From 95bc38991eff46a9119fff90cb73eb58dabc6ed9 Mon Sep 17 00:00:00 2001
From: Daena Rys <rysdaena8@gmail.com>
Date: Tue, 8 Sep 2026 12:51:38 +0300
Subject: [PATCH 1/5] Support UniFrac and pre-calculated dissimilarity in
 getPERMANOVA (#844)

- Add dis.name to getPERMANOVA,SummarizedExperiment to retrieve pre-calculated dissimilarity matrices from metadata(x).

- Automatically delegate to mia::getDissimilarity() when method %in% c('unifrac', 'overlap', 'jsd').

- Allow getPERMANOVA,ANY to accept dist objects directly and align sample metadata rows by sample names.

- Update .calculate_homogeneity() in runCCA.R to accept dist and symmetric matrices without re-running vegdist().

- Add unit tests covering on-the-fly UniFrac, direct dist inputs, dis.name usage, and input validations.

- Synchronize documentation and examples in man/getPERMANOVA.Rd.
---
 R/getPERMANOVA.R                   | 103 ++++++++++++++++++++++++-----
 R/runCCA.R                         |  11 ++-
 man/getPERMANOVA.Rd                |  32 ++++++++-
 tests/testthat/test-getPERMANOVA.R |  97 ++++++++++++++++++++++++++-
 4 files changed, 220 insertions(+), 23 deletions(-)

diff --git a/R/getPERMANOVA.R b/R/getPERMANOVA.R
index 7c5a5c786..57d43a996 100644
--- a/R/getPERMANOVA.R
+++ b/R/getPERMANOVA.R
@@ -54,6 +54,9 @@
 #' @param method \code{Character scalar}. A dissimilarity metric used in
 #' PERMANOVA and group dispersion calculation. (Default: \code{"bray"})
 #'
+#' @param dis.name \code{Character scalar}. Specifies the name of dissimilarity
+#' matrix from \code{metadata(x)} used in calculation. (Default: \code{NULL})
+#'
 #' @param test.homogeneity \code{Logical scalar}. Should the homogeneity of
 #' group dispersions be evaluated? (Default: \code{TRUE})
 #'
@@ -105,6 +108,25 @@
 #' # Significance results are similar to PERMANOVA
 #' attr(rda_res, "significance")
 #'
+#' # Perform PERMANOVA with UniFrac directly on TreeSE
+#' tse <- addPERMANOVA(
+#'     tse,
+#'     assay.type = "counts",
+#'     method = "unifrac",
+#'     formula = x ~ SampleType,
+#'     name = "unifrac_permanova",
+#'     permutations = 99
+#' )
+#'
+#' # Run PERMANOVA using a pre-calculated dissimilarity matrix from metadata
+#' tse <- addDissimilarity(tse, method = "unifrac", name = "unifrac_dist")
+#' res_precalc <- getPERMANOVA(
+#'     tse,
+#'     dis.name = "unifrac_dist",
+#'     formula = x ~ SampleType,
+#'     permutations = 99
+#' )
+#'
 #' @seealso
 #' For more details on the actual implementation see
 #' \code{\link[vegan:adonis2]{vegan::adonis2}},
@@ -128,10 +150,9 @@ setMethod("getPERMANOVA", "SingleCellExperiment", function(x,  ...){
 #' @rdname getPERMANOVA
 setMethod("getPERMANOVA", "SummarizedExperiment",
     function(
-        x, assay.type = "counts", formula = NULL, col.var = NULL, ...){
+        x, assay.type = "counts", formula = NULL, col.var = NULL,
+        dis.name = NULL, method = "bray", ...){
         ############################# Input check ##############################
-        # Assay must be present
-        .check_assay_present(assay.type, x)
         # Formula must be either correctly specified or not specified
         if( !(is.null(formula) || is(formula, "formula")) ){
             stop("'formula' must be formula or NULL.", call. = FALSE)
@@ -142,14 +163,45 @@ setMethod("getPERMANOVA", "SummarizedExperiment",
             stop("'col.var' must specify column from colData(x) or be NULL.",
                 call. = FALSE)
         }
+        if( !is.null(formula) && !is.null(col.var) ){
+            stop("Specify either 'formula' or 'col.var'.", call. = FALSE)
+        }
+        if( !.is_a_string(method) ){
+            stop("'method' must be a single character value.", call. = FALSE)
+        }
+        # User can specify either abundance matrix or dissimilarity matrix from
+        # metadata.
+        if( !is.null(dis.name) ){
+            if( !.is_a_string(dis.name) ){
+                stop("'dis.name' must be a single character value.",
+                    call. = FALSE)
+            }
+            .check_metadata_present(dis.name, x)
+            mat <- metadata(x)[[dis.name]]
+            if( is.matrix(mat) ){
+                mat <- as.dist(mat)
+            }
+            if( !inherits(mat, "dist") ){
+                stop("'", dis.name, "' in metadata(x) must be a 'dist' object or ",
+                    "a symmetric distance matrix.", call. = FALSE)
+            }
+        } else{
+            .check_assay_present(assay.type, x)
+            if( method %in% c("unifrac", "overlap", "jsd") ){
+                mat <- getDissimilarity(
+                    x, method = method, assay.type = assay.type, ...)
+            } else{
+                mat <- assay(x, assay.type)
+            }
+        }
         ########################### Input check end ############################
-        # Get abundance table, formula and related sample metadata as DF
-        mat <- assay(x, assay.type)
+        # Get formula and related sample metadata as DF
         temp <- .get_formula_and_covariates(x, formula, col.var)
         formula <- temp[["formula"]]
         covariates <- temp[["variables"]]
-        # Calculate PERMANOVA with matrix method
-        res <- getPERMANOVA(mat, formula = formula, data = covariates, ...)
+        # Calculate PERMANOVA with matrix/dist method
+        res <- getPERMANOVA(
+            mat, formula = formula, data = covariates, method = method, ...)
         return(res)
     }
 )
@@ -158,18 +210,31 @@ setMethod("getPERMANOVA", "SummarizedExperiment",
 #' @rdname getPERMANOVA
 setMethod("getPERMANOVA", "ANY", function(
         x, formula, data, method = "bray", test.homogeneity = TRUE, ...){
-    if( !is.matrix(x) ){
-        stop("'x' must be matrix.", call. = FALSE)
+    is_dist <- inherits(x, "dist")
+    if( !(is.matrix(x) || is_dist) ){
+        stop("'x' must be a matrix or a 'dist' object.", call. = FALSE)
     }
     if( !is(formula, "formula") ){
         stop("'formula' must be formula or NULL.", call. = FALSE)
     }
     if( !(is.data.frame(data) || is.matrix(data) || is(data, "DFrame")) ){
-        stop("'data' must be data.frame or coarcible to one.", call. = FALSE)
+        stop("'data' must be data.frame or coercible to one.", call. = FALSE)
+    }
+    n_samples <- if( is_dist ) attr(x, "Size") else ncol(x)
+    if( n_samples != nrow(data) ){
+        if( is_dist ){
+            stop("Number of samples in 'x' should match with number of rows in ",
+                "'data'.", call. = FALSE)
+        } else {
+            stop("Number of columns in 'x' should match with number of rows in ",
+                "'data'.", call. = FALSE)
+        }
     }
-    if( ncol(x) != nrow(data) ){
-        stop("Number of columns in 'x' should match with number of rows in ",
-            "'data'.", call. = FALSE)
+    # Align rows of data if names match
+    sample_names <- if( is_dist ) labels(x) else colnames(x)
+    if( !is.null(sample_names) && !is.null(rownames(data)) &&
+            setequal(sample_names, rownames(data)) ){
+        data <- data[sample_names, , drop = FALSE]
     }
     if( !.is_a_string(method) ){
         stop("'method' must be a single character value.", call. = FALSE)
@@ -197,7 +262,7 @@ setMethod("addPERMANOVA", "SummarizedExperiment",
     }
     # Calculate permanova
     res <- getPERMANOVA(x, ...)
-    # Addresults to metadata
+    # Add results to metadata
     x <- .add_values_to_metadata(x, name, res)
     return(x)
     }
@@ -206,7 +271,7 @@ setMethod("addPERMANOVA", "SummarizedExperiment",
 ################################ HELP FUNCTIONS ################################
 
