Merge pull request #5808 from bernt-matthias/multigsea

Add tutorial for multigsea

CONTRIBUTORS.yaml

@@ -2916,11 +2916,14 @@ tbrown91:
     orcid: 0000-0001-8293-4816
     joined: 2024-08
 
+tStehling:
+    name: Thorben Stehling
+    joined: 2024-11
+
 rmassei:
     name: Riccardo Massei
     email: [email protected]
     orcid: 0000-0003-2104-9519
     joined: 2024-11
     affiliations:
       - nfdi4bioimage
-

topics/proteomics/tutorials/multiGSEA-tutorial/data-library.yaml

@@ -0,0 +1,27 @@
+---
+destination:
+  type: library
+  name: GTN - Material
+  description: Galaxy Training Network Material
+  synopsis: Galaxy Training Network Material. See https://training.galaxyproject.org
+items:
+- name: The new topic
+  description: Summary
+  items:
+  - name: Using MultiGSEA
+    items:
+    - name: 'DOI: 10.5281/zenodo.14216972'
+      description: latest
+      items:
+      - url: https://zenodo.org/api/records/14216972/files/metabolome.tsv/content
+        src: url
+        ext: auto
+        info: https://zenodo.org/records/14216972
+      - url: https://zenodo.org/api/records/14216972/files/proteome.tsv/content
+        src: url
+        ext: auto
+        info: https://zenodo.org/records/14216972
+      - url: https://zenodo.org/api/records/14216972/files/transcriptome.tsv/content
+        src: url
+        ext: auto
+        info: https://zenodo.org/records/14216972

topics/proteomics/tutorials/multiGSEA-tutorial/faqs/index.md

@@ -0,0 +1,3 @@
+---
+layout: faq-page
+---

topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.bib

@@ -0,0 +1,48 @@
+
+# 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
+
+@misc{https://doi.org/10.18129/b9.bioc.multigsea,
+  doi = {10.18129/B9.BIOC.MULTIGSEA},
+  url = {https://bioconductor.org/packages/multiGSEA},
+  author = {{Sebastian Canzler,  J\"{o}rg Hackerm\"{u}ller}},
+  title = {multiGSEA},
+  publisher = {Bioconductor},
+  year = {2020}
+}
+
+@article{quiros2017multi,
+  title={Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals},
+  author={Quir{\'o}s, Pedro M and Prado, Miguel A and Zamboni, Nicola and D’Amico, Davide and Williams, Robert W and Finley, Daniel and Gygi, Steven P and Auwerx, Johan},
+  journal={Journal of Cell Biology},
+  volume={216},
+  number={7},
+  pages={2027-2045},
+  year={2017},
+  publisher={The Rockefeller University Press},
+  doi={10.1083/jcb.201702058}
+}
+
+@article{LOUGHIN2004467,
+title = {A systematic comparison of methods for combining p-values from independent tests},
+journal = {Computational Statistics & Data Analysis},
+volume = {47},
+number = {3},
+pages = {467-485},
+year = {2004},
+issn = {0167-9473},
+doi = {10.1016/j.csda.2003.11.020},
+author = {Thomas M. Loughin}
+}

topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md

@@ -0,0 +1,168 @@
+---
+layout: tutorial_hands_on
+
+title: Multiomics data analysis using MultiGSEA
+subtopic: multi-omics
+tags:
+  - multi-omics
+  - transcriptomics
+  - proteomics
+  - metabolomics
+zenodo_link: 'https://zenodo.org/records/14216972'
+questions:
+- How to use MultiGSEA for GSEA-based pathway enrichment for multiple omics layers?
+objectives:
+- Perform GSEA-based pathway enrichment for transcriptomics, proteomics, and metabolomics data.
+- Understand how to combine p-values across multiple omics layers.
+time_estimation: 1H
+key_points:
+- MultiGSEA provides an integrated workflow for pathway enrichment analysis across multi-omics data.
+- Supports pathway definitions from several databases and robust ID mapping.
+contributions:
+  authorship:
+    - tStehling
+    - bernt-matthias
+
+
+---
+
+
+The multiGSEA package was designed to run a robust GSEA-based pathway enrichment for multiple omics layers. The enrichment is calculated for each omics layer separately and aggregated p-values are calculated afterwards to derive a composite multi-omics pathway enrichment.
+
+Pathway definitions can be downloaded from up to eight different pathway databases by means of the graphite Bioconductor package (Sales, Calura, and Romualdi 2018). Feature mapping for transcripts and proteins is supported towards Entrez Gene IDs, Uniprot, Gene Symbol, RefSeq, and Ensembl IDs. The mapping is accomplished through the AnnotationDbi package (Pagès et al. 2019) and currently supported for 11 different model organisms including human, mouse, and rat. ID conversion of metabolite features to Comptox Dashboard IDs (DTXCID, DTXSID), CAS-numbers, Pubchem IDs (CID), HMDB, KEGG, ChEBI, Drugbank IDs, or common metabolite names is accomplished through the AnnotationHub package metabliteIDmapping. This package provides a comprehensive ID mapping for more than 1.1 million entries.
+
+This tutorial covers a simple example workflow illustrating how the multiGSEA package works. The omics data sets that will be used throughout the example were originally provided by {% cite quiros2017multi %}. In their publication the authors analyzed the mitochondrial response to four different toxicants, including Actinonin, Diclofenac, FCCB, and Mito-Block (MB), within the transcriptome, proteome, and metabolome layer.
+In this tutorial we will solely focus on the Actinonin data set.
+
+For more background information you can read following articles: 
+
+- Multiomics analysis by Quiros {% cite quiros2017multi %}
+
+- Methods for combining p-values by Loughin {% cite LOUGHIN2004467 %}
+
+> <agenda-title></agenda-title>
+>
+> In this tutorial, we will cover:
+>
+> 1. TOC
+> {:toc}
+>
+{: .agenda}
+
+# Preparing the Data
+
+To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the file type TSV . Each individual data set contains four columns representing the feature (denoted as Symbol), the log2 fold change (logFC), the p-value (pValue), and the adjusted p-values (adj.pValue). We'll use example data provided on Zenodo.
+
+## Get data
+
+### Data Upload
+
+> <hands-on-title> Getting datasets </hands-on-title>
+> 1. Create a new history for this tutorial.
+>
+>    {% snippet faqs/galaxy/histories_create_new.md %}
+>
+> 2. Import the datasets from [Zenodo]({{ page.zenodo_link }})  into your Galaxy instance:
+>    ```
+>    https://zenodo.org/records/14216972/files/transcriptome.tsv
+>    https://zenodo.org/records/14216972/files/proteome.tsv
+>    https://zenodo.org/records/14216972/files/metabolome.tsv
+>    ```
+{: .hands_on}
+
+
+# Running MultiGSEA
+
+In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrichment on the uploaded datasets.
+
+> <hands-on-title> Task description </hands-on-title>
+>
+> 1. Run {% tool [multiGSEA](toolshed.g2.bx.psu.edu/repos/iuc/multigsea/multigsea/1.12.0+galaxy0) %} with the following parameters
+>    - *"Select transcriptomics data"*: `Enabled`
+>        - {% icon param-file %} *"Transcriptomics data"*: `Transcriptomics`
+>        - {% icon param-select %} *"Gene ID format in transcriptomics data"*: `SYMBOL`
+>    - *"Select proteomics data"*: `Enabled`
+>        - {% icon param-file %} *"Proteomics data"*: `Proteomics`
+>        - {% icon param-select %} *"Gene ID format in proteomics data"*: `SYMBOL`
+>    - *"Select metabolomics data"*: `Enabled`
+>        - {% icon param-file %} *"Metabolomics data"*: `Metabolomics`
+>        - {% icon param-select %} *"Metabolite ID format"*: `HMDB`
+>    - *"Supported organisms"*: `Homo sapiens (Human)`.
+>    - *"Pathway databases"*: `Kegg`, `Reactome`
+>    - *"Combine p-values method"*: `Stouffer`
+>    - *"P-value correction method"*: `BH`
+>
+>    > <tip-title>About the parameters</tip-title>
+>    > - **Pathway databases**: Databases often contain their own format in which pathway definitions are provided. So you can select a relevant database. For the tutorial we choose the preset "Kegg" and "Reactome".
+>    > - **Combine p-values method**: Choose a method (here `Stouffer` for balanced weighting). To more comprehensively measure a pathway response, multiGSEA provides different approaches to compute an aggregated p value over multiple omics layers. Because no single approach for aggregating p values performs best under all circumstances, {% cite LOUGHIN2004467 %} proposed basic recommendations on which method to use depending on structure and expectation of the problem. If small p values should be emphasized, Fisher’s method should be chosen. In cases where p values should be treated equally, Stouffer’s method is preferable. If large p values should be emphasized, the user should select Edgington’s method. Figure 2 indicates the difference between those three methods.  
