microbiome · RiboRings · Sep 9, 2025 · Sep 9, 2025 · Sep 9, 2025 · Sep 9, 2025
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,5 +1,5 @@
 Package: miaDash
-Version: 1.1.4
+Version: 1.1.5
 Authors@R: 
     c(person(given = "Giulio", family = "Benedetti", role = c("aut", "cre"),
            email = "giulio.benedetti@utu.fi",
@@ -25,6 +25,7 @@ Depends:
     shiny
 Imports:
     ape,
+    bluster,
     htmltools,
     iSEEtree (>= 1.1.4),
     mia,
@@ -50,6 +51,6 @@ Suggests:
 URL: https://github.com/microbiome/miaDash
 BugReports: https://github.com/microbiome/miaDash/issues
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 VignetteBuilder: knitr
 Config/testthat/edition: 3
diff --git a/NAMESPACE b/NAMESPACE
@@ -9,6 +9,10 @@ importFrom(SummarizedExperiment,rowData)
 importFrom(TreeSummarizedExperiment,TreeSummarizedExperiment)
 importFrom(TreeSummarizedExperiment,rowTree)
 importFrom(ape,read.tree)
+importFrom(bluster,DmmParam)
+importFrom(bluster,HclustParam)
+importFrom(bluster,KmeansParam)
+importFrom(bluster,NNGraphParam)
 importFrom(htmltools,HTML)
 importFrom(htmltools,br)
 importFrom(htmltools,div)
@@ -27,6 +31,7 @@ importFrom(iSEEtree,RowGraphPlot)
 importFrom(iSEEtree,RowTreePlot)
 importFrom(iSEEtree,ScreePlot)
 importFrom(mia,addAlpha)
+importFrom(mia,addCluster)
 importFrom(mia,addHierarchyTree)
 importFrom(mia,addPrevalence)
 importFrom(mia,addPrevalentAbundance)

diff --git a/NEWS b/NEWS
@@ -1,3 +1,6 @@
+Changes in version 1.1.5
+* Added Cluster tab
+
 Changes in version 1.1.4
 * Switch from csv to tsv support
 * Improve interoperability with MGnify datasets

diff --git a/R/constants.R b/R/constants.R
@@ -18,7 +18,8 @@
 #' @keywords internal
 #' @name constants
 #' @aliases .miaDashDefaultPanels .miaDashOtherPanels .transformMethods
-#' .alphaMetrics .betaMetrics .betaMethods .qualityMetrics
+#' .qualityMetrics .alphaMetrics .betaMetrics .betaMethods .clustMethods
+#' .DmmCriteria
 NULL
 
 #' @rdname constants
@@ -36,6 +37,11 @@ NULL
     "hellinger", "log", "log10", "log2", "max", "normalize", "pa",# "philr",
     "range", "rank", "rclr", "relabundance", "rrank", "standardize", "total")
 
+#' @rdname constants
+.qualityMetrics <- list("Library size" = "PerCellQC",
+    "Prevalence" = "Prevalence", "Prevalent abundance" = "PrevalentAbundance",
+    "Hierarchy tree" = "HierarchyTree")
+
 #' @rdname constants
 .alphaMetrics <- c("coverage_diversity", "fisher_diversity", "faith_diversity",
     "gini_simpson_diversity", "inverse_simpson_diversity",
@@ -54,6 +60,7 @@ NULL
 #"TSNE", "UMAP")
 
 #' @rdname constants
-.qualityMetrics <- list("Library size" = "PerCellQC",
-    "Prevalence" = "Prevalence", "Prevalent abundance" = "PrevalentAbundance",
-    "Hierarchy tree" = "HierarchyTree")
+.clustMethods <- c("Dmm", "Hclust", "Kmeans", "NNGraph")
+
+#' @rdname constants
+.DmmCriteria <- c("laplace", "AIC", "BIC")
diff --git a/R/landing_page.R b/R/landing_page.R
@@ -93,23 +93,26 @@
                             div(style = "margin-top: -20px"),
 
                             conditionalPanel(
-                                condition = "input.ftype == 'Mothur' | input.ftype == 'QIIME2'",
+                                condition = paste0("input.ftype == 'Mothur' | ",
+                                    "input.ftype == 'QIIME2'"),
 
                                 fileInput(inputId = "f.rowdata",
                                     label = "rowData:", accept = c(".taxonomy",
                                     ".qza"), placeholder = "taxonomy or qza"),
                                 div(style = "margin-top: -20px")),
 
                             conditionalPanel(
-                                condition = "input.ftype == 'biom' | input.ftype == 'MetaPhlAn'",
+                                condition = paste0("input.ftype == 'biom' | ",
+                                    "input.ftype == 'MetaPhlAn'"),
 
                                 fileInput(inputId = "tree.file",
                                     label = "rowTree:", placeholder = "tree.tree",
                                     accept = c(".tree", ".tre", ".qza")),
                                 div(style = "margin-top: -20px")),
 
                             conditionalPanel(
-                                condition = "input.ftype == 'biom' | input.ftype == 'HUMAnN'",
+                                condition = paste0("input.ftype == 'biom' | ",
+                                    "input.ftype == 'HUMAnN'"),
 
                                 checkboxInput(inputId = "rm.tax.pref",
                                     label = "Remove taxa prefixes")),
@@ -204,7 +207,7 @@
                                 inline = TRUE),
 