 # This function is internal function to perform PERMANOVA from abundance
-# matrix, formula and sample metadata table.
+# matrix or distance matrix, formula and sample metadata table.
 #' @importFrom vegan adonis2
 .calculate_permanova <- function(
         x, formula, data, by = "margin", na.action = na.fail, ...){
@@ -216,8 +281,10 @@ setMethod("addPERMANOVA", "SummarizedExperiment",
     }
     #
     # Get abundance data into correct orientation. Samples must be in rows and
-    # features in columns. Also ensure that the abundance table is matrix.
-    x <- as.matrix(t(x))
+    # features in columns.
+    if( !inherits(x, "dist") ){
+        x <- as.matrix(t(x))
+    }
     # Covariate table must be data.frame
     data <- data.frame(data, check.names = FALSE)
     # Instead of letting na.action pass through, give informative error
@@ -227,7 +294,7 @@ setMethod("addPERMANOVA", "SummarizedExperiment",
             "to remove samples with missing values.", call. = FALSE)
     }
     # This next step ensures that the formula points correctly to abundance
-    # table
+    # table or distance matrix
     formula <- as.character(formula)
     formula[[2]] <- "x"
     formula <- as.formula(
diff --git a/R/runCCA.R b/R/runCCA.R
index af2ba2446..26b068d7e 100644
--- a/R/runCCA.R
+++ b/R/runCCA.R
@@ -674,10 +674,15 @@ setMethod("addRDA", "SingleCellExperiment",
             paste0(names(cols)[!cols], collapse = "','"), "'", call. = FALSE)
         variables <- variables[, cols, drop = FALSE]
     }
-    #
     # Calculate dissimilarity matrix
-    mat <- t(mat)
-    diss_mat <- vegdist(mat, method = method, ...)
+    if( inherits(mat, "dist") ){
+        diss_mat <- mat
+    } else if( is.matrix(mat) && isSymmetric(unname(mat)) ){
+        diss_mat <- as.dist(mat)
+    } else {
+        mat <- t(mat)
+        diss_mat <- vegdist(mat, method = method, ...)
+    }
     # For all variables run the analysis
     homogeneity <- lapply(colnames(variables), function(x){
         # Get variable values
diff --git a/man/getPERMANOVA.Rd b/man/getPERMANOVA.Rd
index ccb3c4574..8358c7b5b 100644
--- a/man/getPERMANOVA.Rd
+++ b/man/getPERMANOVA.Rd
@@ -15,7 +15,15 @@ addPERMANOVA(x, ...)
 
 \S4method{getPERMANOVA}{SingleCellExperiment}(x, ...)
 
-\S4method{getPERMANOVA}{SummarizedExperiment}(x, assay.type = "counts", formula = NULL, col.var = NULL, ...)
+\S4method{getPERMANOVA}{SummarizedExperiment}(
+  x,
+  assay.type = "counts",
+  formula = NULL,
+  col.var = NULL,
+  dis.name = NULL,
+  method = "bray",
+  ...
+)
 
 \S4method{getPERMANOVA}{ANY}(x, formula, data, method = "bray", test.homogeneity = TRUE, ...)
 
@@ -68,6 +76,9 @@ including covariates defined by \code{formula}.}
 \item{method}{\code{Character scalar}. A dissimilarity metric used in
 PERMANOVA and group dispersion calculation. (Default: \code{"bray"})}
 
+\item{dis.name}{\code{Character scalar}. Specifies the name of dissimilarity
+matrix from \code{metadata(x)} used in calculation. (Default: \code{NULL})}
+
 \item{test.homogeneity}{\code{Logical scalar}. Should the homogeneity of
 group dispersions be evaluated? (Default: \code{TRUE})}
 
@@ -140,6 +151,25 @@ rda_res <- getRDA(
 # Significance results are similar to PERMANOVA
 attr(rda_res, "significance")
 
+# Perform PERMANOVA with UniFrac directly on TreeSE
+tse <- addPERMANOVA(
+    tse,
+    assay.type = "counts",
+    method = "unifrac",
+    formula = x ~ SampleType,
+    name = "unifrac_permanova",
+    permutations = 99
+)
+
+# Run PERMANOVA using a pre-calculated dissimilarity matrix from metadata
+tse <- addDissimilarity(tse, method = "unifrac", name = "unifrac_dist")
+res_precalc <- getPERMANOVA(
+    tse,
+    dis.name = "unifrac_dist",
+    formula = x ~ SampleType,
+    permutations = 99
+)
+
 }
 \seealso{
 For more details on the actual implementation see
diff --git a/tests/testthat/test-getPERMANOVA.R b/tests/testthat/test-getPERMANOVA.R
index 0e586264d..1986f57b7 100644
--- a/tests/testthat/test-getPERMANOVA.R
+++ b/tests/testthat/test-getPERMANOVA.R
@@ -189,7 +189,7 @@ test_that("getPERMANOVA matches direct calculations", {
         permutations = 99
     )
     homogeneity_direct <- vegan::betadisper(
-        vegdist(t(assay(tse, "relabundance"))),
+        vegan::vegdist(t(assay(tse, "relabundance"))),
         group = tse$SampleType
     )
     
