up

Author: TuomasBorman

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: OMA
 Title: Orchestrating Microbiome Analysis with Bioconductor
-Version: 0.99.2
-Date: 2026-03-24
+Version: 0.99.3
+Date: 2026-03-25
 Authors@R:
     c(
   person(given = "Tuomas", family = "Borman", role = c("aut", "cre"), email = "tuomas.v.borman@utu.fi", comment = c(ORCID = "0000-0002-8563-8884")),

inst/pages/clustering.qmd

@@ -127,7 +127,7 @@ to visualize ecological differences between the clusters.
 library(dendextend)
 
 # Get hclust data from metadata
-hclust_data <- metadata(altExp(tse, "prevalent"))$clusters$hclust
+hclust_data <- metadata(altExp(tse, "prevalent"))$clustering$hclust
 
 # Get the dendrogram object
 dendro <- as.dendrogram(hclust_data)
@@ -430,7 +430,7 @@ tse <- addCluster(
     assay.type = "standardize", HclustParam(method = "complete"),
     full = TRUE
 )
-taxa_hclust <- metadata(tse)[["clusters"]][["hclust"]]
+taxa_hclust <- metadata(tse)[["clustering"]][["hclust"]]
 
 # Creates a tree
 taxa_tree <- as.phylo(taxa_hclust)
@@ -493,15 +493,15 @@ Now we can create a heatmap with additional annotations.
 
 # Order the data based on clusters
 tse <- tse[
-    order(rowData(tse)[["clusters"]]),
-    order(colData(tse)[["clusters"]])
+    order(rowData(tse)[["cluster"]]),
+    order(colData(tse)[["cluster"]])
 ]
 
 # Get data for plotting
 mat <- assay(tse, "standardize")
-taxa_clusters <- rowData(tse)[, "clusters", drop = FALSE] |>
+taxa_clusters <- rowData(tse)[, "cluster", drop = FALSE] |>
     as.data.frame()
-sample_data <- colData(tse)[, c("clusters", "Gender"), drop = FALSE] |>
+sample_data <- colData(tse)[, c("cluster", "Gender"), drop = FALSE] |>
     as.data.frame()
 colnames(sample_data) <- c("clusters_col", "gender")