diff --git a/.Rbuildignore b/.Rbuildignore
index c46ab48e..0f56b95e 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -5,3 +5,5 @@
 .RData
 .pdf
 inst/extras/
+^doc$
+^Meta$
diff --git a/.gitignore b/.gitignore
index b424f209..2bab5502 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,6 @@ inst/doc
 docs
 .Rhistory
 *.RData
+.DS_Store
+/doc/
+/Meta/
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..75245367
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,349 @@
+# Contributing to this R package
+
+## Etiquette and conduct
+
+Contributors and maintainers are expected to abide by the
+[Bioconductor Code of Conduct](https://bioconductor.org/about/code-of-conduct/)
+
+This instructions have been adapted from 
+
+- [(Bioconductor contributor guidelines)](https://github.com/Bioconductor/Contributions/blob/master/CONTRIBUTING.md)
+- [(BioJulia contributor guidelines)](https://github.com/BioJulia/Contributing)
+
+
+## How can I contribute?
+
+
+### Reporting Bugs
+
+Following these guidelines will help us to better understand your
+report, replicate the issues, and track down the source problems.
+
+#### Before creating a bug report:
+
+Please do the following:
+
+1. Check the GitHub issues for this package
+
+2. If you find an open issue that describes the same problem, kindly add a comment to let
+  others know that you are experiencing the same issue.
+
+3. If no **open** issue already exists for your problem 
+   then kindly create a new issue.
+
+   > **Note:** If you find a **Closed** issue that seems like it is the same thing
+   > that you're experiencing, open a new issue and include a link to the original
+   > issue in the body of the new one.
+
+#### How to create a (good) new bug report:
+
+Bugs are tracked as [GitHub issues](https://guides.github.com/features/issues/).
+
+When you are creating a bug report, please do the following:
+
+1. **Explain the problem**
+
+   - *Use a clear and descriptive title* for the issue to identify the problem.
+   - *Describe the exact steps which reproduce the problem* in as many details as possible.
+   - *Provide a specific example*. (Includes links to pastebin, gists and so on.)
+       If you're providing snippets in the issue, use
+       [Markdown code blocks](https://help.github.com/articles/markdown-basics/#multiple-lines).
+
+   - *Describe the behaviour you observed after following the steps*
+     - Point out what exactly is the problem with that behaviour.
+     - *Explain which behaviour you expected to see instead and why.*
+     - *OPTIONALLY: Include screenshots and animated GIFs* which show you
+       following the described steps.
+
+2. **Provide additional context for the problem (some of these may not always apply)**
+
+   - *Did the problem start happening recently* (e.g. after updating to a new version)?
+     - If the problem started recently, *can you reproduce the problem in older versions?*
+     - Do you know the most recent package version in which the problem doesn't happen?
+
+   - *Can you reliably reproduce the issue?* If not...
+     - Provide details about how often the problem happens.
+     - Provide details about under which conditions it normally happens.
+
+   - Is the problem is related to *working with files*? If so....
+     - Does the problem happen for all files and projects or only some?
+     - Does the problem happen only when working with local or remote files?
+     - Does the problem happen for files of a specific type, size, or encoding?
+     - Is there anything else special about the files you are using?
+
+3. **Include details about your configuration and environment**
+
+- Packages and versions you are using? (see R command sessionInfo())
+
+- Name and version of the OS you're using?
+
+
+### Suggesting enhancements
+
+Enhancements include new features as well as minor improvements to
+existing functionality. Following these suggestions will help
+maintainers and the community understand your suggestion and find
+related suggestions.
+
+#### Before Submitting An Enhancement Proposal
+
+1. **Perform a cursory issue search** to see if the enhancement has already been suggested.
+2. If it has not, open a new issue as per the guidance below.
+3. If it has...
+  1. Add a comment to the existing issue instead of opening a new one.
+  2. If it was closed, take the time to understand why this was so (it's ok to
+     ask! :) ), and consider whether anything has changed that makes the reason
+     outdated. If you can think of a convincing reason to reconsider the
+     enhancement, feel free to open a new issue as per the guidance below.
+
+#### How to submit a (good) new enhancement proposal
+
+Enhancement proposals are tracked as
+[GitHub issues](https://guides.github.com/features/issues/).
+
+1. **Explain the enhancement**
+   - *Use a clear and descriptive title* for the issue to identify the suggestion.
+   - *Provide a step-by-step description of the suggested enhancement* in as many details as possible.
+   - *Provide specific examples to demonstrate the steps*.
+     Include copy/pasteable snippets which you use in those examples, as
+     [Markdown code blocks](https://help.github.com/articles/markdown-basics/#multiple-lines).
+
+   - If you want to change current behaviour...
+     - Describe the *current* behaviour.
+     - *Explain which behaviour you expected* to see instead and *why*.
+     - *Will the proposed change alter APIs or existing exposed methods/types?*
+       If so, this may cause dependency issues and breakages, so the maintainer
+       will need to consider this when versioning the next release.
+   - *OPTIONALLY: Include screenshots and animated GIFs*.
+
+2. **Provide additional context for the enhancement**
+
+   - *Explain why this enhancement would be useful* to users and
+     isn't something that can or should be implemented as a separate package.
+
+   - *Do you know of other projects where this enhancement exists?*
+
+3. **Include details about your configuration and environment**
+
+   - Package versions (see R sessionInfo()).
+
+   - Name and version of the OS
+
+
+### Making Pull Requests
+
+
+All packages can be developed locally, and contributions suggested via pull requests.
+
+Let's use a git flow kind of approach. Development version should be done 
+against the `master` branch and then merged to `release` for release. 
+(https://guides.github.com/introduction/flow/)
+
+Before you start working on code, it is often a good idea to open an enhancement
+[suggestion](#suggest-an-enhancement)
+
+Hit the 'Fork' button on the repositories page to create a forked copy
+of the target package for your own Github account. This will ensure
+your work and experiments won't hinder other users of the released and
+stable package.
+
+From there you can clone your fork of the package and work on it on your
+machine using git.
+Here's an example of cloning, assuming you already forked `miaViz`:
+
+```sh
+git clone https://github.com/<YOUR_GITHUB_USERNAME_HERE>/miaViz.git
+```
+
+Git will download or "clone" your fork and put it in your local folder.
+
+It is beyond the scope of this document to describe good git and github use in
+more specific detail, as the folks at Git and GitHub have already done that wonderfully
+on their own sites.
+
+If you have additional questions,
+see the contact details at [microbiome.github.io](microbiome.github.io).
+
+#### How to make (good) code contributions and new Pull-Requests
+
+1. **In your code changes**
+
+   - **Branch properly!**
+     - If you are making a bug-fix, then you need to checkout your bug-fix branch
+       from the last release tag.
+     - If you are making a feature addition or other enhancement, checkout your
+       branch from master.
+     - See [here](#a-suggested-branching-model) for more information (or ask a package maintainer :smile:).
+
+   - Please comment liberally for complex pieces of internal code to facilitate comprehension.
+
+2. **In your pull request**
+
+   - *Describe* the changes in the pull request
+
+   - Provide a *clear, simple, descriptive title*.
+
+   - Do not include issue numbers in the PR title.
+
+   - If you have implemented *new features* or behaviour