@@ -209,3 +209,98 @@ test_that("getPERMANOVA matches direct calculations", {
     expect_equal(
         res[[2]][[2]][[1]][[1]]$distances, homogeneity_direct$distances)
 })
+
+test_that("getPERMANOVA works with UniFrac and dist object directly", {
+    # Test on-the-fly UniFrac calculation on TreeSummarizedExperiment
+    set.seed(42)
+    res_unifrac <- getPERMANOVA(
+        tse, assay.type = "counts",
+        method = "unifrac",
+        formula = x ~ SampleType,
+        test.homogeneity = TRUE,
+        permutations = 99
+    )
+    expect_type(res_unifrac, "list")
+    expect_s3_class(res_unifrac$permanova, "anova.cca")
+    expect_s3_class(res_unifrac$homogeneity, "data.frame")
+
+    # Test passing a dist object directly
+    d <- getDissimilarity(tse, method = "unifrac")
+    expect_s3_class(d, "dist")
+    setseed_wrap <- set.seed(42)
+    res_dist <- suppressWarnings(getPERMANOVA(
+        d, formula = x ~ SampleType, data = colData(tse),
+        test.homogeneity = TRUE, permutations = 99
+    ))
+    expect_type(res_dist, "list")
+    expect_s3_class(res_dist$permanova, "anova.cca")
+    expect_equal(res_unifrac$permanova$SumOfSqs, res_dist$permanova$SumOfSqs)
+    expect_equal(res_unifrac$permanova$R2, res_dist$permanova$R2)
+    expect_equal(res_unifrac$permanova$F, res_dist$permanova$F)
+})
+
+test_that("getPERMANOVA and addPERMANOVA work with pre-calculated dissimilarity (dis.name)", {
+    # Pre-calculate dissimilarity and store in metadata
+    tse_diss <- addDissimilarity(tse, method = "unifrac", name = "unifrac_dist")
+    expect_true("unifrac_dist" %in% names(metadata(tse_diss)))
+
+    # Run getPERMANOVA using dis.name
+    set.seed(42)
+    res_precalc <- getPERMANOVA(
+        tse_diss, dis.name = "unifrac_dist",
+        formula = x ~ SampleType, permutations = 99
+    )
+    expect_s3_class(res_precalc$permanova, "anova.cca")
+
+    d <- getDissimilarity(tse, method = "unifrac")
+    set.seed(42)
+    res_dist <- suppressWarnings(getPERMANOVA(
+        d, formula = x ~ SampleType, data = colData(tse), permutations = 99
+    ))
+    expect_equal(res_precalc$permanova$SumOfSqs, res_dist$permanova$SumOfSqs)
+    expect_equal(res_precalc$permanova$F, res_dist$permanova$F)
+
+    # Test addPERMANOVA with dis.name
+    tse_meta <- addPERMANOVA(
+        tse_diss, dis.name = "unifrac_dist",
+        formula = x ~ SampleType, name = "perm_precalc", permutations = 99
+    )
+    expect_true("perm_precalc" %in% names(metadata(tse_meta)))
+    expect_s3_class(metadata(tse_meta)[["perm_precalc"]]$permanova, "anova.cca")
+
+    # Test addPERMANOVA with method = unifrac on-the-fly
+    tse_meta2 <- addPERMANOVA(
+        tse, method = "unifrac",
+        formula = x ~ SampleType, name = "perm_unifrac", permutations = 99
+    )
+    expect_true("perm_unifrac" %in% names(metadata(tse_meta2)))
+    expect_s3_class(metadata(tse_meta2)[["perm_unifrac"]]$permanova, "anova.cca")
+})
+
+test_that("getPERMANOVA input validations for dis.name and dist objects", {
+    # Nonexistent dis.name in metadata
+    expect_error(
+        getPERMANOVA(tse, dis.name = "nonexistent", formula = x ~ SampleType),
+        "'dis.name' must be a valid name of metadata"
+    )
+
+    # Invalid dis.name type
+    expect_error(
+        getPERMANOVA(tse, dis.name = 123, formula = x ~ SampleType),
+        "'dis.name' must be a single character value"
+    )
+
+    # Dist object with sample size mismatch
+    d <- getDissimilarity(tse, method = "bray")
+    expect_error(
+        getPERMANOVA(d, formula = x ~ SampleType, data = colData(tse)[1:10, ]),
+        "Number of samples in 'x' should match"
+    )
+
+    # Invalid x (neither matrix nor dist)
+    expect_error(
+        getPERMANOVA(1:10, formula = x ~ SampleType, data = colData(tse)),
+        "'x' must be a matrix or a 'dist' object"
+    )
+})
+

From 3d8e0b17c7a8ab2aed67072160471cde83687dce Mon Sep 17 00:00:00 2001
From: Tuomas Borman <tvborm@utu.fi>
Date: Fri, 25 Sep 2026 09:50:32 +0300
Subject: [PATCH 2/5] up

