diff --git a/CONTRIBUTORS.yaml b/CONTRIBUTORS.yaml index 056c097c4d8fbb..a367ee1beb1b06 100644 --- a/CONTRIBUTORS.yaml +++ b/CONTRIBUTORS.yaml @@ -1377,6 +1377,11 @@ LeilyR: name: Leily Rabbani joined: 2018-09 + +lenaarenot: + name: Lena Arent + joined: 2024-05 + lilianarutaihwa: name: Liliana Rutaihwa joined: 2024-06 @@ -1385,6 +1390,7 @@ linzyelton: name: Linzy Elton joined: 2024-06 + lgallegovillar: name: Lorena Gallego Villar joined: 2021-10 diff --git a/topics/microbiome/tutorials/visualisation-ampvis/data-library.yaml b/topics/microbiome/tutorials/visualisation-ampvis/data-library.yaml new file mode 100644 index 00000000000000..eba74fe0ce38ff --- /dev/null +++ b/topics/microbiome/tutorials/visualisation-ampvis/data-library.yaml @@ -0,0 +1,39 @@ +--- +destination: + type: library + name: GTN - Material + description: Galaxy Training Network Material + synopsis: Galaxy Training Network Material. See https://training.galaxyproject.org +items: +- name: Metagenomics + description: Training material for visualisation methods on amplicon data with ampvis2 + items: + - name: Divers and Adaptable Visualisations of Metabarcoding Data Using ampvis2 + items: + - name: 'DOI: 10.5281/zenodo.12591715' + description: latest + items: + - url: MiDAS_otushort_table.tsv + src: url + ext: auto + info: https://zenodo.org/records/12591715 + - url: MiDAS_metadata.tsv + src: url + ext: auto + info: https://zenodo.org/records/12591715 + - url: MiDAS_taxtable.tsv + src: url + ext: auto + info: https://zenodo.org/records/12591715 + - name: 'DOI: 10.5281/zenodo.10362755' + items: + - url: BIOMARCS_ASV_tables.xlsx + src: url + ext: auto + info: https://zenodo.org/records/10362755 + - name: 'DOI: 10.5281/zenodo.7020318' + items: + - url: closed_otu_table_mc2_w_tax_0.00005_rarefied12000_filtered.biom + src: url + ext: auto + info: https://zenodo.org/records/7020318 \ No newline at end of file diff --git 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@@ -0,0 +1,72 @@ + +# This is the bibliography file for your tutorial. +# +# To add bibliography (bibtex) entries here, follow these steps: +# 1) Find the DOI for the article you want to cite +# 2) Go to https://doi2bib.org and fill in the DOI +# 3) Copy the resulting bibtex entry into this file +# +# To cite the example below, in your tutorial.md file +# use {% cite Batut2018 %} +# +# If you want to cite an online resourse (website etc) +# you can use the 'online' format (see below) +# +# You can remove the examples below + +@article{Andersen2018, + title = {ampvis2: an R package to analyse and visualise 16S rRNA amplicon data}, + url = {http://dx.doi.org/10.1101/299537}, + DOI = {10.1101/299537}, + publisher = {Cold Spring Harbor Laboratory}, + author = {Andersen, Kasper S. and Kirkegaard, Rasmus H. and Karst, Søren M. and Albertsen, Mads}, + year = {2018}, + month = apr +} + +@online{ampvis-intro, + author = {Kasper Skytte Andersen}, + title = {Introduction to ampvis2}, + url = {https://kasperskytte.github.io/ampvis2/articles/ampvis2.html#heatmap}, + urldate = {2024-06-11} +} + +@article{DeGooijer2006, + title = {25 years of time series forecasting}, + volume = {22}, + ISSN = {0169-2070}, + url = {http://dx.doi.org/10.1016/j.ijforecast.2006.01.001}, + DOI = {10.1016/j.ijforecast.2006.01.001}, + number = {3}, + journal = {International Journal of Forecasting}, + publisher = {Elsevier BV}, + author = {De Gooijer, Jan G. and Hyndman, Rob J.}, + year = {2006}, + month = jan, + pages = {443–473} +} + +@article{Gotelli2001, + title = {Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness}, + volume = {4}, + ISSN = {1461-0248}, + url = {http://dx.doi.org/10.1046/j.1461-0248.2001.00230.x}, + DOI = {10.1046/j.1461-0248.2001.00230.x}, + number = {4}, + journal = {Ecology Letters}, + publisher = {Wiley}, + author = {Gotelli, Nicholas J. and Colwell, Robert K.}, + year = {2001}, + month = jul, + pages = {379–391} +} + +@article{Dueholm2019, + title = {Generation of comprehensive ecosystems-specific reference databases with species-level resolution by high-throughput full-length 16S rRNA gene sequencing and automated taxonomy assignment (AutoTax)}, + url = {http://dx.doi.org/10.1101/672873}, + DOI = {10.1101/672873}, + publisher = {Cold Spring Harbor Laboratory}, + author = {Dueholm, Morten Simonsen and Andersen, Kasper Skytte and McIlroy, Simon Jon and Kristensen, Jannie Munk and Yashiro, Erika and Karst, Søren Michael and Albertsen, Mads and Nielsen, Per Halkjær}, + year = {2019}, + month = jun +} \ No newline at end of file diff --git a/topics/microbiome/tutorials/visualisation-ampvis/tutorial.md b/topics/microbiome/tutorials/visualisation-ampvis/tutorial.md new file mode 100644 index 00000000000000..e1e8d0ebd39a61 --- /dev/null +++ b/topics/microbiome/tutorials/visualisation-ampvis/tutorial.md @@ -0,0 +1,987 @@ +--- +layout: tutorial_hands_on + + +title: Divers and Adaptable Visualisations of Metabarcoding Data Using ampvis2 +level: Intermediate +zenodo_link: https://zenodo.org/records/12591715 +zenodo_link2: "https://zenodo.org/records/10362755" +questions: +- How can the plots be adapted to suit the research data? +- How can the data be filtered to show only significant information? +- How can multiple visualisation methods be compared? +- How can numerical and categorical metadata be used for amplicon visualisation? +objectives: +- Use heatmap workflow to analyse and visualise amplicon data +- Use ungrouped or grouped data or grouped data with facets +- Use ordination plot, or boxplot, or rarefaction curve, or timeseries +time_estimation: 2H +key_points: +- Using various visualisation methods can present data from different perspectives +- With sufficient metadata, the data can be visualised in relation to different groups and facets +contributors: +- lenaarenot +- paulzierep + +--- + + + + +Microbiome analysis using amplicon sequencing is central to many ecological studies {% cite Andersen2018 %}. +This method is crucial for identifying microorganisms within an ecosystem or engineered system, as understanding which +microorganisms are present is key to comprehending the communities and their functions. After sequencing, the amplicons +can be processed as exact amplicon sequence variants (ASVs) or clustered into operational taxonomic units (OTUs) based on +sequence identity {% cite Dueholm2019 %}. + +Visualising amplicon data is essential for interpreting the complex relationships within microbial communities. It allows +researchers to explore patterns of diversity, abundance, and ecological interactions. Various tools can be used for this purpose, +such as FISH-based visualisation (fluorescence in situ hybridisation), Usearch for mapping sequences, or R for analysis and +visualisation with packages like ggplot2. Among these tools, ampvis2 stands out for its ability to handle large datasets and +provide a range of visualisation options tailored to microbial ecology, making it a wide-ranging choice for many researchers {% cite Dueholm2019 %}. + +If amplicon data and an OTU table have already been generated, the data are ready for visualisation. This tutorial can be followed +either by using your own data or by downloading the dataset used here to proceed step-by-step. + +These OTU tables can be generated using various tools on Galaxy: +> Generate OTU or ASV table with one of this tools +> +> 1. Use one of this mothur tools: {% tool [Cluster](toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fmothur_cluster%2Fmothur_cluster%2F1.39.5.0) %} or {% tool [Hcluster](toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fmothur_hcluster%2Fmothur_hcluster%2F1.36.1.0&version=latest) %} +> +> 2. {% tool [LotuS2](toolshed.g2.bx.psu.edu/repos/earlhaminst/lotus2/lotus2/2.32+galaxy0) %} +> +> 3. {% tool [qiime2 fragment-insertion classify-otus-experimental](toolshed.g2.bx.psu.edu%2Frepos%2Fq2d2%2Fqiime2__fragment_insertion__classify_otus_experimental%2Fqiime2__fragment_insertion__classify_otus_experimental%2F2024.5.0%2Bq2galaxy.2024.5.0&version=latest) %} +> +> > +> > Alternatively, you can generate an ASV table, which functions similarly to an OTU table and is also accepted in ampvis_load. +> > +> > ASVs, with their higher phylogenetic resolution, are often preferred over OTUs because they provide sub-genus and sub-species + classification. However, without taxonomic assignment, ASVs are difficult to compare with other studies. Additionally, + linking microbial identity to functions using ASVs may not yield sufficient results. {% cite Dueholm2019 %}. +> {: .comment} +> +> 4. {% tool [dada2: makeSequenceTable](toolshed.g2.bx.psu.edu%2Frepos%2Fiuc%2Fdada2_makesequencetable%2Fdada2_makeSequenceTable%2F1.30.0%2Bgalaxy0&version=latest) %} +> +> > +> > The Galaxy Training Network provides nice tutorials on this topic, such as [Building an amplicon sequence variant (ASV) table from 16S data using DADA2](https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/dada-16S/tutorial.html) +> {: .comment} +> +> 5. Additionally, you can use abundance tables generated by tools like [Kraken2](https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/dada-16S/tutorial.html) + and [MetaPhlAn](https://training.galaxyproject.org/training-material/topics/microbiome/tutorials/taxonomic-profiling/tutorial.html) + for visualisation in ampvis2, instead of OTU tables. These abundance tables provide an alternative approach for microbial analysis. +> +{: .tip} + +# Plotting options with ampvis2 +First of all you can put your data into +- {% tool [rarefaction curve](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_rarecurve/ampvis2_rarecurve/2.8.9+galaxy0) %} + +to explore species richness. Then you can input your data into {% tool [subsets](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_subset_samples/ampvis2_subset_samples/2.8.9+galaxy0) %} and finally create: +- {% tool [heatmap](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_heatmap/ampvis2_heatmap/2.8.9+galaxy0) %} +- {% tool [boxplot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_boxplot/ampvis2_boxplot/2.8.9+galaxy0) %} +- {% tool [ordination plot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_ordinate/ampvis2_ordinate/2.8.9+galaxy0) %} +- {% tool [timeseries plot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_timeseries/ampvis2_timeseries/2.8.9+galaxy0) %} + +Most of these visualisation methods are described in +[Introduction to ampvis2](https://kasperskytte.github.io/ampvis2/articles/ampvis2.html#heatmap). +![overview of visualisation methods](./images/overview.png +"Overview of posible visualisation methods (taken from: Introduction to ampvis2 by Kasper Skytte Andersen)") + +Your data need to be in an acceptable format for the ampvis_load tool. The tool +requires an OTU table and accepts the following formats: +- _phyloseq_ +- _biom_ +- _dada2_sequencetable_ +- _tabular_ + +The OTU table is the only mandatory input for ampvis_load, but you can also input: +- _sample_metadata_ (in _tabular_ or _tsv_ formats) +- _taxonomy_table_ (in _tabular_ format) +- _fasta_file_ (in _fasta_ format) +- _phylogenetic_tree_ (in _newick_ format) +- as well as various combinations thereof. + +> +> - If you work without taxonomy table, ampvis wouldn't be able to visualise taxonomy hierarchy and other options might be missing +{: .comment} + +## Upload .biom file; create a phyloseq file + +* Both biom and phyloseq files are mentioned here for completeness + +* Either biom or phyloseq files can be used as input for all of the visualisation methods