+     - *Provide a description of the addition* in as many details as possible.
+     - *Provide justification of the addition*.
+     - *Provide a runnable example of use of your addition*. This lets reviewers
+       and others try out the feature before it is merged or makes it's way to release.
+
+   - If you have *changed current behaviour*...
+     - *Describe the behaviour prior to you changes*
+     - *Describe the behaviour after your changes* and justify why you have made the changes.
+     - *Does your change alter APIs or existing exposed methods/types?*
+       If so, this may cause dependency issues and breakages, so the maintainer
+       will need to consider this when versioning the next release.
+     - If you are implementing changes that are intended to increase performance, you
+       should provide the results of a simple performance benchmark exercise
+       demonstrating the improvement. Especially if the changes make code less legible.
+
+#### Reviews and merging
+
+You can open a pull request early on and push changes to it until it is ready,
+or you can do all your editing locally and make a pull request only when it is
+finished - it is up to you.
+
+When your pull request is ready on Github,
+mention one of the maintainers of the repo in a comment e.g. `@antagomir`
+and ask them to review it.
+You can also use Github's review feature.
+They will review the code and documentation in the pull request,
+and will assess it.
+
+Your pull request will be accepted and merged if:
+
+1. The dedicated package maintainers approve the pull request for merging.
+2. The automated build system confirms that all unit tests pass without any issues.
+
+It may also be that the reviewers or package maintainers will want to
+you to make changes to your pull request before they will merge it.
+Take the time to understand why any such request has been made, and
+freely discuss it with the reviewers.
+
+## Styleguides
+
+### Git Commit messages
+
+* Use the present tense ("Add feature" not "Added feature").
+* Use the imperative mood ("Move cursor to..." not "Moves cursor to...").
+* Limit the first line to 72 characters or less.
+* Reference issues and pull requests liberally after the first line.
+* Consider starting the commit message with an applicable emoji:
+    * :art: `:art:` when improving the format/structure of the code
+    * :racehorse: `:racehorse:` when improving performance
+    * :memo: `:memo:` when writing docs
+    * :penguin: `:penguin:` when fixing something on Linux
+    * :apple: `:apple:` when fixing something on macOS
+    * :checkered_flag: `:checkered_flag:` when fixing something on Windows
+    * :bug: `:bug:` when fixing a bug
+    * :fire: `:fire:` when removing code or files
+    * :green_heart: `:green_heart:` when fixing the CI build
+    * :white_check_mark: `:white_check_mark:` when adding tests
+    * :arrow_up: `:arrow_up:` when upgrading dependencies
+    * :arrow_down: `:arrow_down:` when downgrading dependencies
+    * :exclamation: `:exclamation:` when removing warnings or depreciations
+
+### Additional style suggestions
+
+- Indent with 4 spaces.
+
+- Separate logical blocks of code with one blank line. Although it is common
+  and acceptable for short single-line functions to be defined together on
+  consecutive lines with no blank lines between them.
+
+- Function names, apart from constructors, are all camelCase.
+
+- Generally try to keep lines below 80-columns, unless splitting a long line
+  onto multiple lines makes it harder to read.
+
+
+## How to develop the package
+
+A minimal workflow for package development is as follows.
+
+1. _Fork_ (with Git) miaViz R package from [https://github.com/microbiome/miaViz]
+
+1. _Clone_ (with Git) your own fork of miaViz; or if you have cloned it
+  earlier, then make sure that your branch is in sync with the
+  _upstream_ repository (see next section)
+
+1. Test package (in R): `devtools::test()` (and fix possible issues)
+
+1. Test examples: `devtools::run_examples()`
+
+1. Update documentation (convert R files to man/ files): `devtools::document()` 
+
+1. Build package: `devtools::build()`
+
+1. Run Bioconductor checks: `BiocCheck::BiocCheck()` (for more details on Bioconductor formatting guidelines and automated checks, see [Bioconductor package guidelines](https://bioconductor.org/packages/devel/bioc/vignettes/BiocCheck/inst/doc/BiocCheck.html) and instructions on [Bioconductor packages: Development, Maintenance, and Peer Review](https://contributions.bioconductor.org/))
+
+1. Load the updated package: `devtools::load_all()`
+
+1. Install the package: `devtools::install()`
+
+1. Once the package passes all build checks, commit & push to your own
+   fork, then make a pull request (PR) to the original
+   repository. This will trigger additional automated checks. If they
+   fail, inspect the reason, fix accordingly, and update your PR
+   (simply commit + push new material to your fork; the PR is
+   already open).
+
+
+Final checks after the R tests; run on command line (replace "R" with
+your custom R path if necessary):
+
+- `R CMD build `miaViz/`
+- `R CMD check miaViz_x.y.z.tar.gz`
+- `R CMD BiocCheck miaViz_x.y.z.tar.gz`
+- `R CMD INSTALL miaViz_x.y.z.tar.gz`
+
+
+After updating the package, remember to do the following (if applicable):
+
+- update documentation 
+- update unit tests (in `tests/`)
+- update vignettes (in `vignettes`)
+- run all checks from above
+
+After accepted pull request, check if further updates are needed in:
+
+- OMA
+- other related packages (e.g. mia, miaSim, miaTime)
+
+
+## Sync with the official Github development version
+
+Before new pull request, check that your own fork is in sync with the
+main development version. Command line version goes as follows.
+
+```
+# Add the main repository as your _upstream_  
+`git remote add upstream git@github.com:microbiome/miaViz.git`
+
+# Fetch the changes from upstream (main version) and origin (your fork):
+git fetch --all
+
+# Merge those changes to your fork:
+git merge origin/master
+git merge upstream/master
+
+# Finally, commit and push to origin (your version)
+# then open a new PR from the Github site if relevant.
+git push origin master
+```
+
+## Syncing with the official Bioconductor version
+
+This is usually done only by the main developer. In this case, the upstream will be set to Bioconductor: `git remote add upstream git@git.bioconductor.org:packages/miaViz.git`
+
+- [Workflow to sync between github and bioc versions](https://bioconductor.org/developers/how-to/git/sync-existing-repositories/)
+
+- [Bioconductor Build reports (devel)](https://bioconductor.org/checkResults/devel/bioc-LATEST/)
+
+- [Package page in Bioconductor](https://bioconductor.org/packages/miaViz/)
+
+
diff --git a/DESCRIPTION b/DESCRIPTION
index 95e74db4..1c3551eb 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: miaViz
 Title: Microbiome Analysis Plotting and Visualization
-Version: 1.9.3
+Version: 1.9.5
 Authors@R: 
     c(person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
              email = "tuomas.v.borman@utu.fi",
@@ -12,6 +12,9 @@ Authors@R:
              email = "leo.lahti@iki.fi",
              comment = c(ORCID = "0000-0001-5537-637X")),
       person(given = "Basil", family = "Courbayre", role = c("ctb")),
+      person(given = "Giulio", family = "Benedetti", role = c("ctb"),
+             email = "giulio.benedetti@utu.fi",
+             comment = c(ORCID = "0000-0002-8732-7692")),
       person(given = "Muluh", family = "Muluh", role=c("ctb"))
 	     )
 Description: The miaViz package implements functions to visualize
@@ -47,7 +50,9 @@ Imports:
     tidyr,
     dplyr,
     ape,
-    DirichletMultinomial
+    DirichletMultinomial,
+    ggrepel,
+    SingleCellExperiment
 Suggests:
     knitr,
     rmarkdown,
@@ -56,8 +61,7 @@ Suggests:
     patchwork,
     vegan,
     microbiomeDataSets,
-    bluster,
-    miaTime
+    bluster
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
 VignetteBuilder: knitr
diff --git a/NAMESPACE b/NAMESPACE
index c4a449db..387a8e02 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,11 +1,13 @@
 # Generated by roxygen2: do not edit by hand
 