---
 DESCRIPTION                             |   2 +-
 R/addDissimilarity.R                    |   6 +-
 R/getPERMANOVA.R                        |  79 +++++-------------
 R/runCCA.R                              |  11 +--
 tests/testthat/test-5getDissimilarity.R |   6 +-
 tests/testthat/test-getPERMANOVA.R      | 102 +++++++-----------------
 6 files changed, 61 insertions(+), 145 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index a4c7e7952..a6547382d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: mia
 Type: Package
-Version: 1.21.7
+Version: 1.21.9
 Authors@R:
     c(person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
              email = "tuomas.v.borman@utu.fi",
diff --git a/R/addDissimilarity.R b/R/addDissimilarity.R
index 844d80478..323b6ec42 100644
--- a/R/addDissimilarity.R
+++ b/R/addDissimilarity.R
@@ -57,7 +57,7 @@
 #'   links, you can provide a vector with whose length equals to the number of
 #'   rows/columns in \code{x}. Alternatively, you can provide a named vector
 #'   where \code{names} represent names in abundance table and values their
-#'   corresponding node in tree. 
+#'   corresponding node in tree.
 #'
 #'   \item \code{chunkSize}: (JSD) \code{Integer scalar}. Defines the size of
 #'   data  send to the individual worker. Only has an effect, if \code{BPPARAM}
@@ -130,7 +130,7 @@
 #'
 #' Lozupone C, Knight R. ``Unifrac: a new phylogenetic method for comparing
 #' microbial communities.'' Appl Environ Microbiol. 2005 71 (12):8228-35.
-#' 
+#'
 #' McDonald D, Vázquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Xu Z,
 #' Gonzalez A, Knight R. ``Striped UniFrac: enabling microbiome analysis at
 #' unprecedented scale.'' Nat Methods. 2018 15 (11):847-848.
@@ -206,7 +206,7 @@ setMethod(
     #
     res <- getDissimilarity(x, method = method, ...)
     # Add matrix to original SE
-    x <- .add_values_to_metadata(x, names = name, values = as.matrix(res))
+    x <- .add_values_to_metadata(x, names = name, values = res)
     return(x)
     }
 )
diff --git a/R/getPERMANOVA.R b/R/getPERMANOVA.R
index 57d43a996..78c8e4528 100644
--- a/R/getPERMANOVA.R
+++ b/R/getPERMANOVA.R
@@ -45,18 +45,15 @@
 #' \code{test.homogeneity = TRUE}. \code{addPERMANOVA} adds these results to
 #' metadata of \code{x}.
 #'
-#' @inheritParams addAlpha
 #' @inheritParams runCCA
-#'
+#' @inheritParams addAlpha
+
 #' @param name \code{Character scalar}. A name for the results that will be
 #' stored to metadata. (Default: \code{"permanova"})
 #'
 #' @param method \code{Character scalar}. A dissimilarity metric used in
 #' PERMANOVA and group dispersion calculation. (Default: \code{"bray"})
 #'
-#' @param dis.name \code{Character scalar}. Specifies the name of dissimilarity
-#' matrix from \code{metadata(x)} used in calculation. (Default: \code{NULL})
-#'
 #' @param test.homogeneity \code{Logical scalar}. Should the homogeneity of
 #' group dispersions be evaluated? (Default: \code{TRUE})
 #'
@@ -108,17 +105,9 @@
 #' # Significance results are similar to PERMANOVA
 #' attr(rda_res, "significance")
 #'
-#' # Perform PERMANOVA with UniFrac directly on TreeSE
-#' tse <- addPERMANOVA(
-#'     tse,
-#'     assay.type = "counts",
-#'     method = "unifrac",
-#'     formula = x ~ SampleType,
-#'     name = "unifrac_permanova",
-#'     permutations = 99
-#' )
-#'
-#' # Run PERMANOVA using a pre-calculated dissimilarity matrix from metadata
+#' # Run PERMANOVA using a pre-calculated dissimilarity matrix from metadata.
+#' # By doing like this, you can use all dissimilarity methods from
+#' # getDissimilarity() in PERMANOVA.
 #' tse <- addDissimilarity(tse, method = "unifrac", name = "unifrac_dist")
 #' res_precalc <- getPERMANOVA(
 #'     tse,
@@ -150,8 +139,8 @@ setMethod("getPERMANOVA", "SingleCellExperiment", function(x,  ...){
 #' @rdname getPERMANOVA
 setMethod("getPERMANOVA", "SummarizedExperiment",
     function(
-        x, assay.type = "counts", formula = NULL, col.var = NULL,
-        dis.name = NULL, method = "bray", ...){
+        x, assay.type = NULL, dis.name = NULL, formula = NULL, col.var = NULL,
+         ...){
         ############################# Input check ##############################
         # Formula must be either correctly specified or not specified
         if( !(is.null(formula) || is(formula, "formula")) ){
@@ -166,33 +155,20 @@ setMethod("getPERMANOVA", "SummarizedExperiment",
         if( !is.null(formula) && !is.null(col.var) ){
             stop("Specify either 'formula' or 'col.var'.", call. = FALSE)
         }
-        if( !.is_a_string(method) ){
-            stop("'method' must be a single character value.", call. = FALSE)
-        }
         # User can specify either abundance matrix or dissimilarity matrix from
         # metadata.
-        if( !is.null(dis.name) ){
-            if( !.is_a_string(dis.name) ){
-                stop("'dis.name' must be a single character value.",
-                    call. = FALSE)
-            }
+        if( sum(c(is.null(assay.type), is.null(dis.name))) != 1L ){
+            stop("Either 'assay.type' or 'dis.name' must be specified.",
+                 call. = FALSE)
+        }
+        if( !is.null(assay.type) ){
+            # Get assay
+            .check_assay_present(assay.type, x)
+            mat <- assay(x, assay.type)
+        } else{
+            # Get dissimilarity matrix
             .check_metadata_present(dis.name, x)
             mat <- metadata(x)[[dis.name]]
-            if( is.matrix(mat) ){
-                mat <- as.dist(mat)
-            }
-            if( !inherits(mat, "dist") ){
-                stop("'", dis.name, "' in metadata(x) must be a 'dist' object or ",
-                    "a symmetric distance matrix.", call. = FALSE)
-            }
-        } else{
-            .check_assay_present(assay.type, x)
-            if( method %in% c("unifrac", "overlap", "jsd") ){
-                mat <- getDissimilarity(
-                    x, method = method, assay.type = assay.type, ...)
-            } else{
-                mat <- assay(x, assay.type)
-            }
         }
         ########################### Input check end ############################
         # Get formula and related sample metadata as DF
@@ -201,7 +177,7 @@ setMethod("getPERMANOVA", "SummarizedExperiment",
         covariates <- temp[["variables"]]
         # Calculate PERMANOVA with matrix/dist method
         res <- getPERMANOVA(
-            mat, formula = formula, data = covariates, method = method, ...)
+            mat, formula = formula, data = covariates, ...)
         return(res)
     }
 )
@@ -222,19 +198,8 @@ setMethod("getPERMANOVA", "ANY", function(
     }
     n_samples <- if( is_dist ) attr(x, "Size") else ncol(x)
     if( n_samples != nrow(data) ){
-        if( is_dist ){
-            stop("Number of samples in 'x' should match with number of rows in ",
-                "'data'.", call. = FALSE)
-        } else {
-            stop("Number of columns in 'x' should match with number of rows in ",
-                "'data'.", call. = FALSE)
-        }
-    }
-    # Align rows of data if names match
-    sample_names <- if( is_dist ) labels(x) else colnames(x)
-    if( !is.null(sample_names) && !is.null(rownames(data)) &&
-            setequal(sample_names, rownames(data)) ){
-        data <- data[sample_names, , drop = FALSE]
+        stop("Number of samples in 'x' should match with number of rows in ",
+            "'data'.", call. = FALSE)
     }
     if( !.is_a_string(method) ){
         stop("'method' must be a single character value.", call. = FALSE)
@@ -282,8 +247,8 @@ setMethod("addPERMANOVA", "SummarizedExperiment",
     #
     # Get abundance data into correct orientation. Samples must be in rows and
     # features in columns.
-    if( !inherits(x, "dist") ){
-        x <- as.matrix(t(x))
+    if( is.matrix(x) ){
+        x <- x |> t()
     }
     # Covariate table must be data.frame
     data <- data.frame(data, check.names = FALSE)
diff --git a/R/runCCA.R b/R/runCCA.R
index 26b068d7e..c06486f4d 100644
--- a/R/runCCA.R
+++ b/R/runCCA.R
@@ -675,13 +675,8 @@ setMethod("addRDA", "SingleCellExperiment",
         variables <- variables[, cols, drop = FALSE]
     }
     # Calculate dissimilarity matrix
-    if( inherits(mat, "dist") ){
-        diss_mat <- mat
-    } else if( is.matrix(mat) && isSymmetric(unname(mat)) ){
-        diss_mat <- as.dist(mat)
-    } else {
-        mat <- t(mat)
-        diss_mat <- vegdist(mat, method = method, ...)
+    if( is.matrix(mat) ){
+        mat <- vegdist(mat |> t(), method = method, ...)
     }
     # For all variables run the analysis
     homogeneity <- lapply(colnames(variables), function(x){
@@ -693,7 +688,7 @@ setMethod("addRDA", "SingleCellExperiment",
         # "missing observations due to 'group' removed"
         suppressWarnings(
             suppressMessages(
-                betadisper_res <- betadisper(diss_mat, group = var)
+                betadisper_res <- betadisper(mat, group = var)
             )
         )
         # Run significance test
diff --git a/tests/testthat/test-5getDissimilarity.R b/tests/testthat/test-5getDissimilarity.R
index bd4752b58..6dafeab94 100644
--- a/tests/testthat/test-5getDissimilarity.R
+++ b/tests/testthat/test-5getDissimilarity.R
@@ -18,13 +18,13 @@ test_that("Dissimilarity calculation", {
   }
   set.seed(123)
   res1 <- vegan::avgdist(t(mat), distfun = vegdist, dmethod = "euclidean",
-                         sample = min(colSums2(mat)), 
+                         sample = min(colSums2(mat)),
                          iterations = 10, transf = clr) |> expect_warning()
   set.seed(123)
-  res2 <- getDissimilarity(tse_sub, method = "euclidean", niter = 10, 
+  res2 <- getDissimilarity(tse_sub, method = "euclidean", niter = 10,
                            transf = clr) |> expect_warning()
   expect_equal(as.matrix(res1), as.matrix(res2))
-  
+
   # Test unifrac with named vector as node labels
   rownames(tse) <- paste0("taxa", seq_len(nrow(tse)))
   nodes <- rowLinks(tse)[["nodeLab"]]
diff --git a/tests/testthat/test-getPERMANOVA.R b/tests/testthat/test-getPERMANOVA.R
index 1986f57b7..9cdc35a58 100644
--- a/tests/testthat/test-getPERMANOVA.R
+++ b/tests/testthat/test-getPERMANOVA.R
@@ -13,7 +13,7 @@ test_that("getPERMANOVA works on SummarizedExperiment", {
         permutations = 99
     )
     expect_s3_class(res, "anova.cca")
-    
+
     # Test PERMANOVA with homogeneity check enabled
     res <- getPERMANOVA(
         tse, assay.type = "relabundance",
@@ -26,7 +26,7 @@ test_that("getPERMANOVA works on SummarizedExperiment", {
     expect_true("homogeneity" %in% names(res))
     expect_s3_class(res$permanova, "anova.cca")
     expect_s3_class(res$homogeneity, "data.frame")
-    
+
     # Test full results with nested structure validation for detailed outputs
     res <- getPERMANOVA(
         tse, assay.type = "relabundance",
@@ -70,7 +70,7 @@ test_that("getPERMANOVA input validations", {
         getPERMANOVA(
             assay(tse[, 1:10]), formula = x ~ SampleType, data = colData(tse)
         ),
-        "Number of columns in 'x' should match with number of rows in 'data'"
+        "Number of samples in 'x' should match with number of rows in 'data'"
     )
     # Check that invalid homogeneity test settings raise an error
     expect_error(
@@ -81,37 +81,37 @@ test_that("getPERMANOVA input validations", {
         ),
         "'test.homogeneity' must be TRUE or FALSE"
     )
-    
+
     # Invalid assay type (non-existent type)
     expect_error(
         getPERMANOVA(tse, assay.type = "nonexistent", formula = x ~ SampleType)
     )
-    
+
     # Invalid variable
     expect_error(
         getPERMANOVA(tse, assay.type = "relabundance", col.var = "invalid")
     )
-    
+
     # Incorrect permutations
     expect_error(
         getPERMANOVA(
-            tse, assay.type = "relabundance", formula = x ~ SampleType, 
+            tse, assay.type = "relabundance", formula = x ~ SampleType,
             permutations = -10
         )
     )
-    
+
     # Incorrect 'by' parameter
     expect_error(
         getPERMANOVA(
-            tse, assay.type = "relabundance", formula = x ~ SampleType, 
+            tse, assay.type = "relabundance", formula = x ~ SampleType,
             by = "invalid_option"
         )
     )
-    
+
     # Missing or incorrect 'homogeneity.test' option
     expect_error(
         getPERMANOVA(
-            tse, assay.type = "relabundance", formula = x ~ SampleType, 
+            tse, assay.type = "relabundance", formula = x ~ SampleType,
             homogeneity.test = "unsupported_test"
         )
     )
@@ -136,7 +136,7 @@ test_that("getPERMANOVA 'by' and 'homogeneity.test' options", {
         formula = x ~ SampleType, by = "terms"
     )
     expect_s3_class(res_by_terms$permanova, "anova.cca")
-    
+
     # Test homogeneity test with ANOVA option
     res_anova <- getPERMANOVA(
         tse, assay.type = "relabundance",
@@ -144,7 +144,7 @@ test_that("getPERMANOVA 'by' and 'homogeneity.test' options", {
         homogeneity.test = "anova"
     )
     expect_s3_class(res_anova$homogeneity, "data.frame")
-    
+
     # Test homogeneity test with Tukey HSD and full results
     res_tukey <- getPERMANOVA(
         tse, assay.type = "relabundance",
@@ -158,8 +158,8 @@ test_that("getPERMANOVA 'by' and 'homogeneity.test' options", {
 