presented in this tutorial (though not demonstrated in this tutorial) + +* Use your own biom dataset or select one online, and upload it to Galaxy (as described in **Use Case 1**) + +> How the Upload should look like +> +> Make sure to select "biom2" instead of "Auto-detect". +> +> ![Select biom](./images/upload_biom.png "Make sure to select "biom2" like shown on the picture") +> +{: .details} + +* You can create a phylosec object using these tools: + +> Create phyloseq +> +> 1. {% tool [Create phyloseq object](toolshed.g2.bx.psu.edu/repos/iuc/phyloseq_from_biom/phyloseq_from_biom/1.46.0+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"BIOM file"*: `output` (Input dataset) +> +> 2. {% tool [ampvis2 load](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_load/ampvis2_load/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"OTU table"*: `output` (output of **Create phyloseq object** {% icon tool %}) +> +{: .hands_on} + + +In this tutorial all visualisations are demonstrated by using a combination of 3 inputs: +OTU table, sample metadata and taxonomy table, all in _tabular_ format. + + +> +> +> In this tutorial, the following topics will be covered: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +This tutorial has 2 versions: +- A short version, running prebuilt workflows +- A long version, going step-by-step + +{% include _includes/cyoa-choices.html option1="Short Version" option2="Long Version" default="Short-Version" %} + +# Use Case 1: Load your own data and create a Rarefaction Curve + +As first exploration of your data, you can start with rarefaction curve. +Rarefaction curves are a refined version of accumulation curves. They help visualise +the number of species (species richness) in your samples by showing the average number +of species observed as more samples are added. This method pools all the samples +together and calculates the mean species richness for different sample sizes, resulting +in a smooth curve. Rarefaction curves are particularly useful for comparing different datasets, +as they provide a standard way to assess species richness regardless of sample size differences {% cite Gotelli2001 %}. +> +> - For this part 'raw' data is required; it should not be normalised +> - A different dataset was used for this section compared to the rest of the tutorial +{: .comment} + +## Get Data +If you don't use your own dataset, you can also go to **Zenodo** and find a dataset to download. +We looked for a dataset marked "open" and used the following: + +> Data Upload +> +> 1. Create a new history for this tutorial +> 2. Import the files from [Zenodo]({{ page.zenodo_link2 }}) or a data library: +> ```text +> {{ page.zenodo_link2 }}/files/BIOMARCS_ASV_tables.xlsx +> ``` +> +> {% snippet faqs/galaxy/datasets_import_via_link.md %} +> +> {% snippet faqs/galaxy/datasets_import_from_data_library.md %} +> +> 3. Rename the datasets +> 4. Check that the datatype +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="datatypes" %} +> +> 5. Add to each database a tag corresponding to ... +> +> {% snippet faqs/galaxy/datasets_add_tag.md %} +> +{: .hands_on} + +There are 2 datasets available: **"V4-18S"** and **"COI"**, with **"COI"** being used here. + +### Sub-step: Generate **Uploadable Datasets from Downloaded Excel Sheet** +All data (OTU, metadata and tax table) that needs to be uploaded to Galaxy separately is combined in one excel sheet and looks like this... +![data to separate](./images/all_data.png +"OTU, metadata and tax table is combined in one sheet and needs to be separated") + +> Generate separated datasets for Galaxy upload +> +> 1. For OTU table: +> - Keep the sample names in the first row +> - Keep the asv+number in the first column +> - The first cell (A1) needs to reed ASV or OTU +> +> > Attention! +> > +> > Make sure sample names have no blank spaces +> {: .comment} +> +> > How it will look like +> > +> > After separeting OTUs from the main data sheet it will look like this... +> > +> >![separated OTU table](./images/otu.png "Separated OTU table") +> {: .details} +> +> 2. For metadata table: +> - Copy the metadata (marked in blue) and the sample names to a new sheet +> +> > How it will look like +> > +> > The copied set of metadata will look like this... +> > +> >![copied metadata](./images/meta_row.png "Copied set of metadata") +> {: .details} +> +> - Transpose the dataset and copy to a new sheet +> - Remove blank spaces from sample names +> +> > How it will look like +> > +> > The transposed set of metadata will look like this... +> > +> >![transposed metadata](./images/meta.png "Transposed metadata") +> {: .details} +> +> 3. For tax table: +> - Keep the asv+number in the first column +> - Keep the last column "lineage" +> - Split the "lineage"-column by the delimeter __semicolon__ +> - Give all columns a name +> +> > How it will look like +> > +> > After separeting the taxa into diferent columns and renaming, it will look like this... +> > +> >![separated and renamed tax table](./images/taxa.png "Separated and renamed tax table") +> {: .details} +> +> 4. Save all 3 data sheets separately +> +> 5. Convert to tsv format +> - Use any free available tool online to convert xlsx to tsv +> +{: .hands_on} + + +> +> +> 1. Why do you need to ensure that sample names have no blank spaces in the OTU table? +> 2. Why don't you use the metadata in its original untransposed form? +> +> > +> > +> > 1. Many bioinformatics tools assume that sample names are continuous strings, and spaces can be +interpreted as delimiters or end-of-string characters, leading to incorrect data parsing or analysis failures. +> > 2. You need the sample names as a column. Bioinformatics tools expect the metadata to be organised such that +each metadata attribute has its own column. +> > +> {: .solution} +> +{: .question} + +## Rarefaction Curve +You can generate the rarefaction curve directly using the ampvis_load tool, or you can do so with subsets. +Subsets allow you to filter the data, focusing on specific parts that are relevant to a particular research question. + +Follow this workflow to create a rarefaction curve directly from ampvis_load. +