 export(plotAbundanceDensity)
+export(plotCCA)
 export(plotColGraph)
 export(plotColTile)
 export(plotDMNFit)
 export(plotPrevalence)
 export(plotPrevalentAbundance)
+export(plotRDA)
 export(plotRowGraph)
 export(plotRowTile)
 export(plotSeries)
@@ -16,12 +18,14 @@ exportMethods(colTreeData)
 exportMethods(combineTreeData)
 exportMethods(plotAbundance)
 exportMethods(plotAbundanceDensity)
+exportMethods(plotCCA)
 exportMethods(plotColGraph)
 exportMethods(plotColTile)
 exportMethods(plotColTree)
 exportMethods(plotDMNFit)
 exportMethods(plotPrevalence)
 exportMethods(plotPrevalentAbundance)
+exportMethods(plotRDA)
 exportMethods(plotRowGraph)
 exportMethods(plotRowTile)
 exportMethods(plotRowTree)
@@ -45,6 +49,8 @@ importFrom(DelayedArray,rowMeans)
 importFrom(DelayedArray,rowSums)
 importFrom(DirichletMultinomial,mixture)
 importFrom(S4Vectors,metadata)
+importFrom(SingleCellExperiment,reducedDim)
+importFrom(SingleCellExperiment,reducedDimNames)
 importFrom(SummarizedExperiment,assay)
 importFrom(SummarizedExperiment,colData)
 importFrom(SummarizedExperiment,rowData)
@@ -90,6 +96,8 @@ importFrom(ggplot2,scale_y_discrete)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_bw)
 importFrom(ggplot2,theme_classic)
+importFrom(ggrepel,geom_label_repel)
+importFrom(ggrepel,geom_text_repel)
 importFrom(ggtree,geom_cladelab)
 importFrom(ggtree,geom_highlight)
 importFrom(ggtree,geom_nodelab)
@@ -105,6 +113,7 @@ importFrom(rlang,"!!")
 importFrom(rlang,":=")
 importFrom(rlang,sym)
 importFrom(scater,plotExpression)
+importFrom(scater,plotReducedDim)
 importFrom(scater,retrieveCellInfo)
 importFrom(scater,retrieveFeatureInfo)
 importFrom(stats,sd)
diff --git a/NEWS b/NEWS
index f51dd3fa..f30f55fa 100644
--- a/NEWS
+++ b/NEWS
@@ -20,4 +20,5 @@ Changes in version 1.7.x
 
