", call. = FALSE)
 
-    if ((missing(indY) || is.null(indY)) ) {
+    if ( missing(indY) || is.null(indY) || isTRUE(tryCatch(is.integer(as.integer(indY)) && indY > length(X))) ){
         indY <- length(X) + 1
-        X <- c(X, list(Y = matrix())) ## just so we have Y in X for design
+        X <- c(X, list(Y = matrix())) ## just so we have Y in X for design, ind
     }
 
     blocks <- names(X)

examples/network-examples.R

@@ -26,10 +26,12 @@ show.edge.labels = FALSE)
 dev.off()
 
 
-## interactive 'cutoff'
-
-network(nutri.res, comp = 1:3, cutoff = 0.55, interactive = TRUE)
-## select the 'cutoff' and "see" the new network
+## interactive 'cutoff' - select the 'cutoff' and "see" the new network
+## only run this during an interactive session
+if (interactive()) {
+    network(nutri.res, comp = 1:3, cutoff = 0.55, interactive = TRUE)
+}
+dev.off()
 
 ## network representation for objects of class 'spls'
 data(liver.toxicity)

examples/plotLoadings-examples.R

@@ -58,7 +58,7 @@ name.var = name.var, size.name = 0.5,complete.name.var=TRUE, contrib = "max")
 # look at the contribution (median) for each variable
 plot.contrib = plotLoadings(splsda.liver, comp = 2, method = 'median', plot = FALSE,
 contrib = "max")
-head(plot.contrib[,1:4])
+head(plot.contrib[[1]][,1:4])
 # change the title of the legend and title name
 plotLoadings(splsda.liver, comp = 2, method = 'median', legend.title = 'Time',
 title = 'Contribution plot', contrib = "max")

examples/splsda-examples.R

@@ -67,6 +67,7 @@ plotIndiv(splsda.liver, ind.names = Y, ellipse = TRUE, legend = TRUE)
 ## Fifth example: 16S data with multilevel decomposion and log ratio transformation
 # ------------------------------------------------
 
+data(diverse.16S)
 splsda.16S = splsda(
 X = diverse.16S$data.TSS,  # TSS normalised data
 Y =  diverse.16S$bodysite,

examples/tune.block.splsda-examples.R

@@ -28,8 +28,11 @@ subset <- mixOmics:::stratified.subsampling(breast.TCGA$data.train$subtype, fold
 data <- lapply(data, function(omic) omic[subset,])
 Y <- breast.TCGA$data.train$subtype[subset]
 ## ---- run
-## setup cluster - use SnowParam() on Widnows
-BPPARAM <- BiocParallel::MulticoreParam(workers = parallel::detectCores()-1)
+# Check if the environment variable exists (during R CMD check) and limit cores accordingly
+max_cores <- if (Sys.getenv("_R_CHECK_LIMIT_CORES_") != "") 2 else parallel::detectCores() - 1
+# Setup the parallel backend with the appropriate number of workers
+BPPARAM <- BiocParallel::MulticoreParam(workers = max_cores)
+
 tune <- tune.block.splsda(
     X = data,
     Y = Y,

examples/tune.spca-examples.R

@@ -12,9 +12,10 @@ tune.spca.res
 plot(tune.spca.res)
 \dontrun{
 ## parallel processing using BiocParallel on repeats with more workers (cpus)
-## You can use BiocParallel::MulticoreParam() on non_Windows machines 
-## for faster computation
-BPPARAM <- BiocParallel::SnowParam(workers = max(parallel::detectCores()-1, 2))
+# Check if the environment variable exists (during R CMD check) and limit cores accordingly
+max_cores <- if (Sys.getenv("_R_CHECK_LIMIT_CORES_") != "") 2 else parallel::detectCores() - 1
+# Setup the parallel backend with the appropriate number of workers
+BPPARAM <- BiocParallel::MulticoreParam(workers = max_cores)
 tune.spca.res <- tune.spca(
     X = nutrimouse$lipid,
     ncomp = 2,