                             conditionalPanel(
-                                condition = paste("input.bmethod == 'MDS' || ",
+                                condition = paste0("input.bmethod == 'MDS' || ",
                                     "input.bmethod == 'NMDS' || ",
                                     "input.bmethod == 'RDA'"),
 
@@ -222,6 +225,49 @@
                                 label = "Number of components:", min = 1,
                                 step = 1)),
 
+                        tabPanel(title = "Cluster", value = "cluster",
+
+                            radioButtons(inputId = "cmethod",
+                                label = "Method:", choices = .clustMethods,
+                                inline = TRUE),
+
+                            conditionalPanel(
+                                condition = paste0("input.cmethod == 'Dmm' | ",
+                                    "input.cmethod == 'Kmeans'"),
+
+                                numericInput(inputId = "kclusters", value = 3,
+                                    label = "Number of clusters:", min = 1,
+                                    step = 1)),
+
+                            conditionalPanel(
+                                condition = "input.cmethod == 'Dmm'",
+
+                                selectInput(inputId = "dmm.type",
+                                    label = "Criterion:",
+                                    choices = .DmmCriteria,
+                                    selected = .DmmCriteria[1]),
+
+                                numericInput(inputId = "dmm.seed", value = 1L,
+                                    label = "Seed:", min = 0, step = 1)),
+
+                            conditionalPanel(
+                                condition = "input.cmethod == 'NNGraph'",
+
+                                numericInput(inputId = "kneighbours",
+                                    value = 10, label = "Number of neighbours:",
+                                    min = 1, step = 1),
+
+                                checkboxInput(inputId = "nn.shared",
+                                    label = "Construct shared graph")),
+
+                            checkboxInput(inputId = "clust.full",
+                                label = "Add to metadata"),
+
+                            radioButtons(inputId = "clust.margin",
+                                label = "Margin:", inline = TRUE,
+                                choices = c("samples", "features"),
+                                selected = "features")),
+
                         footer = textInput(inputId = "estimate.name",
                             label = "Name:")),
 

diff --git a/R/observers.R b/R/observers.R
@@ -67,7 +67,7 @@
                 rObjects$tse <- .update_tse(
                      rObjects$tse, TreeSummarizedExperiment, fun_args
                 )
-                print(input$taxa.from.rownames)
+
                 if( input$taxa.from.rownames ){
 
                     rObjects$tse <- .update_tse(
@@ -223,11 +223,12 @@
 #' @rdname create_observers
 #' @importFrom stats as.formula
 #' @importFrom mia addAlpha runNMDS runRDA getDissimilarity addHierarchyTree
-#'   addPrevalence addPrevalentAbundance
+#'   addPrevalence addPrevalentAbundance addCluster
 #' @importFrom TreeSummarizedExperiment rowTree
 #' @importFrom scater runMDS runPCA
 #' @importFrom scuttle addPerCellQC
 #' @importFrom vegan vegdist
+#' @importFrom bluster KmeansParam DmmParam HclustParam NNGraphParam
 .create_estimate_observers <- function(input, rObjects) {
 
     # nocov start
@@ -295,7 +296,8 @@
                     name <- input$bmethod
                 }
 
-                beta_args <- list(x = rObjects$tse, assay.type = input$assay.type,
+                beta_args <- list(x = rObjects$tse,
+                    assay.type = input$estimate.assay,
                     ncomponents = input$ncomponents, name = name)
 
                 if( input$beta.index == "unifrac" ){
@@ -329,14 +331,58 @@
 
                     beta_args <- c(beta_args,
                         formula = as.formula(input$rda.formula))
-
                 }
 
                 beta_fun <- eval(parse(text = paste0("run", input$bmethod)))
 
                 rObjects$tse <- .update_tse(rObjects$tse, beta_fun, beta_args)
             })
 
+        }else if( input$estimate == "cluster" ){
+
+            isolate({
+                req(input$estimate.assay)
+
+                if( input$estimate.name != "" ){
+                    name <- input$estimate.name
+                }else{
+                    name <- "clusters"
+                }
+
+                if( input$cmethod == "Dmm" ){
+
+                    blus_params <- list(k = input$kclusters,
+                        type = deparse(input$dmm.type))#, seed = input$dmm.seed)
+
+                }else if( input$cmethod == "Hclust" ){
+
+                    blus_params <- list()
+
+                }else if( input$cmethod == "Kmeans" ){
+
+                    blus_params <- list(centers = input$kclusters)
+
+                }else if( input$cmethod == "NNGraph" ){
+
+                    blus_params <- list(shared = input$nn.shared,
+                        k = input$kneighbours)
+
+                }
+
+                blus_params <- sprintf("%s=%s", names(blus_params), blus_params)
+                blus_params <- paste(blus_params, collapse = ", ")
+                blus_fun <- sprintf("%sParam(%s)", input$cmethod, blus_params)
+
+                clust_args <- list(x = rObjects$tse,
+                    assay.type = input$estimate.assay,
+                    by = input$clust.margin, full = input$clust.full, 
+                    BLUSPARAM = eval(parse(text = blus_fun)),
+                    name = name, clust.col = name)
+
+                rObjects$tse <- .update_tse(
+                    rObjects$tse, addCluster, clust_args)
+            })
+
         }
 
     }, ignoreInit = TRUE, ignoreNULL = TRUE)

diff --git a/man/constants.Rd b/man/constants.Rd