 test_that("getPERMANOVA handles edge cases", {
     # Test handling of a missing formula (default behavior)
-    expect_no_error(getPERMANOVA(tse, assay.type = "relabundance"))
-    
+    expect_error(getPERMANOVA(tse, assay.type = "relabundance"))
+
     # Test for error when permutations count is zero
     expect_error(
         getPERMANOVA(
@@ -167,10 +167,10 @@ test_that("getPERMANOVA handles edge cases", {
             formula = x ~ SampleType, permutations = 0
         )
     )
-    
+
     # Test for error when input matrix is NULL
     expect_error(getPERMANOVA(NULL, formula = x ~ SampleType))
-    
+
     # Test for warning when only one level exists in the factor variable
     tse_subset <- tse[, tse$SampleType == "Soil"]
     expect_warning(
@@ -192,7 +192,7 @@ test_that("getPERMANOVA matches direct calculations", {
         vegan::vegdist(t(assay(tse, "relabundance"))),
         group = tse$SampleType
     )
-    
+
     # Run the getPERMANOVA function and compare results
     res <- getPERMANOVA(
         tse, assay.type = "relabundance",
@@ -201,80 +201,36 @@ test_that("getPERMANOVA matches direct calculations", {
         full = TRUE,
         permutations = 99
     )
-    
+
     # Verify permanova results match
     expect_equal(res$permanova$aov.tab, permanova_direct$aov.tab)
-    
+
     # Verify homogeneity results match
     expect_equal(
         res[[2]][[2]][[1]][[1]]$distances, homogeneity_direct$distances)
 })
 