+> Create a rarefaction curve +> +> 1. **Import the workflow** into Galaxy +> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_rarefaction_v1.0.ga) or download it to your computer +> - Import the workflow into Galaxy +> +> {% snippet faqs/galaxy/workflows_import.md %} +> +> 2. Run **Workflow: (without subsempling)** {% icon workflow %} using the following parameters +> - *"Step size"*: `5` +> - *"Color curves by"*: `sample_id` +> - *"Scales of the facets"*: `Free scale` +> +> {% snippet faqs/galaxy/workflows_run.md %} +> +{: .hands_on} +
+ +
+> Rarefaction curve workflow steps +> +> 1. {% tool [ampvis2 load](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_load/ampvis2_load/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"OTU table"*: `output` (Input dataset) +> - {% icon param-file %} *"Sample metadata"*: `output` (Input dataset) +> - {% icon param-file %} *"Taxonomy table"*: `output` (Input dataset) +> +> 2. {% tool [ampvis2 rarefaction curve](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_rarecurve/ampvis2_rarecurve/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `ampvis` (output of **ampvis2 load** {% icon tool %}) +> - {% icon param-file %} *"Metadata list"*: `metadata_list_out` (output of **ampvis2 load** {% icon tool %}) +> - *"Step size"*: `5` +> - *"Color curves by"*: `sample_id` +> - *"Scales of the facets"*: `Free scale` +> +{: .hands_on} +
+ +Unfortunately multiple parameter selection is not possible at the time of the publication of this tutorial and +the workflow can not be prepared and and executed all at once. Therefore, both tools, **ampvis load** and **ampvis subset samples**, +must be run first before proceeding with the remaining workflow that includes the visualisation tools. + +### **ampvis2 load** +
+> Create ampvis2_load datasets +> +> Run {% tool [ampvis2 load](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_load/ampvis2_load/2.8.9+galaxy0) %} +> +{: .hands_on} +
+ +
+> Create ampvis2_load datasets +> +> 1. {% tool [ampvis2 load](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_load/ampvis2_load/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"OTU table"*: `output` (Input dataset) +> - {% icon param-file %} *"Sample metadata"*: `output` (Input dataset) +> - {% icon param-file %} *"Taxonomy table"*: `output` (Input dataset) +> +{: .hands_on} +
+ +You will need the output of **ampvis2 load** as input for **ampvis2 subset samples**. + +### **ampvis2 subset samples** +
+> Create subsamples datasets +> +> Run {% tool [ampvis2 subset samples](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_subset_samples/ampvis2_subset_samples/2.8.9+galaxy0) %} using the following parameters +> - *"Metadata variable"*: `sample_id` +> - *"Metadata value(s)"*: `COI-B1b COI-B2a COI-B3 COI-B4 COI-B5 COI-B6 COI-B7 COI-B8 COI-B9 COI-B10 COI-B11 COI-B12 COI-B13 COI-B14 COI-B15 COI-B16 COI-B17 COI-B18 COI-B19 COI-B20 COI-B21 COI-B22 COI-B23` +> +{: .hands_on} +
+ +
+> Create subsamples datasets +> +> 1. {% tool [ampvis2 subset samples](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_subset_samples/ampvis2_subset_samples/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Metadata variable"*: `sample_id` +> - *"Metadata value(s)"*: `COI-B1b COI-B2a COI-B3 COI-B4 COI-B5 COI-B6 COI-B7 COI-B8 COI-B9 COI-B10 COI-B11 COI-B12 COI-B13 COI-B14 COI-B15 COI-B16 COI-B17 COI-B18 COI-B19 COI-B20 COI-B21 COI-B22 COI-B23` +> +{: .hands_on} +
+ +Now you can use the output of **ampvis2 subset samples** as input for the rarefaction curve. + +> Select the right dataset to continue +> +> If you are not sure how to select the right datasets, you can scroll down to use case 2, find the details box with the same name as this one +and look at the picture how it was selected there. +> +{: .details} + +
+> Create a rarefaction curve +> +> Run {% tool [ampvis2 rarefaction curve](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_rarecurve/ampvis2_rarecurve/2.8.9+galaxy0) %} +> +{: .hands_on} +
+ +
+> Rarefaction curve workflow steps +> +> 1. {% tool [ampvis2 rarefaction curve](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_rarecurve/ampvis2_rarecurve/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `ampvis` (output of **ampvis2 subset samples** {% icon tool %}) +> - {% icon param-file %} *"Metadata list"*: `metadata_list_out` (output of **ampvis2 subset samples** {% icon tool %}) +> - *"Step size"*: `5` +> - *"Color curves by"*: `sample_id` +> - *"Scales of the facets"*: `Free scale` +> +{: .hands_on} +