 Changes in version 1.9.x
 + Updated plotDMN to work with newest mia version
++ Added plotCCA and plotRDA functions
 + deprecate agglomerateByRank and agglomerateByPrevalence
diff --git a/R/plotAbundance.R b/R/plotAbundance.R
index 22bdd805..2d485522 100644
--- a/R/plotAbundance.R
+++ b/R/plotAbundance.R
@@ -110,7 +110,7 @@
 #' ## Compositional barplot with top 5 taxa and samples sorted by "Bacteroidetes"
 #' 
 #' # Getting top taxa on a Phylum level
-#' se <- transformCounts(se, method="relabundance")
+#' se <- transformAssay(se, method="relabundance")
 #' se_phylum <- mergeFeaturesByRank(se, rank ="Phylum", onRankOnly=TRUE)
 #' top_taxa <- getTopTaxa(se_phylum,top = 5, assay.type = "relabundance")
 #' 
diff --git a/R/plotAbundanceDensity.R b/R/plotAbundanceDensity.R
index b8d3ea8f..b8d314d6 100644
--- a/R/plotAbundanceDensity.R
+++ b/R/plotAbundanceDensity.R
@@ -94,7 +94,7 @@
 #' plotAbundanceDensity(tse, assay.type = "counts")
 #' 
 #' # Counts relative abundances
-#' tse <- transformCounts(tse, method = "relabundance")
+#' tse <- transformAssay(tse, method = "relabundance")
 #' 
 #' # Plots the relative abundance of 10 most abundant taxa. 
 #' # "nationality" information is used to color the points. X-axis is log-scaled.
diff --git a/R/plotCCA.R b/R/plotCCA.R
new file mode 100644
index 00000000..1ad2eec3
--- /dev/null
+++ b/R/plotCCA.R
@@ -0,0 +1,582 @@
+#' Plot RDA or CCA object
+#'
+#' \code{plotRDA} and \code{plotCCA} create an RDA/CCA plot starting from the
+#' output of \code{\link[mia:runCCA]{CCA and RDA}} functions, two common methods
+#' for supervised ordination of microbiome data.
+#'
+#' @param object a
+#'   \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-constructor]{TreeSummarizedExperiment}}
+#'   or a matrix of weights. The latter is returned as output from \code{\link[mia:runCCA]{calculateRDA}}.
+#' 
+#' @param dimred A string or integer scalar indicating the reduced dimension to
+#'   plot. This is the output of \code{\link[mia:runCCA]{runRDA}} and resides in
+#'   \code{reducedDim(tse, dimred)}.
+#' 
+#' @param add.ellipse One of \code{c(TRUE, FALSE, "fill", "colour", "color")},
+#'   indicating whether ellipses should be present, absent, filled or colored.
+#'   (default: \code{ellipse.fill = TRUE})
+#'
+#' @param ellipse.alpha Number between 0 and 1 to adjust the opacity of ellipses.
+#'   (default: \code{ellipse.alpha = 0.2})
+#'
+#' @param ellipse.linewidth Number specifying the size of ellipses.
+#'   (default: \code{ellipse.linewidth = 0.1})
+#' 
+#' @param ellipse.linetype Discrete number specifying the style of ellipses.
+#'   (default: \code{ellipse.linetype = 1})
+#'
+#' @param add.vectors TRUE or FALSE, should vectors appear in the plot.
+#'    (default: \code{add.vectors = TRUE})
+#' 
+#' @param vec.size Number specifying the size of vectors.
+#'   (default: \code{vec.size = 0.5})
+#' 
+#' @param vec.colour String specifying the colour of vectors.
+#'   (default: \code{vec.color = "black"})
+#' 
+#' @param vec.color Alias for `vec.colour`.
+#' 
+#' @param vec.linetype Discrete number specifying the style of vector lines.
+#'   (default: \code{vec.linetype = 1})
+#' 
+#' @param arrow.size Number specifying the size of arrows.
+#'   (defaults: \code{arrow.size = 0.25})
+#' 
+#' @param label.size Number specifying the size of text and labels.
+#'   (default: \code{label.size = 4})
+#' 
+#' @param label.colour String specifying the colour of text and labels.
+#'   (default: \code{label.color = "black"})
+#' 
+#' @param label.color Alias for `label.colour`.
+#' 
+#' @param sep.group String specifying the separator used in the labels.
+#'   (default: \code{sep.group = "\U2012"})
+#'   
+#' @param repl.underscore String used to replace underscores in the labels.
+#'   (default: \code{repl.underscore = " "})
+#' 
+#' @param vec.text TRUE or FALSE, should text instead of labels be used to label vectors.
+#'   (default: \code{vec.text = TRUE})
+#' 
+#' @param repel.labels TRUE or FALSE, should labels be repelled.
+#'   (default: \code{repel.labels = TRUE})
+#'
+#' @param parse.labels TRUE or FALSE, should labels be parsed.
+#'   (default: \code{parse.labels = TRUE})
+#'
+#' @param add.significance TRUE or FALSE, should explained variance and p-value
+#'   appear in the labels. (default: \code{add.significance = TRUE})
+#'
+#' @param add.expl.var TRUE or FALSE, should explained variance appear on the
+#'   coordinate axes. (default: \code{add.expl.var = TRUE})
+#' 
+#' @param ... additional parameters for plotting, inherited from
+#'   \code{\link[scater:plotReducedDim]{plotReducedDim}},
+#'   \code{\link[ggplot2:geom_label]{geom_label}} and
+#'   \code{\link[ggrepel:geom_label_repel]{geom_label_repel}}.
+#' 
+#' @details
+#' \code{plotRDA} and \code{plotCCA} create an RDA/CCA plot starting from the
+#' output of \code{\link[mia:runCCA]{CCA and RDA}} functions, two common methods
+#' for supervised ordination of microbiome data. Either a
+#' \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-constructor]{TreeSummarizedExperiment}}
+#' or a matrix object is supported as input. When the input is a
+#' TreeSummarizedExperiment, this should contain the output of runRDA
+#' in the reducedDim slot and the argument \code{dimred} needs to be defined.
+#' When the input is a matrix, this should be returned as output from
+#' calculateRDA. However, the first method is recommended because it provides
+#' the option to adjust aesthetics to the colData variables through the
+#' arguments inherited from \code{\link[scater:plotReducedDim]{plotReducedDim}}.
+#' 
+#' @return 
+#' A \code{ggplot2} object 
+#'
+#' @name plotCCA
+#'
+#' @examples
+#' # Load dataset
+#' library(miaViz)
+#' data("enterotype", package = "mia")
+#' tse <- enterotype
+#'  
+#' # Run RDA and store results into TreeSE
+#' tse <- runRDA(tse,
+#'               formula = assay ~ ClinicalStatus + Gender + Age,
+#'               FUN = vegan::vegdist,
+#'               distance = "bray",
+#'               na.action = na.exclude)
+#'                
+#' # Create RDA plot coloured by variable
+#' plotRDA(tse, "RDA",
+#'         colour_by = "ClinicalStatus")
+#'  
+#' # Create RDA plot with empty ellipses
+#' plotRDA(tse, "RDA",
+#'         colour_by = "ClinicalStatus",
+#'         add.ellipse = "colour")
+#'  
+#' # Create RDA plot with text encased in labels
+#' plotRDA(tse, "RDA",
+#'         colour_by = "ClinicalStatus",
+#'         vec.text = FALSE)
+#'  
+#' # Create RDA plot without repelling text
+#' plotRDA(tse, "RDA",
+#'         colour_by = "ClinicalStatus",
+#'         repel.labels = FALSE)
+#'  
+#' # Create RDA plot without vectors
+#' plotRDA(tse, "RDA",
+#'         colour_by = "ClinicalStatus",
+#'         add.vectors = FALSE)
+#'  
+#' # Calculate RDA as a separate object
+#' rda_mat <- calculateRDA(tse,
+#'                         formula = assay ~ ClinicalStatus + Gender + Age,
+#'                         FUN = vegan::vegdist,
+#'                         distance = "bray",
+#'                         na.action = na.exclude)
+#'  
+#' # Create RDA plot from RDA matrix
+#' plotRDA(rda_mat)
+NULL
+
+#' @rdname plotCCA
+#' @aliases plotRDA
+#' @export
+setGeneric("plotCCA", signature = c("object"),
+           function(object, ...) standardGeneric("plotCCA"))
+
+#' @rdname plotCCA
+#' @aliases plotRDA
+#' @export
+setMethod("plotCCA", signature = c(object = "SingleCellExperiment"),
+    function(object, dimred, ...){
+        # Reproduce plotRDA function
+        return(plotRDA(object, dimred, ...))
+    }
+)
+
+#' @rdname plotCCA
+#' @aliases plotRDA
+#' @export
+setMethod("plotCCA", signature = c(object = "matrix"),
+    function(object, ...){
+        # Reproduce plotRDA function
+        return(plotRDA(object, ...))
+    }
+)
+
+#' @rdname plotCCA
+#' @aliases plotCCA
+#' @export
+setGeneric("plotRDA", signature = c("object"),
+    function(object, ...) standardGeneric("plotRDA"))
+
+#' @rdname plotCCA
+#' @aliases plotCCA
+#' @export
+setMethod("plotRDA", signature = c(object = "SingleCellExperiment"),
+    function(object, dimred,
+             add.ellipse = TRUE, ellipse.alpha = 0.2, ellipse.linewidth = 0.1, ellipse.linetype = 1,
+             vec.size = 0.5, vec.color = vec.colour, vec.colour = "black", vec.linetype = 1,
+             arrow.size = 0.25, label.color = label.colour, label.colour = "black", label.size = 4,
+             vec.text = TRUE, repel.labels = TRUE, sep.group = "\U2012", repl.underscore = " ",
+             add.significance = TRUE, add.expl.var = TRUE, add.vectors = TRUE, parse.labels = TRUE, ...){
+        ###################### Input check ########################
+        if( !(add.ellipse %in% c(TRUE, FALSE, "fill", "color", "colour")) ){
+            stop("'add.ellipse' must be one of c(TRUE, FALSE, 'fill', 'color', 'colour').", call. = FALSE)