-test_that("getPERMANOVA works with UniFrac and dist object directly", {
-    # Test on-the-fly UniFrac calculation on TreeSummarizedExperiment
-    set.seed(42)
-    res_unifrac <- getPERMANOVA(
-        tse, assay.type = "counts",
-        method = "unifrac",
-        formula = x ~ SampleType,
-        test.homogeneity = TRUE,
-        permutations = 99
-    )
-    expect_type(res_unifrac, "list")
-    expect_s3_class(res_unifrac$permanova, "anova.cca")
-    expect_s3_class(res_unifrac$homogeneity, "data.frame")
-
-    # Test passing a dist object directly
-    d <- getDissimilarity(tse, method = "unifrac")
-    expect_s3_class(d, "dist")
-    setseed_wrap <- set.seed(42)
-    res_dist <- suppressWarnings(getPERMANOVA(
-        d, formula = x ~ SampleType, data = colData(tse),
-        test.homogeneity = TRUE, permutations = 99
-    ))
-    expect_type(res_dist, "list")
-    expect_s3_class(res_dist$permanova, "anova.cca")
-    expect_equal(res_unifrac$permanova$SumOfSqs, res_dist$permanova$SumOfSqs)
-    expect_equal(res_unifrac$permanova$R2, res_dist$permanova$R2)
-    expect_equal(res_unifrac$permanova$F, res_dist$permanova$F)
-})
-
 test_that("getPERMANOVA and addPERMANOVA work with pre-calculated dissimilarity (dis.name)", {
     # Pre-calculate dissimilarity and store in metadata
-    tse_diss <- addDissimilarity(tse, method = "unifrac", name = "unifrac_dist")
-    expect_true("unifrac_dist" %in% names(metadata(tse_diss)))
+    tse <- addDissimilarity(tse, method = "unifrac", name = "unifrac_dist")
 
     # Run getPERMANOVA using dis.name
     set.seed(42)
     res_precalc <- getPERMANOVA(
-        tse_diss, dis.name = "unifrac_dist",
+        tse, dis.name = "unifrac_dist",
         formula = x ~ SampleType, permutations = 99
     )
     expect_s3_class(res_precalc$permanova, "anova.cca")
 
-    d <- getDissimilarity(tse, method = "unifrac")
     set.seed(42)
-    res_dist <- suppressWarnings(getPERMANOVA(
-        d, formula = x ~ SampleType, data = colData(tse), permutations = 99
-    ))
+    res_dist <- getPERMANOVA(
+        x = metadata(tse)[["unifrac_dist"]],
+        formula = x ~ SampleType,
+        data = colData(tse)[, "SampleType", drop = FALSE],
+        permutations = 99
+    )
     expect_equal(res_precalc$permanova$SumOfSqs, res_dist$permanova$SumOfSqs)
     expect_equal(res_precalc$permanova$F, res_dist$permanova$F)
-
-    # Test addPERMANOVA with dis.name
-    tse_meta <- addPERMANOVA(
-        tse_diss, dis.name = "unifrac_dist",
-        formula = x ~ SampleType, name = "perm_precalc", permutations = 99
-    )
-    expect_true("perm_precalc" %in% names(metadata(tse_meta)))
-    expect_s3_class(metadata(tse_meta)[["perm_precalc"]]$permanova, "anova.cca")
-
-    # Test addPERMANOVA with method = unifrac on-the-fly
-    tse_meta2 <- addPERMANOVA(
-        tse, method = "unifrac",
-        formula = x ~ SampleType, name = "perm_unifrac", permutations = 99
-    )
-    expect_true("perm_unifrac" %in% names(metadata(tse_meta2)))
-    expect_s3_class(metadata(tse_meta2)[["perm_unifrac"]]$permanova, "anova.cca")
 })
 
 test_that("getPERMANOVA input validations for dis.name and dist objects", {
@@ -287,7 +243,7 @@ test_that("getPERMANOVA input validations for dis.name and dist objects", {
     # Invalid dis.name type
     expect_error(
         getPERMANOVA(tse, dis.name = 123, formula = x ~ SampleType),
-        "'dis.name' must be a single character value"
+        "'dis.name' must be a single non-empty character value."
     )
 
     # Dist object with sample size mismatch

From 9307a6fe2063dcf279911024bdf247ebb7a6601c Mon Sep 17 00:00:00 2001
From: Tuomas Borman <tvborm@utu.fi>
Date: Fri, 25 Sep 2026 09:53:17 +0300
Subject: [PATCH 3/5] up

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index a6547382d..7d6959702 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: mia
 Type: Package
-Version: 1.21.9
+Version: 1.21.8
 Authors@R:
     c(person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
              email = "tuomas.v.borman@utu.fi",

From a94bbe617aa57ddef5c2345a624797b1a2555b09 Mon Sep 17 00:00:00 2001
From: Tuomas Borman <tvborm@utu.fi>
Date: Fri, 25 Sep 2026 12:38:20 +0300
Subject: [PATCH 4/5] up