+ +> How it will look like +> +> The result of the rarefaction curve indicates that the sample __"COI-B2b"__ has the highest richness, with over 150 observed OTUs, +whereas the other samples have fewer than 50 observed OTUs. +> +>![Result of the rarefaction curve](./images/rarefaction.png "Result of the rarefaction curve") +{: .details} + +> +> +> 1. If you run the workflow with subset on Galaxy and use the following metadata for the subset: + metadata variable = sample_id and metadata values = (select all possible samples). + What is the difference from the rarefaction curve you generated in the workflow without subsets? +> 2. If you consider the output of the rarefaction curve before, one sample has a high curve and the rest are close to each other. + Can you make the rest more "visible"? +> +> > +> > +> > 1. There is no difference;, it's the same output +> > 2. Yes, if you run the workflow (with subsets) and select all samples except of __"COI-B2b"__ +> > +> > > How it will look like +> > > +> > > Result of this rarefaction curve. +> > > +> > >![Result of this rarefaction curve](./images/rarefaction_without.png "Result of the rarefaction curve without "COI-B2b" ") +> > > +> > {: .details} +> > +> {: .solution} +> +{: .question} + +# Use Case 2: Heatmap, Ordination Plot or Boxploot + +To create a heatmap, ordination plot, or boxplot you can continue with your dataset or use the same as shown in the next sections. + +> +> - In this section normalised data are used and a different dataset than for the rarefaction curve, as this dataset has more metadata +> - However, this normalised data cannot be use for rarefaction analysis, which requires raw data. Rarefaction analysis +examines the distribution of sequencing depth across samples by counting the number of observed species or OTUs. +Normalised data loses information about the original sequencing depth, making it impossible to accurately evaluate species richness. +{: .comment} + +## Get Data + +> Data Upload +> +> Import the files from [Zenodo]({{ page.zenodo_link }}) or a data library: +> +> ```text +> {{ page.zenodo_link }}/files/MiDAS_otushort_table.tsv +> {{ page.zenodo_link }}/files/MiDAS_metadata.tsv +> {{ page.zenodo_link }}/files/MiDAS_taxtable.tsv +> ``` +> +{: .hands_on} + +![Running the workflow](./images/choose_parameters.png "Running the workflow, choose the right datasets and mandatory parameters") + +## Create datasets +First you will need to run these 2 tools ampvis2_load and after that ampvis2_subset. Here is how to run the tools: +### **ampvis2 load** +
+> Create ampvis2_load datasets +> +> Run {% tool [ampvis2 load](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_load/ampvis2_load/2.8.9+galaxy0) %} +> +{: .hands_on} +
+ +
+> Create ampvis2_load datasets +> +> 1. {% tool [ampvis2 load](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_load/ampvis2_load/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"OTU table"*: `output` (Input dataset) +> - {% icon param-file %} *"Sample metadata"*: `output` (Input dataset) +> - {% icon param-file %} *"Taxonomy table"*: `output` (Input dataset) +> +{: .hands_on} +
+ +You will need the output of **ampvis2 load** as input for **ampvis2 subset samples**. + +### **ampvis2 subset samples** +
+> Create subsamples datasets +> +> Run {% tool [ampvis2 subset samples](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_subset_samples/ampvis2_subset_samples/2.8.9+galaxy0) %} using the following parameters +> - *"Metadata variable"*: `Plant` +> - *"Metadata value(s)"*: `Aalborg East & Aalborg West` +> +{: .hands_on} +
+ +
+> Create subsamples datasets +> +> 1. {% tool [ampvis2 subset samples](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_subset_samples/ampvis2_subset_samples/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Metadata variable"*: `Plant` +> - *"Metadata value(s)"*: `Aalborg East & Aalborg West` +> +{: .hands_on} +
+ +Now you can use the output of **ampvis2 subset samples** as input for the rest of use case 2 and for use case 3. + +> Select the right dataset to continue +> +> Select the datasets from **ampvis2 subset samples** output. Also you will need to select parameters to group (and facet) +the heatmaps. You will find these parameters listed under the corresponding heatmap sections. +> +>![select dataset](./images/select_datasets.png "Select the datasets from **ampvis2 subset samples** output") +> +{: .details} + +## Heatmaps +Heatmaps show relationships between 2 variables ploted on 2 axis and colour intensity representing the abundance of taxa in +relation to what it was ploted to, like sample, specific plant or year ([A complete guide to heatmaps](https://www.atlassian.com/data/charts/heatmap-complete-guide)). + +Now, the data can be used to create subsets and generate ungrouped or grouped outputs, including those with facets. +The subsets are based on variables that are define and are available in the metadata {% cite Andersen2018 %}. + +### Heatmap (ungrouped) +Follow this workflow to create a simple heatmap without grouping or faceting data. +
+> Create a simple heatmap +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_heatmap__ordination__boxplot.ga) {% icon workflow %} +> +{: .hands_on} +
+ +
+> Create a simple heatmap +> +> 1. {% tool [ampvis2 heatmap](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_heatmap/ampvis2_heatmap/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"The taxonomic level to aggregate the OTUs"*: `Phylum` +> - *"Limit the number of shown taxa"*: `Select a number of taxa to show` +> - *"Plot the values on the heatmap"*: `Yes` +> - *"Color missing values with the lowest color in the scale"*: `Yes` +> - *"Sort heatmap by most abundant taxa"*: `No` +> - *"Show functional information about the Genus-level OTUs"*: `No` +> +{: .hands_on} +