+        }
+        if ( !.is_a_bool(add.vectors) ){
+            stop("'add.vectors must be TRUE or FALSE.", call. = FALSE)
+        }
+        if ( !add.vectors ){
+            warning("'add.vectors' is FALSE, so other arguments for vectors and labels will be disregarded.", call. = FALSE)
+        }
+        if( !.is_a_bool(vec.text) ){
+            stop("'vec.text' must be TRUE or FALSE.", call. = FALSE)
+        }
+        if( !.is_a_bool(repel.labels) ){
+            stop("'repel.labels' must be TRUE or FALSE.", call. = FALSE)
+        }
+        if( !.is_a_bool(parse.labels) ){
+            stop("'parse.labels' must be TRUE or FALSE.", call. = FALSE)
+        }
+        if( !.is_a_bool(add.significance) ){
+            stop("'add.significance' must be TRUE or FALSE.", call. = FALSE)
+        }
+        if( parse.labels && !add.significance ){
+            parse.labels <- FALSE
+            warning("'parse.labels' was turned off because 'add.significance' is FALSE.", call. = FALSE)
+        }
+        if( !.is_a_bool(add.expl.var) ){
+            stop("'add.expl.var' must be TRUE or FALSE.", call. = FALSE)
+        }
+        if( !.are_whole_numbers(ellipse.linetype) ){
+            stop("'ellipse.linetype' must be a whole number.", call. = FALSE)
+        }
+        if( !.are_whole_numbers(ellipse.linetype) ){
+            stop("'vec.linetype' must be a whole number.", call. = FALSE)
+        }
+        if( ellipse.alpha < 0 || ellipse.alpha > 1 ){
+            stop("'ellipse.alpha' must be a number between 0 and 1.", call. = FALSE)
+        }
+        if ( !is.numeric(ellipse.linewidth) && ellipse.linewidth > 0 ) {
+            stop("'ellipse.linewidth' must be a positive number.", call. = FALSE)
+        }
+        if ( !is.numeric(vec.size) && vec.size > 0 ) {
+            stop("'vec.size' must be a positive number.", call. = FALSE)
+        }
+        if ( !is.numeric(arrow.size) && arrow.size > 0 ) {
+            stop("'arrow.size' must be a positive number.", call. = FALSE)
+        }
+        if ( !is.numeric(label.size) && label.size > 0 ) {
+            stop("'label.size' must be a positive number.", call. = FALSE)
+        }
+        if ( !.is_non_empty_string(vec.color) ) {
+            stop("'vec.color' must be a non-empty string specifying a colour", call. = FALSE)
+        }
+        if ( !.is_non_empty_string(label.color) ) {
+            stop("'label.color' must be a non-empty string specifying a colour", call. = FALSE)
+        }
+        if ( !.is_a_string(sep.group) ) {
+            stop("'sep.group' must be a string specifying a separator.", call. = FALSE)
+        }
+        if ( !.is_a_string(repl.underscore) ) {
+            stop("'repl.underscore' must be a string.", call. = FALSE)
+        }
+        ###################### Input check end ####################
+        # Get data for plotting
+        plot_data <- .incorporate_rda_vis(
+            object, dimred, sep.group = sep.group, repl.underscore = repl.underscore,
+            add.significance = add.significance, add.expl.var = add.expl.var,
+            add.ellipse = add.ellipse, add.vectors = add.vectors, ...
+        )
+        # Create a plot
+        plot <- .rda_plotter(
+            plot_data, ellipse.alpha = ellipse.alpha, ellipse.linewidth = ellipse.linewidth,
+            ellipse.linetype = ellipse.linetype, vec.size = vec.size, vec.color = vec.color,
+            vec.colour = vec.colour, vec.linetype = vec.linetype, arrow.size = arrow.size,
+            label.color = label.color, label.colour = label.colour, label.size = label.size,
+            vec.text = vec.text, add.ellipse = add.ellipse, repel.labels = repel.labels,
+            parse.labels = parse.labels, ...
+        )
+        return(plot)
+    }
+)
+
+#' @rdname plotCCA
+#' @aliases plotCCA
+#' @export
+setMethod("plotRDA", signature = c(object = "matrix"),
+    function(object, ...){
+        # Construct TreeSE from rda/cca object
+        object <- .rda2tse(object)
+        # Run plotRDA method for TreeSE
+        return(plotRDA(object, "RDA", ...))
+    }
+)
+
+
+################## HELP FUNCTIONS ##########################
+
+# Construct TreeSE from rda/cca object to pass it to downstream functions
+.rda2tse <- function(object) {
+    # Convert rda/cca object to TreeSE
+    object <- TreeSummarizedExperiment(
+      assays = matrix(ncol = nrow(object), dimnames = list(NULL, rownames(object))),
+      reducedDims = list(RDA = object)
+    )
+    return(object)
+}
+
+# Get data for plotting
+#' @importFrom scater plotReducedDim retrieveCellInfo
+#' @importFrom SingleCellExperiment reducedDim reducedDimNames
+.incorporate_rda_vis <- function(
+        tse, dimred, ncomponents = 2, colour_by = color_by, color_by = NULL,
+        add.significance = TRUE, add.expl.var = TRUE, add.ellipse = TRUE,
+        add.vectors = TRUE, vec.lab = NULL, expl_var = NULL,
+        sep.group = "\U2012", repl.underscore = " ", ...){
+
+    # Check dimred
+    if( !dimred %in% reducedDimNames(tse) ){
+        stop("'dimred' must specify reducedDim.", call. = FALSE)
+    }
+    # Get reducedDim
+    reduced_dim <- reducedDim(tse, dimred)
+    # Check that there are at least 2 coordinates
+    if( ncol(reduced_dim) < 2 ){
+        stop("reducedDim specified by 'dimred' must have at least 2 columns.", call. = FALSE)
+    }
+    # Only 2 dimensions are supported currently
+    ncomponents <- 2
+    
+    # If specified, get explained variance
+    if( add.expl.var ){
+        # Check if data is available
+        ind <- names(attributes(reduced_dim)) %in% c("rda", "cca")
+        # If it can be found
+        if( any(ind) ){
+            # Add explained variance
+            rda <- attributes(reduced_dim)[ind][[1]]
+            expl_var <- summary(rda)$concont$importance[2, ]*100
+        } else{
+            # If it cannot be found, give warning
+            warning(paste("RDA/CCA object was not found. Please compute",
+                          "RDA/CCA by using runCCA or calculateCCA."),
+                    call. = FALSE)
+        }
+    }
+    
+    # Get scatter plot with plotReducedDim --> keep theme similar between ordination methods
+    plot <- plotReducedDim(
+        tse, dimred = dimred, ncomponents = ncomponents, colour_by = colour_by,
+        percentVar = expl_var, ...)
+    
+    # Get data for ellipse
+    ellipse_data <- NULL
+    if( add.ellipse != FALSE && !is.null(colour_by) ){
+        ellipse_data <- reduced_dim
+        ellipse_data <- as.data.frame(ellipse_data)
+        ellipse_data[[colour_by]] <- retrieveCellInfo(tse, colour_by)[["value"]]
+        attributes(ellipse_data)$colour_by <- colour_by
+    }
+    
+    # Get data for vectors
+    vector_data <- NULL
+    if( add.vectors ){
+        # Check if data is available
+        ind <- names(attributes(reduced_dim)) %in% c("rda", "cca")
+        # If it can be found
+        if( any(ind) ){
+            # Get biplot from cca object
+            rda <- attributes(reduced_dim)[ind][[1]]
+            vector_data <- rda$CCA$biplot
+            vector_data <- as.data.frame(vector_data)
+            vector_data[["group"]] <- rownames(vector_data)
+        } else{
+            # If it cannot be found, give warning
+            warning(paste("RDA/CCA object was not found. Please compute RDA/CCA",
+                          "by using runCCA or calculateCCA."),
+                    call. = FALSE)
+        }
+    }
+    
+    # Get variable names from sample metadata
+    coldata <- colData(tse)
+    variable_names <- colnames(coldata)
+    # Check if variable names can be found metadata
+    all_var_found <- FALSE
+    if( !is.null(vector_data) && length(variable_names) > 0 ){
+        all_var_found <- all(colSums(
+            sapply(rownames(vector_data), function(x)
+                sapply(variable_names, function(y) grepl(y, x)) )) == 1)
+    }
+    
+    # Get vector labels
+    if( !is.null(vector_data) ){
+        # Get those names that are present in data
+        
+        # If user has not provided vector labels
+        if( is.null(vec.lab) ){
+            vector_label <- rownames(vector_data)
+            # Make labels more tidy
+            if( all_var_found ){
+                vector_label <- .tidy_vector_labels(
+                    vector_label, coldata,
+                    sep.group = sep.group,
+                    repl.underscore = repl.underscore
+                )
+            }
+            # Add to df
+            vector_data$vector_label <- vector_label
+        } else{
+            # Check that user-provided labels are correct length
+            if( length(vec.lab) != nrow(vector_data) ){
+                stop("Number of labels in 'vec_lab' do not match with number of vectors.",