---
 R/addDissimilarity.R    | 10 +++++-----
 man/addAlpha.Rd         |  5 +++++
 man/getDissimilarity.Rd | 20 ++++++++++++--------
 man/getPERMANOVA.Rd     | 29 ++++++++++-------------------
 man/getRPCA.Rd          |  4 ++++
 5 files changed, 36 insertions(+), 32 deletions(-)

diff --git a/R/addDissimilarity.R b/R/addDissimilarity.R
index 323b6ec42..e2c3bfbb4 100644
--- a/R/addDissimilarity.R
+++ b/R/addDissimilarity.R
@@ -160,12 +160,12 @@
 #' ### Overlap dissimilarity
 #'
 #' tse <- addDissimilarity(tse, method = "overlap", detection = 0.25)
-#' metadata(tse)[["overlap"]][1:6, 1:6]
+#' metadata(tse)[["overlap"]]
 #'
 #' ### JSD dissimilarity
 #'
 #' tse <- addDissimilarity(tse, method = "jsd")
-#' metadata(tse)[["jsd"]][1:6, 1:6]
+#' metadata(tse)[["jsd"]]
 #'
 #' # Multi Dimensional Scaling applied to JSD dissimilarity matrix
 #' tse <- addMDS(tse, method = "overlap", assay.type = "counts")
@@ -177,7 +177,7 @@
 #' dim(as.matrix(res))
 #'
 #' tse <- addDissimilarity(tse, method = "unifrac", weighted = TRUE)
-#' metadata(tse)[["unifrac"]][1:6, 1:6]
+#' metadata(tse)[["unifrac"]]
 #'
 #' ### Bray dissimilarity
 #'
@@ -185,7 +185,7 @@
 #' # transformation first
 #' tse <- transformAssay(tse, method = "relabundance")
 #' res <- getDissimilarity(tse, method = "bray", assay.type = "relabundance")
-#' as.matrix(res)[1:6, 1:6]
+#' res
 #'
 #' # If applying rarefaction, the input must be count matrix and transformation
 #' # method specified in function call (Note: increase niter)
@@ -194,7 +194,7 @@
 #' }
 #' res <- getDissimilarity(
 #'     tse, method = "euclidean", transf = rclr, niter = 2L)
-#' as.matrix(res)[1:6, 1:6]
+#' res
 #'
 NULL
 
diff --git a/man/addAlpha.Rd b/man/addAlpha.Rd
index acc06a0f2..723620d39 100644
--- a/man/addAlpha.Rd
+++ b/man/addAlpha.Rd
@@ -416,6 +416,11 @@ Efficient computation of Faith's phylogenetic diversity with applications
 in characterizing microbiomes.
 \emph{Genome Res.} 31(11):2131-2137. doi: 10.1101/gr.275777.121
 
+Armstrong G. et al. (2021)
+Efficient computation of Faith's phylogenetic diversity with applications
+in characterizing microbiomes.
+\emph{Genome Res.} 31(11):2131-2137. doi: 10.1101/gr.275777.121
+
 Beisel J-N. et al. (2003)
 A Comparative Analysis of Diversity Index Sensitivity.
 \emph{Internal Rev. Hydrobiol.} 88(1):3-15.
diff --git a/man/getDissimilarity.Rd b/man/getDissimilarity.Rd
index 0769b0e70..7b6fa8822 100644
--- a/man/getDissimilarity.Rd
+++ b/man/getDissimilarity.Rd
@@ -132,9 +132,8 @@ similarity is. When using relative abundances, overlap value 1 means that
 all the abundances of features are equal between two samples, and 0 means
 that samples have completely different relative abundances.
 
-Unifrac is calculated with \code{
-\link[ecodive:unweighted_unifrac]{ecodive:unweighted_unifrac()}}
-or \code{\link[ecodive:weighted_unifrac]{ecodive:weighted_unifrac()}}.
+Unifrac is calculated with a C++ implementation of the Striped Unifrac
+algorithm (McDonald et al. 2021).
 
 If rarefaction is enabled, \code{\link[vegan:avgdist]{vegan:avgdist()}} is
 utilized.
@@ -163,12 +162,12 @@ tse <- GlobalPatterns
 ### Overlap dissimilarity
 
 tse <- addDissimilarity(tse, method = "overlap", detection = 0.25)
-metadata(tse)[["overlap"]][1:6, 1:6]
+metadata(tse)[["overlap"]]
 
 ### JSD dissimilarity
 
 tse <- addDissimilarity(tse, method = "jsd")
-metadata(tse)[["jsd"]][1:6, 1:6]
+metadata(tse)[["jsd"]]
 
 # Multi Dimensional Scaling applied to JSD dissimilarity matrix
 tse <- addMDS(tse, method = "overlap", assay.type = "counts")
@@ -180,7 +179,7 @@ res <- getDissimilarity(tse, method = "unifrac", weighted = FALSE)
 dim(as.matrix(res))
 
 tse <- addDissimilarity(tse, method = "unifrac", weighted = TRUE)
-metadata(tse)[["unifrac"]][1:6, 1:6]
+metadata(tse)[["unifrac"]]
 
 ### Bray dissimilarity
 
@@ -188,7 +187,7 @@ metadata(tse)[["unifrac"]][1:6, 1:6]
 # transformation first
 tse <- transformAssay(tse, method = "relabundance")
 res <- getDissimilarity(tse, method = "bray", assay.type = "relabundance")
-as.matrix(res)[1:6, 1:6]
+res
 
 # If applying rarefaction, the input must be count matrix and transformation
 # method specified in function call (Note: increase niter)
@@ -197,7 +196,7 @@ rclr <- function(x){
 }
 res <- getDissimilarity(
     tse, method = "euclidean", transf = rclr, niter = 2L)
-as.matrix(res)[1:6, 1:6]
+res
 
 }
 \references{
@@ -216,6 +215,11 @@ microbial communities.'' Appl Environ Microbiol. 2007
 Lozupone C, Knight R. ``Unifrac: a new phylogenetic method for comparing
 microbial communities.'' Appl Environ Microbiol. 2005 71 (12):8228-35.
 
+McDonald D, Vázquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Xu Z,
+Gonzalez A, Knight R. ``Striped UniFrac: enabling microbiome analysis at
+unprecedented scale.'' Nat Methods. 2018 15 (11):847-848.
+doi: 10.1038/s41592-018-0187-8.
+
 For JSD dissimilarity:
 Jensen-Shannon Divergence and Hilbert space embedding.
 Bent Fuglede and Flemming Topsoe University of Copenhagen,
diff --git a/man/getPERMANOVA.Rd b/man/getPERMANOVA.Rd
index 8358c7b5b..da7fb4f51 100644
--- a/man/getPERMANOVA.Rd
+++ b/man/getPERMANOVA.Rd
@@ -17,11 +17,10 @@ addPERMANOVA(x, ...)
 