+ +> How it will look like +> +> Heatmap showing ungrouped data, where each sample is plotted against the identified Phyla. +The colour intensity represents the abundance of each Phylum within the samples. The last line shows the remaining taxa. +> +>![Result of the heatmap](./images/heatmap_no_group_new.png "Result of the heatmap") +> +> > +> > +> > Difault pdf output is generating a compressed picture, to make it more visible click on heatmap, go to "output optionts" + open it and input a different plot width or height. +> > +> {: .comment} +> +>![Result of the heatmap with 20 cm plot width](./images/heatmap_no_group_total.png "Result of the heatmap with 20 cm plot width") +{: .details} + +> Play around with options of output +> +> * Choose a different **The taxonomic level to aggregate the OTUs**, in the Tutorial **Phylum** was used, but you might have different preferences +> * Use **Additional taxonomic level(s) to display** to show more taxa on the plot +> * Select **Plot the values on the heatmap** as **No** to generate a legend for the heatmap, or as **YES** to have the values insede the heatmap +> * Set **Display sum of remaining taxa** to **YES** to have the sum as last row on your plot +> +{: .tip} + +### Heatmap (grouped) +Subsampling is used to standardise the dataset by reducing it to a manageable size, often resulting in the removal of rare taxa. +Grouping involves aggregating data based on specific criteria, which simplifies the heatmap by reducing complexity and noise. +As a result, the community structure of the microbiome becomes clearer and easier to interpret. + +We used 2 different subsets of metadata to focus on specific aspects of the data: +- 1) **Plant-based subset**: In this subset, the metadata variable chosen was Plant, with the metadata values being +Aalborg East and Aalborg West. This subset focuses on comparing the two different plant locations. The data were +grouped according to the variable Plant, which allows us to examine how the microbial communities differ between these two locations +- 2) **Seasonal subset**: The second subset focused on the Period variable, with the metadata values being Winter and Summer. +This subset was grouped by Year and aims to explore how microbial communities vary between the two seasons + +Follow this workflow to create a heatmap by grouping the data. +
+> Create a heatmap by grouping the data +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_heatmap__ordination__boxplot.ga) {% icon workflow %} using the following parameters +> - *"Group samples"*: `Plant` (Option 1) or +> - *"Group samples"*: `Year` (Option 2) +> +{: .hands_on} +
+ +
+ +> Create a heatmap by grouping the data +> +> 1. {% tool [ampvis2 heatmap](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_heatmap/ampvis2_heatmap/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Group samples"*: `Plant` (Option 1) or `Year` (Option 2) +> - *"The taxonomic level to aggregate the OTUs"*: `Phylum` +> - *"Limit the number of shown taxa"*: `Select a number of taxa to show` +> - *"Plot the values on the heatmap"*: `Yes` +> - *"Color missing values with the lowest color in the scale"*: `Yes` +> - *"Sort heatmap by most abundant taxa"*: `No` +> - *"Show functional information about the Genus-level OTUs"*: `No` +> +{: .hands_on} +
+ +> How it will look like +> +> Result of the first metadata subset heatmap created with grouped by Plant data. To see the difference, one is ploted with +values inside the heatmap itself and the other with a legend instead. +> +>![Result of the heatmap](./images/heatmap_gr_by_plant.png "Result of the heatmap created with grouped by _Plant_ with values inside the heatmap") +> +>![Result of the heatmap](./images/heatmap_gr_by_plant_new.png "Result of the heatmap created with grouped by _Plant_ with a legend") +> +> Result of the second metadata subset heatmap created with grouped by Year data. +> +>![Result of the heatmap](./images/heatmap_gr_by_year.png "Result of the heatmap created with grouped by _Year_" and also showing additional taxa") +{: .details} + +> +> +> 1. Can you create a heatmap that shows only the first and the last year of data collection? +> 2. Can you create a heatmap using the following settings: metadata variable = Year and + metadata value = Date plus grouped by = Year? +> +> > +> > +> > 1. Yes, with the following settings: metadata variable = Year, + metadata values = 2006 & 2015, grouped by = Year +> > 2. No, the metadata values must correspond to the metadata variable options +> > +> {: .solution} +> +{: .question} + +### Heatmap (grouped with facets) +We used 2 different metadata subsets: +- 1) Metadata used for this subset: metadata variable = Plant, metadata values = Aalborg East & Aalborg West, + grouped by = Plant, facet by = Period +- 2) Metadata used for this subset: metadata variable = Period, metadata values = Winter & Summer, + grouped by = Year, facet by = Period + +Follow this workflow to create a heatmap by grouping and faceting the data. +
+> Create a heatmap by grouping and faceting the data +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_heatmap__ordination__boxplot.ga) {% icon workflow %} using the following parameters +> - *"Group samples"*: `Plant` (Option 1) or `Year` (Option 2) +> - *"Facet the samples"*: `Period` +> +{: .hands_on} +
+ +
+ +> Create a heatmap by grouping and faceting the data +> +> 1. {% tool [ampvis2 heatmap](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_heatmap/ampvis2_heatmap/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Group samples"*: `Plant` (Option 1) or `Year` (Option 2) +> - *"Facet the samples"*: `Period` +> - *"The taxonomic level to aggregate the OTUs"*: `Phylum` +> - *"Limit the number of shown taxa"*: `Select a number of taxa to show` +> - *"Plot the values on the heatmap"*: `Yes` +> - *"Color missing values with the lowest color in the scale"*: `Yes` +> - *"Sort heatmap by most abundant taxa"*: `No` +> - *"Show functional information about the Genus-level OTUs"*: `No` +> +{: .hands_on} +