+                     call. = FALSE)
+            }
+            # If they are, add labels to data
+            vector_data$vector_label <- vec.lab
+        }
+    }
+    
+    # Get significance data
+    signif_data <- NULL
+    if( add.significance && !is.null(vector_data) && all_var_found ){
+        # Check if data is available
+        ind <- names(attributes(reduced_dim)) %in% c("significance")
+        # If it can be found
+        if( any(ind) ){
+            # Get biplot from cca object
+            signif_data <- attributes(reduced_dim)[ind][[1]]
+            signif_data <- signif_data[["permanova"]]
+            signif_data <- as.data.frame(signif_data)
+            # Add significance to vector labels
+            # Get vector labels
+            vector_label <- vector_data[["vector_label"]]
+            # Add significance to vector labels
+            vector_label <- .add_signif_to_vector_labels(
+                vector_label, variable_names, signif_data, repl.underscore)
+            vector_data[["vector_label"]] <- vector_label
+        } else{
+            # If it cannot be found, give warning
+            warning(paste("Significance data was not found. please compute",
+                          "CCA/RDA by using runCCA or calculateCCA."),
+                    call. = FALSE)
+        }
+    }
+    
+    # Create a list to return
+    result <- list(
+        plot = plot,
+        ellipse_data = ellipse_data,
+        vector_data = vector_data
+    )
+    return(result)
+}
+
+# Make vector labels more tidy, i.e, separate variable and group names.
+# Replace also underscores with space
+.tidy_vector_labels <- function(
+        vector_label, coldata, sep.group = "\U2012", repl.underscore = " ", ...){
+    # Get variable names from sample metadata
+    var_names <- colnames(coldata)
+    # Loop through vector labels
+    vector_label <- sapply(vector_label, FUN = function(name){
+        # Get the real variable name from sample metadata
+        var_name <- var_names[ sapply(var_names, function(x) grepl(x, name)) ]
+        # If the vector label includes also group name
+        if( !name %in% var_names ){
+            # Get the group name
+            group_name <- unique( coldata[[var_name]] )[ 
+                which( paste0(var_name, unique( coldata[[var_name]] )) == name ) ]
+            # Modify vector so that group is separated from variable name
+            new_name <- paste0(var_name, " ", sep.group, " ", group_name)
+        } else{
+            new_name <- name
+        }
+        # Replace underscores with space
+        new_name <- gsub("_", repl.underscore, new_name)
+        return(new_name)
+    })
+    return(vector_label)
+}
+
+# This function adds significance info to vector labels
+.add_signif_to_vector_labels <- function(
+        vector_label, var_names, signif_data, repl.underscore = " ", ...){
+    # Replace underscores from significance data and variable names to match labels
+    rownames(signif_data) <- sapply(rownames(signif_data), function(x) gsub("_", repl.underscore, x))
+    var_names <- sapply(var_names, function(x) gsub("_", repl.underscore, x))
+    # Loop through vector labels
+    vector_label <- sapply(vector_label, FUN = function(name){
+        # Get the real variable name from sample metadata
+        var_name <- var_names[ sapply(var_names, function(x) grepl(x, name)) ]
+        # Add percentage how much this variable explains, and p-value
+        new_name <- expr(
+            paste(!!name, " (", 
+                  !!format(
+                      round( signif_data[var_name, "Explained variance"]*100, 1),
+                      nsmall = 1), "%, ", italic("P"), " = ",
+                  !!gsub("0\\.","\\.", format(
+                      round( signif_data[var_name, "Pr(>F)"], 3),
+                      nsmall = 3)), ")"))
+        
+        return(new_name)
+    })
+    return(vector_label)
+}
+
+# Plot based on the data
+#' @importFrom ggrepel geom_text_repel geom_label_repel
+.rda_plotter <- function(
+        plot_data, ellipse.alpha = 0.2, ellipse.linewidth = 0.1, ellipse.linetype = 1,
+        vec.size = 0.5, vec.color = vec.colour, vec.colour = "black",
+        vec.linetype = 1, arrow.size = 0.25, min.segment.length = 5,
+        label.color = label.colour, label.colour = "black", label.size = 4,
+        parse.labels = TRUE, vec.text = TRUE, repel.labels = TRUE, add.ellipse = TRUE,
+        position = NULL, nudge_x = NULL, nudge_y = NULL, direction = "both",
+        max.overlaps = 10, check_overlap = FALSE, ...){
+
+    # Get the scatter plot
+    plot <- plot_data[["plot"]]
+    # Add ellipse
+    if( !is.null(plot_data$ellipse_data) ){
+        # Get data and variabe names
+        data <- plot_data$ellipse_data
+        xvar <- colnames(data)[[1]]
+        yvar <- colnames(data)[[2]]
+        colour_var <- attributes(plot_data$ellipse_data)$colour_by
+        # Add ellipses to plot (fill or colour the edge)
+        if( add.ellipse %in% c(TRUE, "fill") ){
+            plot <- plot +
+                stat_ellipse(data = data,
+                             aes(x = .data[[xvar]], y = .data[[yvar]],
+                                 color = .data[[colour_var]], fill = after_scale(color)),
+                             geom = "polygon", alpha = ellipse.alpha,
+                             size = ellipse.linewidth, linetype = ellipse.linetype)
+        } else if ( add.ellipse %in% c("color", "colour") ){
+            plot <- plot +
+                stat_ellipse(data = data,
+                             aes(x = .data[[xvar]], y = .data[[yvar]], color = .data[[colour_var]]),
+                             geom = "polygon", alpha = 0, linetype = ellipse.linetype)
+        }
+        
+    }
+    # Add vectors
+    if( !is.null(plot_data$vector_data) ){
+        # Get data and variabe names
+        data <- plot_data$vector_data
+        xvar <- colnames(data)[[1]]
+        yvar <- colnames(data)[[2]]
+        # Add vectors
+        plot <- plot +
+            geom_segment(data = data,
+                         aes(x = 0, y = 0, xend = .data[[xvar]], yend = .data[[yvar]],
+                             group = .data[["group"]]),
+                         arrow = arrow(length = unit(arrow.size, "cm")),
+                         color = vec.color, linetype = vec.linetype, size = vec.size)
+        # Add vector labels (text or label)
+        # Make list of arguments for geom_text/geom_label
+        label_args <- list(
+                        data = data,
+                        mapping = aes(x = .data[[xvar]], y = .data[[yvar]]),
+                        label = data[["vector_label"]], parse = parse.labels,
+                        color = label.color, size = label.size, stat = "identity",
+                        nudge_x = nudge_x, nudge_y = nudge_y, show.legend = NA,
+                        na.rm = FALSE, inherit.aes = TRUE
+                      )
+        # Repel text
+        if( repel.labels ){
+          # Add arguments for geom_text_repel/geom_label_repel to list
+          label_args <- c(
+            label_args, min.segment.length = min.segment.length,
+            box.padding = 0.25, point.padding = 1e-06, force = 1, force_pull = 1,
+            max.time = 0.5, max.iter = 10000, max.overlaps = max.overlaps,
+            direction = direction, seed = NA, verbose = FALSE
+          )
+          # repelled text
+          if( vec.text ){
+            plot <- plot + do.call("geom_text_repel", label_args)
+          # repelled labels
+          } else{
+            plot <- plot + do.call("geom_label_repel", label_args)
+          }
+        # Do not repel text
+        } else{
+          # not repelled text
+          if( vec.text ){
+            label_args <- c(label_args, check_overlap = check_overlap)
+            plot <- plot + do.call("geom_text", label_args)
+          # not repelled labels
+          } else{
+            plot <- plot + do.call("geom_label", label_args)
+          }
+        }
+        
+    }
+    return(plot)
+}
diff --git a/R/plotSeries.R b/R/plotSeries.R
index d61b23d8..2c8adcfd 100644
--- a/R/plotSeries.R
+++ b/R/plotSeries.R
@@ -67,7 +67,7 @@
 #'            colour_by = "Family")
 #' 
 #' # Counts relative abundances
-#' object <- transformCounts(object, method = "relabundance")
+#' object <- transformAssay(object, method = "relabundance")
 #' 
 #' # Selects taxa
 #' taxa <- c("seq_1", "seq_2", "seq_3", "seq_4", "seq_5")
diff --git a/man/plotAbundance.Rd b/man/plotAbundance.Rd
index 595d87b6..e24a0a1c 100644
--- a/man/plotAbundance.Rd
+++ b/man/plotAbundance.Rd
@@ -134,7 +134,7 @@ wrap_plots(plot, ncol = 1, heights = c(0.8,0.2))
 ## Compositional barplot with top 5 taxa and samples sorted by "Bacteroidetes"
 