 \S4method{getPERMANOVA}{SummarizedExperiment}(
   x,
-  assay.type = "counts",
+  assay.type = NULL,
+  dis.name = NULL,
   formula = NULL,
   col.var = NULL,
-  dis.name = NULL,
-  method = "bray",
   ...
 )
 
@@ -30,7 +29,7 @@ addPERMANOVA(x, ...)
 \S4method{addPERMANOVA}{SummarizedExperiment}(x, name = "permanova", ...)
 }
 \arguments{
-\item{x}{a \code{\link[SummarizedExperiment]{SummarizedExperiment}} object.}
+\item{x}{\code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment}}.}
 
 \item{...}{additional arguments passed to \code{vegan::adonis2}.
 \itemize{
@@ -56,7 +55,10 @@ Options include 'permanova'
 }}
 
 \item{assay.type}{\code{Character scalar}. Specifies the name of assay
-used in calculation. (Default: \code{"counts"})}
+used in calculation. (Default: \code{NULL})}
+
+\item{dis.name}{\code{Character scalar}. Specifies the name of dissimilarity
+matrix from \code{metadata} slot used in calculation. (Default: \code{NULL})}
 
 \item{formula}{\code{formula}. If \code{x} is a
 \code{\link[SummarizedExperiment:SummarizedExperiment-class]{SummarizedExperiment}}
@@ -76,9 +78,6 @@ including covariates defined by \code{formula}.}
 \item{method}{\code{Character scalar}. A dissimilarity metric used in
 PERMANOVA and group dispersion calculation. (Default: \code{"bray"})}
 
-\item{dis.name}{\code{Character scalar}. Specifies the name of dissimilarity
-matrix from \code{metadata(x)} used in calculation. (Default: \code{NULL})}
-
 \item{test.homogeneity}{\code{Logical scalar}. Should the homogeneity of
 group dispersions be evaluated? (Default: \code{TRUE})}
 
@@ -151,17 +150,9 @@ rda_res <- getRDA(
 # Significance results are similar to PERMANOVA
 attr(rda_res, "significance")
 
-# Perform PERMANOVA with UniFrac directly on TreeSE
-tse <- addPERMANOVA(
-    tse,
-    assay.type = "counts",
-    method = "unifrac",
-    formula = x ~ SampleType,
-    name = "unifrac_permanova",
-    permutations = 99
-)
-
-# Run PERMANOVA using a pre-calculated dissimilarity matrix from metadata
+# Run PERMANOVA using a pre-calculated dissimilarity matrix from metadata.
+# By doing like this, you can use all dissimilarity methods from
+# getDissimilarity() in PERMANOVA.
 tse <- addDissimilarity(tse, method = "unifrac", name = "unifrac_dist")
 res_precalc <- getPERMANOVA(
     tse,
diff --git a/man/getRPCA.Rd b/man/getRPCA.Rd
index 0a7a73a33..78fb5ea77 100644
--- a/man/getRPCA.Rd
+++ b/man/getRPCA.Rd
@@ -125,6 +125,10 @@ reconstructed matrix, and reconstruction error) stored in attributes.
 data("ibdmdb")
 mae <- ibdmdb
 
+# Apply filtering
+mae[[1]] <- filterRPCAInput(mae[[1]], assay.type = "mgx")
+mae[[2]] <- filterRPCAInput(mae[[2]], assay.type = "mtx")
+
 # Apply data transformations. With impute=FALSE, missing values are preserved
 # and not imputed.
 mae[[1]] <- transformAssay(

From 19d2c8e428f7dfb93dd9367255150937b4e65e60 Mon Sep 17 00:00:00 2001
From: Tuomas Borman <tvborm@utu.fi>
Date: Fri, 25 Sep 2026 13:33:36 +0300
Subject: [PATCH 5/5] up

---
 tests/testthat/test-5calculateOverlap.R | 16 +++++-----------
 tests/testthat/test-getPERMANOVA.R      |  2 +-
 2 files changed, 6 insertions(+), 12 deletions(-)

diff --git a/tests/testthat/test-5calculateOverlap.R b/tests/testthat/test-5calculateOverlap.R
index 9e0c360c9..c200567ac 100644
--- a/tests/testthat/test-5calculateOverlap.R
+++ b/tests/testthat/test-5calculateOverlap.R
@@ -2,7 +2,7 @@
 context("Overlap")
 
 test_that("Overlap", {
-    
+
     # Get data
     data(esophagus, package="mia")
     tse <- esophagus
@@ -11,7 +11,7 @@ test_that("Overlap", {
         getDissimilarity(tse, method = "overlap", assay.type = "relabundance", detection = 0.15))
     expect_error(getDissimilarity(tse, method = "overlap", detection = "TEST"))
     expect_error(getDissimilarity(tse, method = "overlap", detection = TRUE))
-    
+
     # Calculate overlap
     tse <- transformAssay(tse, method = "relabundance")
     result <- getDissimilarity(tse, method = "overlap", assay.type = "relabundance", detection = 0.15)
@@ -20,7 +20,7 @@ test_that("Overlap", {
     expect_true(class(result) == "dist")
     # Test values
     result <- as.matrix(result)
-    
+
     # Reference
     reference <- matrix(c(0.0000000, 0.3799961, 0.4111838, 0.3799961, 0.0000000,
                           0.3634972, 0.4111838, 0.3634972, 0.0000000), nrow=3)
@@ -28,16 +28,10 @@ test_that("Overlap", {
     # Test with different detection threshold
     result <- getDissimilarity(tse, method = "overlap", assay.type = "relabundance", detection = 0)
     result <- as.matrix(result)
-    
+
     # Reference
-    reference <- matrix(c(0.0000000, 0.8811552, 0.9038734, 0.8811552, 0.0000000, 
+    reference <- matrix(c(0.0000000, 0.8811552, 0.9038734, 0.8811552, 0.0000000,
                           0.8390008, 0.9038734, 0.8390008, 0.00000000), nrow=3)
     expect_equal(round(unname(result), 7), round(reference, 7))
-    
-    # Test .calculate_overlap
-    expect_equal( metadata(addDissimilarity(tse, "overlap"))[["overlap"]], 
-                  as.matrix(getDissimilarity(tse, method = "overlap")) )
-    expect_equal( metadata(addDissimilarity(tse, method = "overlap", assay.type = "relabundance", detection = 0.08))[["overlap"]], 
-                  as.matrix(getDissimilarity(tse, method = "overlap", assay.type = "relabundance", detection = 0.08)) )
 })
 
diff --git a/tests/testthat/test-getPERMANOVA.R b/tests/testthat/test-getPERMANOVA.R
index 9cdc35a58..a50197ee2 100644
--- a/tests/testthat/test-getPERMANOVA.R
+++ b/tests/testthat/test-getPERMANOVA.R
@@ -158,7 +158,7 @@ test_that("getPERMANOVA 'by' and 'homogeneity.test' options", {
 
 test_that("getPERMANOVA handles edge cases", {
     # Test handling of a missing formula (default behavior)
-    expect_error(getPERMANOVA(tse, assay.type = "relabundance"))
+    expect_no_error(getPERMANOVA(tse, assay.type = "relabundance"))
 
     # Test for error when permutations count is zero
     expect_error(