+ +> How it will look like +> +> Result of the first metadata subset heatmap created with grouped by Plant data and facet by Period. +> +>![Result of the heatmap](./images/heatmap_plant_period.png "Result of the heatmap created with grouped by _Plant_ and facet by _Period_ ") +> +> Result of the second metadata subset heatmap created with grouped by Year data and facet by Period. +> +>![Result of the heatmap](./images/heatmap_year_period_new.png "Result of the heatmap created with grouped by _Year_ and facet by _Period_ ") +> +{: .details} + +## Ordination Plots +Ordination techniques are needed to plot a multidimensional dataset onto a lower-dimensional space. In ecological datasets, similar +samples and species are plotted close to each other, while dissimilar samples and species will be found far apart. The dimensions +on the ordination plot represent enviromental gradients and describe the relationship between species patterns and environmental +gradient ([Introduction to ordination](https://ourcodingclub.github.io/tutorials/ordination/)). + +We can now use our data, generate subsets, and create different plots by applying various ordination methods. +As with heatmaps, the subsets are based on variables that are defined and are available in the metadata {% cite Andersen2018 %}. + + +### Ordination Method: PCA +PCA (Principal Component Analysis) is an unconstrained ordination technique and a common reduction technique to linear dimensionality +([MDAnalysis User Guide](https://userguide.mdanalysis.org/stable/examples/analysis/reduced_dimensions/pca.html)). + +Follow this workflow to create a simple ordination plot. +
+> Create a simple ordination plot +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_heatmap__ordination__boxplot.ga) {% icon workflow %} +> +{: .hands_on} +
+ +
+> Create a simple ordination plot +> +> 1. {% tool [ampvis2 ordination plot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_ordinate/ampvis2_ordinate/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Ordination method"*: `(PCA) Principal Components Analysis` +> - *"Color sample points by"*: `Plant` +> - *"Label Frame by"*: `Plant` +> - *"Plot species points"*: `No` +> +{: .hands_on} +
+ +> How it will look like +> +> Result of the ordination plot created with the PCA method. Samples and species with the most similarities in the plant +Aalborg East are closer together on the plot, same for Aalborg West plant. +> +>![Result of the ordiantion plot](./images/ordination_pca.png "Result of the ordination plot created with the PCA method") +{: .details} + +### Ordination Method: PCA plus Trajectory +Using the PCA method, the analysis can be improved by adding a trajectory. This allows the samples +to be visualised following a path over the time points at which they were collected. + +Follow this workflow to create an ordination plot with trajectory. +
+> Create an ordination plot with trajectory +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_heatmap__ordination__boxplot.ga) {% icon workflow %} using the following parameters +> - *"Make a trajectory between sample points by"*: `Date` +> +{: .hands_on} +
+ +
+> Create an ordination plot with trajectory +> +> 1. {% tool [ampvis2 ordination plot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_ordinate/ampvis2_ordinate/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Ordination method"*: `(PCA) Principal Components Analysis` +> - *"Color sample points by"*: `Plant` +> - *"Frame the sample points with a polygon by"*: `Plant` +> - *"Label Frame by"*: `Plant` +> - *"Make a trajectory between sample points by"*: `Date` +> - *"Plot species points"*: `No` +> +{: .hands_on} +
+ +> How it will look like +> +> Result of the ordination plot created with the PCA method plus using the trajectory date. The points are connected in the order they +were sampled. +> +>![Result of the ordiantion plot](./images/ordination_pca_date.png "Result of the ordination plot created with the PCA method plus using the trajectory date") +{: .details} + +> +> +> Does it make sense to run the following settings: metadata variable = Period and metadata values = Winter & Summer? +> +> > +> > +> > No, you will get a very messy bundle of colours. +> > +> {: .solution} +> +{: .question} + +### Ordination Method: CCA +CCA (Canonical Correspondance Analysis) is a constrained ordination technique derived form CA (Correspondance Analysis) and is +prefered for most ecological data. CCA maximises the correlation between samples and species scores ([Ordination Methods](https://ordination.okstate.edu/overview.html)). +Hellinger transformation is a modified species profile computed from Euclidean distances, organised into a matrix of +Hellinger distances. Hellinger distances are used to measure clustering or ordination of species abundance ([Ordination](https://www.numericalecology.com/Reprints/Legendre_Ordination_chapter_in_Palaeolimnology_book_2012.pdf)). + +Follow this workflow to create an ordination plot with the CCA method and the Hellinger transformation. +
+> Create an ordination plot with trajectory +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_heatmap__ordination__boxplot.ga) {% icon workflow %} using the following parameters +> - *"Ordination method"*: `(CCA) Canonical Correspondence Analysis (considered the constrained version of CA)` +> - *"Transforms the abundances before ordination"*: `square root of method = "total" (hellinger)` +> - *"Constrain analysis by"*: `Period` +> +{: .hands_on} +
+ +
+> Create an ordination plot with transformation +> +> 1. {% tool [ampvis2 ordination plot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_ordinate/ampvis2_ordinate/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Ordination method"*: `(CCA) Canonical Correspondence Analysis (considered the constrained version of CA)` +> - *"Transforms the abundances before ordination"*: `square root of method = "total" (hellinger)` +> - *"Constrain analysis by"*: `Period` +> - *"Color sample points by"*: `Period` +> - *"Shape sample points by"*: `Plant` +> - *"Frame the sample points with a polygon by"*: `Date` +> - *"Label Frame by"*: `Period` +> - *"Plot species points"*: `No` +> +{: .hands_on} +