+>    >   ![P-Value](../../images/p-value.png "P-value methods")
+>    > - **P-value correction method** Type I and type II errors depend on each other and thus reducing type I errors through a p value adjustment will likely increase the chance of making a type II error and an appropriate trade-off has to be made. Choose one of the different methods for controlling false discovery rate: For the tutorial choose `BH` (Benjamini-Hochberg).
+>    {: .tip}
+>
+{: .hands_on}
+
+# Mitochondrial stress activates metabolic pathways of amino acid biosynthesis.
+
+After we performed pathway enrichment on our data we want to continue our analysis by filtering the outputs, e.g. by p-value.
+
+><hands-on-title> Filtering by values</hands-on-title>
+>
+> We are going to use the tool {% tool [Filter data on any column using simple expressions](Filter1) %} with the following parameters:
+> - {% icon param-file %} *"Filter"*: Select the output dataset of the multiGSEA tool.
+> - *"With following condition"*: `c9<=0.01`
+>
+{: .hands_on}
+
+
+><hands-on-title> Filtering by keyword</hands-on-title>
+>
+>Mitochondrial stress triggers the activation of amino acid biosynthesis and related metabolic pathways, as highlighted by Quiros et al. (Ref findest du in der Vignette), who identified up-regulation of several amino acid related pathways.
+>These findings align with our results using a multi-omics approach with multiGSEA
+>Our results also reveal the enrichment of amino acid-related pathways. This can be see, e.g., by searching for pathways containing the word "amino" in the name. We can search for this using the regular expression `"\bamino\b"` (`\b` is a special character marking a word border).
+>
+>
+>For further filtering we are going to use the tool {% tool [Search in textfiles](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_grep_tool/9.3+galaxy1) %} with the following parameters:
+> - {% icon param-file %} *"Selectlines from"*: Select the output of the last filter.
+> - *"that"*: Choose "Match"
+> - *"Type of regex*": Choose "Basic (-G)"
+> - *"Regular Expression"*: \bamino\b
+> - *"Match type"*: "case insensitive"
+> - *"Show lines preceding the matched line"*: 0
+> - *"Show lines trailing the matched line"*: 0
+> - "*Output*": "text file (for further processing)"
+>
+>This should produce a file with the following content:
+>```
+>(KEGG) Amino sugar and nucleotide sugar metabolism	0.258278145695364	0.571272307893517	8.40563134115856e-07	4.03302191748788e-05	2.34233775080415e-05	0.00310502061987873	8.75854833022965e-05	0.00347274191804089
+>(KEGG) Biosynthesis of amino acids	3.7749282501799e-05	0.00138827212066317	1.53729180439681e-10	2.45864202583197e-08	0.0794621026894866	0.333364038319823	1.48946778740876e-07	1.13741176493032e-05
+>(REACTOME) Amino acid and derivative metabolism	4.7058253543142e-06	0.000231903073460604	7.70587893652191e-22	1.84864035687161e-18	0.055363321799308	0.286607081313329	9.03772696553135e-14	2.2775071953139e-11
+>(REACTOME) Response of EIF2AK4 (GCN2) to amino acid deficiency	6.42075812177139e-07	4.52021371772706e-05	3.76589836665495e-10	5.31434716565013e-08	NA	NA	3.35403180955872e-11	4.83629358772636e-09
+>
+>```
+>
+{: .hands_on}
+
+> <question-title></question-title>
+>
+> 1. What file format is required for the input data in MultiGSEA?
+> 2. What is the purpose of the “Combine p-values method” parameter, and which method was selected in this tutorial?
+> 3. Why is it important to select pathway databases (e.g., KEGG) when using MultiGSEA?
+>
+> > <solution-title></solution-title>
+> >
+> > 1. The required file format is TSV.
+> > 2. The “Combine p-values method” parameter is used to aggregate p-values across omics layers. In this tutorial, the method Stouffer was selected to apply balanced weighting.
+> > 3. Selecting pathway databases ensures that the analysis uses appropriate and relevant pathway definitions for enrichment.
+> >
+> {: .solution}
+>
+{: .question}
+
+
+# Conclusion
+
+In this tutorial, you explored the capabilities of MultiGSEA for performing pathway enrichment analysis across multiple omics layers, including transcriptomics, proteomics, and metabolomics data. By following the steps, you learned how to:
+
+ - Prepare and upload the required omics datasets.
+ - Configure and execute the MultiGSEA tool within Galaxy.
+ -  Combine p-values from different omics layers to derive a unified perspective on pathway enrichment.

topics/proteomics/tutorials/multiGSEA-tutorial/workflows/MultiGSEA-tests.yml

@@ -0,0 +1,19 @@
+- doc: Test outline for MultiGSEA
+  job:
+    transcriptome.tsv:
+      class: File
+      location: https://zenodo.org/records/14216972/files/transcriptome.tsv
+      filetype: tabular
+    proteome.tsv:
+      class: File
+      location: https://zenodo.org/records/14216972/files/proteome.tsv
+      filetype: tabular
+    metabolome.tsv:
+      class: File
+      location: https://zenodo.org/records/14216972/files/metabolome.tsv
+      filetype: mothur.axes
+  outputs:
+    Filtered output:
+      asserts:
+        has_text:
+          text: "amino"