 # Getting top taxa on a Phylum level
-se <- transformCounts(se, method="relabundance")
+se <- transformAssay(se, method="relabundance")
 se_phylum <- mergeFeaturesByRank(se, rank ="Phylum", onRankOnly=TRUE)
 top_taxa <- getTopTaxa(se_phylum,top = 5, assay.type = "relabundance")
 
diff --git a/man/plotAbundanceDensity.Rd b/man/plotAbundanceDensity.Rd
index 27acde66..8b8a7f3b 100644
--- a/man/plotAbundanceDensity.Rd
+++ b/man/plotAbundanceDensity.Rd
@@ -112,7 +112,7 @@ tse <- microbiomeDataSets::atlas1006()
 plotAbundanceDensity(tse, assay.type = "counts")
 
 # Counts relative abundances
-tse <- transformCounts(tse, method = "relabundance")
+tse <- transformAssay(tse, method = "relabundance")
 
 # Plots the relative abundance of 10 most abundant taxa. 
 # "nationality" information is used to color the points. X-axis is log-scaled.
diff --git a/man/plotCCA.Rd b/man/plotCCA.Rd
new file mode 100644
index 00000000..714ca498
--- /dev/null
+++ b/man/plotCCA.Rd
@@ -0,0 +1,188 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plotCCA.R
+\name{plotCCA}
+\alias{plotCCA}
+\alias{plotRDA}
+\alias{plotCCA,SingleCellExperiment-method}
+\alias{plotCCA,matrix-method}
+\alias{plotRDA,SingleCellExperiment-method}
+\alias{plotRDA,matrix-method}
+\title{Plot RDA or CCA object}
+\usage{
+plotCCA(object, ...)
+
+\S4method{plotCCA}{SingleCellExperiment}(object, dimred, ...)
+
+\S4method{plotCCA}{matrix}(object, ...)
+
+plotRDA(object, ...)
+
+\S4method{plotRDA}{SingleCellExperiment}(
+  object,
+  dimred,
+  add.ellipse = TRUE,
+  ellipse.alpha = 0.2,
+  ellipse.linewidth = 0.1,
+  ellipse.linetype = 1,
+  vec.size = 0.5,
+  vec.color = vec.colour,
+  vec.colour = "black",
+  vec.linetype = 1,
+  arrow.size = 0.25,
+  label.color = label.colour,
+  label.colour = "black",
+  label.size = 4,
+  vec.text = TRUE,
+  repel.labels = TRUE,
+  sep.group = "‒",
+  repl.underscore = " ",
+  add.significance = TRUE,
+  add.expl.var = TRUE,
+  add.vectors = TRUE,
+  parse.labels = TRUE,
+  ...
+)
+
+\S4method{plotRDA}{matrix}(object, ...)
+}
+\arguments{
+\item{object}{a
+\code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-constructor]{TreeSummarizedExperiment}}
+or a matrix of weights. The latter is returned as output from \code{\link[mia:runCCA]{calculateRDA}}.}
+
+\item{...}{additional parameters for plotting, inherited from
+\code{\link[scater:plotReducedDim]{plotReducedDim}},
+\code{\link[ggplot2:geom_label]{geom_label}} and
+\code{\link[ggrepel:geom_label_repel]{geom_label_repel}}.}
+
+\item{dimred}{A string or integer scalar indicating the reduced dimension to
+plot. This is the output of \code{\link[mia:runCCA]{runRDA}} and resides in
+\code{reducedDim(tse, dimred)}.}
+
+\item{add.ellipse}{One of \code{c(TRUE, FALSE, "fill", "colour", "color")},
+indicating whether ellipses should be present, absent, filled or colored.
+(default: \code{ellipse.fill = TRUE})}
+
+\item{ellipse.alpha}{Number between 0 and 1 to adjust the opacity of ellipses.
+(default: \code{ellipse.alpha = 0.2})}
+
+\item{ellipse.linewidth}{Number specifying the size of ellipses.
+(default: \code{ellipse.linewidth = 0.1})}
+
+\item{ellipse.linetype}{Discrete number specifying the style of ellipses.
+(default: \code{ellipse.linetype = 1})}
+
+\item{vec.size}{Number specifying the size of vectors.
+(default: \code{vec.size = 0.5})}
+
+\item{vec.color}{Alias for \code{vec.colour}.}
+
+\item{vec.colour}{String specifying the colour of vectors.
+(default: \code{vec.color = "black"})}
+
+\item{vec.linetype}{Discrete number specifying the style of vector lines.
+(default: \code{vec.linetype = 1})}
+
+\item{arrow.size}{Number specifying the size of arrows.
+(defaults: \code{arrow.size = 0.25})}
+
+\item{label.color}{Alias for \code{label.colour}.}
+
+\item{label.colour}{String specifying the colour of text and labels.
+(default: \code{label.color = "black"})}
+
+\item{label.size}{Number specifying the size of text and labels.
+(default: \code{label.size = 4})}
+
+\item{vec.text}{TRUE or FALSE, should text instead of labels be used to label vectors.
+(default: \code{vec.text = TRUE})}
+
+\item{repel.labels}{TRUE or FALSE, should labels be repelled.
+(default: \code{repel.labels = TRUE})}
+
+\item{sep.group}{String specifying the separator used in the labels.
+(default: \code{sep.group = "\U2012"})}
+
+\item{repl.underscore}{String used to replace underscores in the labels.
+(default: \code{repl.underscore = " "})}
+
+\item{add.significance}{TRUE or FALSE, should explained variance and p-value
+appear in the labels. (default: \code{add.significance = TRUE})}
+
+\item{add.expl.var}{TRUE or FALSE, should explained variance appear on the
+coordinate axes. (default: \code{add.expl.var = TRUE})}
+
+\item{add.vectors}{TRUE or FALSE, should vectors appear in the plot.
+(default: \code{add.vectors = TRUE})}
+
+\item{parse.labels}{TRUE or FALSE, should labels be parsed.
+(default: \code{parse.labels = TRUE})}
+}
+\value{
+A \code{ggplot2} object
+}
+\description{
+\code{plotRDA} and \code{plotCCA} create an RDA/CCA plot starting from the
+output of \code{\link[mia:runCCA]{CCA and RDA}} functions, two common methods
+for supervised ordination of microbiome data.
+}
+\details{
+\code{plotRDA} and \code{plotCCA} create an RDA/CCA plot starting from the
+output of \code{\link[mia:runCCA]{CCA and RDA}} functions, two common methods
+for supervised ordination of microbiome data. Either a
+\code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-constructor]{TreeSummarizedExperiment}}
+or a matrix object is supported as input. When the input is a
+TreeSummarizedExperiment, this should contain the output of runRDA
+in the reducedDim slot and the argument \code{dimred} needs to be defined.
+When the input is a matrix, this should be returned as output from
+calculateRDA. However, the first method is recommended because it provides
+the option to adjust aesthetics to the colData variables through the
+arguments inherited from \code{\link[scater:plotReducedDim]{plotReducedDim}}.
+}
+\examples{
+# Load dataset
+library(miaViz)
+data("enterotype", package = "mia")
+tse <- enterotype
+ 
+# Run RDA and store results into TreeSE
+tse <- runRDA(tse,
+              formula = assay ~ ClinicalStatus + Gender + Age,
+              FUN = vegan::vegdist,
+              distance = "bray",
+              na.action = na.exclude)
+               
+# Create RDA plot coloured by variable
+plotRDA(tse, "RDA",
+        colour_by = "ClinicalStatus")
+ 
+# Create RDA plot with empty ellipses
+plotRDA(tse, "RDA",
+        colour_by = "ClinicalStatus",
+        add.ellipse = "colour")
+ 
+# Create RDA plot with text encased in labels
+plotRDA(tse, "RDA",
+        colour_by = "ClinicalStatus",
+        vec.text = FALSE)
+ 
+# Create RDA plot without repelling text
+plotRDA(tse, "RDA",
+        colour_by = "ClinicalStatus",
+        repel.labels = FALSE)
+ 
+# Create RDA plot without vectors
+plotRDA(tse, "RDA",
+        colour_by = "ClinicalStatus",
+        add.vectors = FALSE)
+ 
+# Calculate RDA as a separate object
+rda_mat <- calculateRDA(tse,
+                        formula = assay ~ ClinicalStatus + Gender + Age,
+                        FUN = vegan::vegdist,
+                        distance = "bray",
+                        na.action = na.exclude)
+ 
+# Create RDA plot from RDA matrix
+plotRDA(rda_mat)
+}
diff --git a/man/plotSeries.Rd b/man/plotSeries.Rd
index 75405a2e..e45fa6aa 100644
--- a/man/plotSeries.Rd
+++ b/man/plotSeries.Rd
@@ -96,7 +96,7 @@ plotSeries(object,
            colour_by = "Family")
 
 # Counts relative abundances
-object <- transformCounts(object, method = "relabundance")
+object <- transformAssay(object, method = "relabundance")
 