+ +> How it will look like +> +> The result of the ordination plot created using the CCA method and the Hellinger transformation shows the correlation between taxa and +seasons in the plants Aalborg East and Aalborg West. +> +>![Result of the ordiantion plot](./images/ordination_cca_hellinger.png "Result of the ordination plot created with the CCA method and the Hellinger transformation") +{: .details} + +> +> +> If you use the CCA ordination method with the following settings: metadata variable = Period and metadata values = Winter & Summer. + What do you need to remove from pre-selected parameters to keep the ordination plot stays readable? +> +> > +> > +> > When you expand the ordination plot set in your history, you see options for colour, shape, frame, and label by. Select colour by _Period_ +and frame by _Period_ and deselect the other mentioned options above, so they read _"Nothing_ _selected"_ . +> > +> {: .solution} +> +{: .question} + +## Boxplot +Boxplots are used to provide high-level information at a glance and make comparisons between mulpiple groups very easy, as they offer +general information about data symmetry, skew, variance and outliers ([A complete guide to box plots](https://www.atlassian.com/data/charts/box-plot-complete-guide)). + +As with heatmaps, the subsets are based on variables that are defined and are available in the metadata {% cite Andersen2018 %}. + +
+> Create a boxplot +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_heatmap__ordination__boxplot.ga) {% icon workflow %} using the following parameters +> - *"Group samples"*: `Period` +> - *"Limit the number of shown taxa"*: `Select a number of taxa to show` +> - *"Number of taxa to show"*: `5` +> +{: .hands_on} +
+ +
+> Create a boxplot +> +> 1. {% tool [ampvis2 boxplot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_boxplot/ampvis2_boxplot/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Group samples"*: `Period` +> - *"The taxonomic level to aggregate the OTUs"*: `Phylum` +> - *"Limit the number of shown taxa"*: `Select a number of taxa to show` +> - *"Number of taxa to show"*: `5` +> +{: .hands_on} +
+ +> How it will look like +> +> Result of the boxplot grouped by Period. +> +>![Result of the boxplot](./images/boxplot_period.png "Result of the boxplot grouped by Period") +{: .details} + +> Create a different boxplot +> +> * Set metadata variable = Period and metadata values = Summer & Winter +> +> > How it will look like +> > +> > Result of this boxplot. +> > +> > ![Result of the boxplot](./images/boxplot_other.png "Result of this boxplot") +> > +> {: .details} +> +{: .tip} + +> +> +> 1. Can you create an output where only odd years are considered? +> 2. Do you need to change any pre-selected parameter for question 1? +> +> > +> > +> > 1. Yes, if you set metadata variable = Year and metadata values = 2007, 2009, 2011, 2013, 2015 +> > 2. Yes, set "group the sample" to _Year_ +> > +> {: .solution} +> +{: .question} + +# Use Case 3: Time Series Plot + +Time series analysis is primarily known for forecasting. A time series can be viewed as +an example of a random or stochastic process, which can be used to visualise seasonal +differences {% cite DeGooijer2006 %}. + +In the dataset, and with the settings listed below, the temporal evolution of the 3 most common microorganisms in the plants +Aalborg East and Aalborg West can be observed over the entire data collection period. + +## Create a Time Series Plot +Like in use case 2, you will need the ampvis2_load datasets as well as ampvis2_subset datasets to start of. + +So, in the same history where you created heatmaps, ordination plots and boxplots with the **metadata variable**: _Plant_ and +**metadata value(s)**: _Aalborg_ _East_ & _Aalborg_ _West_ , select the right datasets and follow this workflow to create a time series plot. + +
+> Create a boxplot +> +> Import and Run [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/ampvis2_timeseries_v1.0.ga) {% icon workflow %} using the following parameters +> - *"Time variable"*: `Date` +> - *"Limit the number of shown taxa"*: `Select a number of taxa to show` +> - *"Number of taxa to show"*: `3` +> +{: .hands_on} +
+ +
+> Create a time series plot +> +> 1. {% tool [ampvis2 timeseries plot](toolshed.g2.bx.psu.edu/repos/iuc/ampvis2_timeseries/ampvis2_timeseries/2.8.6+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Ampvis2 RDS dataset"*: `output` (Input dataset) +> - {% icon param-file %} *"Metadata list"*: `output` (Input dataset) +> - *"Time variable"*: `Date` +> - *"The taxonomic level to aggregate the OTUs"*: `Phylum` +> - *"Limit the number of shown taxa"*: `Select a number of taxa to show` +> - *"Number of taxa to show"*: `3` +> +{: .hands_on} +
+ + +> How it will look like +> +> Result of the time series plot. +> +>![Result of the time series plot](./images/timeseries.png "Result of the time series plot") +{: .details} + +> +> +> 1. If you run the following settings: metadata variable = Period and metadata values = Winter & Summer. + Do you need to change the time variable in the pre-selected parameters? +> 2. Can you adjust the settings to separate the 3 curves into distinct curves for each period, corresponding to the shown Phylum? +> +> > +> > +> > 1. No, _Date_ is still the only possible option +> > 2. Yes, you can separate the curves by expanding the time series set in the history and running it again with "group the sample by" _Period_ +> > +> {: .solution} +> +{: .question} + + +# Conclusion +This tutorial has equipped you with essential skills for visualising and interpreting microbiome data using ampvis2. +By exploring diverse visualisation methods and leveraging metadata effectively, you've learned how to gain valuable insights +from complex datasets. 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