 # Selects taxa
 taxa <- c("seq_1", "seq_2", "seq_3", "seq_4", "seq_5")
diff --git a/tests/testthat/test-2plotSeries.R b/tests/testthat/test-2plotSeries.R
index 817e3a03..b509ae04 100644
--- a/tests/testthat/test-2plotSeries.R
+++ b/tests/testthat/test-2plotSeries.R
@@ -4,7 +4,7 @@ test_that("plot series", {
     # Load data from miaTime package
     if( !require("miaTime") ){
         if( !require("devtools") ){
-            install.packages("devtools")
+            BiocManager::install("devtools")
         }
         devtools::install_github("microbiome/miaTime")
         library("miaTime")
diff --git a/tests/testthat/test-plotCCA.R b/tests/testthat/test-plotCCA.R
new file mode 100644
index 00000000..2ac58037
--- /dev/null
+++ b/tests/testthat/test-plotCCA.R
@@ -0,0 +1,80 @@
+test_that("plot RDA/CCA", {
+  data("Tengeler2020", package = "mia")
+  tse <- Tengeler2020
+  
+  ### 1). TEST error messages ###
+  
+  # Object without reducedDim
+  expect_error(plotRDA(tse), 'argument "dimred" is missing, with no default')
+  expect_error(plotRDA(tse, "RDA"), "'dimred' must specify reducedDim.")
+  
+  # Run/calculate RDA
+  tse <- runRDA(tse, assay ~ patient_status + cohort)
+  rda <- calculateRDA(tse, assay ~ patient_status + cohort)
+  
+  # Minimal functionality
+  expect_no_error(plotRDA(tse, "RDA"))
+
+  # Wrong-entry scenarios
+  expect_error(plotRDA(tse, "RDA", colour_by = "wrong colname"))
+  expect_error(plotRDA(tse, "RDA", colour_by = "cohort", shape_by = "wrong colname"))
+  expect_error(plotRDA(tse, "RDA", add.ellipse = "invalid value"),
+               "'add.ellipse' must be one of c(TRUE, FALSE, 'fill', 'color', 'colour').",
+               fixed = TRUE)
+  expect_error(plotRDA(tse, "RDA", add.significance = "invalid value"),
+               "'add.significance' must be TRUE or FALSE.")
+  expect_error(plotRDA(tse, "RDA", add.expl.var = "invalid value"),
+               "'add.expl.var' must be TRUE or FALSE.")
+  expect_error(plotRDA(tse, "RDA", repel.labels))
+  expect_warning(plotRDA(tse, "RDA", add.significance = FALSE, parse.labels = TRUE),
+                 "'parse.labels' was turned off because 'add.significance' is FALSE.")
+  expect_warning(plotRDA(tse, "RDA", add.vectors = FALSE),
+                 "'add.vectors' is FALSE, so other arguments for vectors and labels will be disregarded.")
+  ## add more tests here
+
+  ### 2). TEST plot layers ###
+  
+  el_true <- plotRDA(tse, "RDA", colour_by = "patient_status")
+  el_false <- plotRDA(tse, "RDA", colour_by = "patient_status", add.ellipse = FALSE)
+  el_col <- plotRDA(tse, "RDA", colour_by = "patient_status", add.ellipse = "colour")
+  el_fill <- plotRDA(tse, "RDA", colour_by = "patient_status", add.ellipse = "fill")
+  expect_warning(
+      vec_false <- plotRDA(tse, "RDA", colour_by = "patient_status", add.vectors = FALSE)
+  )
+  # Filled ellipse has one more layer than no ellipse plot
+  expect_equal(length(ggplot_build(el_true)[["data"]]), 4)
+  expect_equal(length(ggplot_build(el_false)[["data"]]), 3)
+  # No-vector plot has only 2 layers, (points and ellipse)
+  expect_equal(length(ggplot_build(vec_false)[["data"]]), 2)
+  # Coloured ellipse but not filled ellipse plot has all 0 alpha values
+  expect_true(all(ggplot_build(el_col)[["data"]][[2]][["alpha"]] == 0))
+  expect_false(all(ggplot_build(el_fill)[["data"]][[2]][["alpha"]] == 0))
+  
+  # Check ggplot aesthetics
+  p_aes <- plotRDA(tse, "RDA", colour_by = "patient_status", ellipse.alpha = 0.5,
+                   ellipse.linewidth = 0.2, ellipse.linetype = 3, vec.size = 0.6,
+                   vec.colour = "red", vec.linetype = 2, arrow.size = 0.15,
+                   label.colour = "blue", label.size = 5)
+  # Build plot and get data
+  p_aes_build <- ggplot_build(p_aes)[["data"]]
+  # Ellipse aesthetics are correctly defined in ggplot
+  expect_true(all(p_aes_build[[2]][["alpha"]] == 0.5))
+  expect_true(all(ggplot_build(p_aes)[[2]][["linewidth"]] == 0.2))
+  expect_true(all(ggplot_build(p_aes)[[2]][["linetype"]] == 3))
+  # Vector aesthetics are correctly defined in ggplot
+  expect_true(all(p_aes_build[[3]][["colour"]] == "red"))
+  expect_true(all(p_aes_build[[3]][["linewidth"]] == 0.6))
+  expect_true(all(p_aes_build[[3]][["linetype"]] == 2))
+  # Label aesthetics are correctly defined in ggplot
+  expect_true(all(p_aes_build[[4]][["colour"]] == "blue"))
+  expect_true(all(p_aes_build[[4]][["size"]] == 5))
+  # Where is arrow size stored?
+  # expect_true(arrow_size == 0.15)
+  
+  # Vector or label text
+  p_vec <- plotRDA(tse, "RDA", colour_by = "patient_status", vec.text = TRUE)
+  p_lab <- plotRDA(tse, "RDA", colour_by = "patient_status", vec.text = FALSE)
+  # Column "fill" is present in p_vec and missing in p_lab, so length differs by 1
+  expect_length(ggplot_build(p_vec)[["data"]][[4]], 29)
+  expect_length(ggplot_build(p_lab)[["data"]][[4]], 28)
+})
diff --git a/vignettes/miaViz.Rmd b/vignettes/miaViz.Rmd
index f8951e39..e56f29f7 100644
--- a/vignettes/miaViz.Rmd
+++ b/vignettes/miaViz.Rmd
@@ -63,7 +63,7 @@ However, if the `rank` is set not `NULL` a bar plot is returned. At the same
 time the `features` argument can be set to `NULL` (default).
 
 ```{r}
-GlobalPatterns <- transformCounts(GlobalPatterns, method = "relabundance")
+GlobalPatterns <- transformAssay(GlobalPatterns, method = "relabundance")
 ```
 
 ```{r}
@@ -256,12 +256,21 @@ plotColGraph(altExp(GlobalPatterns,"Genus"),
 
 # Plotting of serial data
 
-```{r}
+```{r, eval=FALSE}
+if(!requireNamespace("devtools", quietly = TRUE)){
+    BiocManager::install("devtools")
+}
+if(!requireNamespace("miaTime", quietly = TRUE)){
+    devtools::install_github("microbiome/miaTime", upgrade = "never")
+}
+```
+
+```{r, eval=FALSE}
 # Load data from miaTime package
 library("miaTime")
 data(SilvermanAGutData, package="miaTime")
 tse <- SilvermanAGutData
-tse <- transformCounts(tse, method = "relabundance")
+tse <- transformAssay(tse, method = "relabundance")
 taxa <- getTopTaxa(tse, 2)
 ```
 
@@ -271,7 +280,7 @@ descriptor for ordering the data. The `y` argument selects the feature to show.
 Since plotting a lot of features is not advised a maximum of 20 features can 
 plotted at the same time.
  
-```{r}
+```{r, eval=FALSE}
 plotSeries(tse,
            x = "DAY_ORDER",
            y = taxa,
@@ -281,7 +290,7 @@ If replicated data is present, data is automatically used for calculation of the
 `mean` and `sd` and plotted as a range. Data from different assays can be used
 for plotting via the `assay.type`.
 
-```{r}
+```{r, eval=FALSE}
 plotSeries(tse[taxa,],
            x = "DAY_ORDER",
            colour_by = "Family",
@@ -291,7 +300,7 @@ plotSeries(tse[taxa,],
 
 Additional variables can be used to modify line type aesthetics.
 
-```{r}
+```{r, eval=FALSE}
 plotSeries(tse,
            x = "DAY_ORDER",
            y = getTopTaxa(tse, 5),
@@ -329,23 +338,14 @@ plotDMNFit(dmn_se, type = "laplace")
 
 # Serial data ordination and trajectories
 
-```{r, error=FALSE, warning=FALSE, results='hide'}
-if(!requireNamespace("devtools", quietly = TRUE)){
-    BiocManager::install("devtools")
-}
-if(!requireNamespace("miaTime", quietly = TRUE)){
-    devtools::install_github("microbiome/miaTime")
-}
-```
-
 Principal Coordinates Analysis using Bray-Curtis dissimilarity on the
 `hitchip1006` dataset:
 
-```{r MDS, include=FALSE}
+```{r MDS, eval=FALSE}
 library(miaTime)
 data(hitchip1006, package = "miaTime")
 tse <- hitchip1006
-tse <- transformCounts(tse, method = "relabundance")
+tse <- transformAssay(tse, method = "relabundance")
 ## Ordination with PCoA with Bray-Curtis dissimilarity
 tse <- runMDS(tse, FUN = vegan::vegdist, method = "bray", name = "PCoA_BC",
               assay.type = "relabundance", na.rm = TRUE)
@@ -356,7 +356,7 @@ p
 
 Retrieving information about all available trajectories:
 
-```{r}
+```{r, eval=FALSE}
 library(dplyr)
 
 # List subjects with two time points
@@ -369,7 +369,7 @@ table(table(tse$subject)) %>% as.data.frame() %>%
 
 Lets look at all trajectories having two time points in the data:
 
-```{r}
+```{r, eval=FALSE}
 # plot
 p + geom_path(aes(x=X1, y=X2, group=subject), 
               arrow=arrow(length = unit(0.1, "inches")),
@@ -383,7 +383,7 @@ p + geom_path(aes(x=X1, y=X2, group=subject),
 
 Filtering the two time point trajectories by divergence and displaying top 10%:
 
-```{r}
+```{r, eval=FALSE}
 library(miaTime)
 # calculating step wise divergence based on the microbial profiles
 tse <- getStepwiseDivergence(tse, group = "subject", time_field = "time")
@@ -411,7 +411,7 @@ p + geom_path(aes(x=X1, y=X2,
 
 Plotting an example of the trajectory with the maximum total divergence:
 
-```{r}
+```{r, eval=FALSE}
 # Get subject with the maximum total divergence
 selected.subject <- data.frame(reducedDim(tse), colData(tse)) %>%
     group_by(subject) %>%