From 20b6888666983a139f95b805546202b841d3387b Mon Sep 17 00:00:00 2001 From: Thorben Date: Wed, 27 Nov 2024 12:01:56 +0100 Subject: [PATCH 1/8] Created tutorial for multiGSEA --- .../multiGSEA-tutorial/data-library.yaml | 27 +++++ .../multiGSEA-tutorial/faqs/index.md | 3 + .../tutorials/multiGSEA-tutorial/tutorial.bib | 26 +++++ .../tutorials/multiGSEA-tutorial/tutorial.md | 104 ++++++++++++++++++ .../multiGSEA-tutorial/workflows/index.md | 3 + .../workflows/main_workflow.ga | 1 + 6 files changed, 164 insertions(+) create mode 100644 topics/statistics/tutorials/multiGSEA-tutorial/data-library.yaml create mode 100644 topics/statistics/tutorials/multiGSEA-tutorial/faqs/index.md create mode 100644 topics/statistics/tutorials/multiGSEA-tutorial/tutorial.bib create mode 100644 topics/statistics/tutorials/multiGSEA-tutorial/tutorial.md create mode 100644 topics/statistics/tutorials/multiGSEA-tutorial/workflows/index.md create mode 100644 topics/statistics/tutorials/multiGSEA-tutorial/workflows/main_workflow.ga diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/data-library.yaml b/topics/statistics/tutorials/multiGSEA-tutorial/data-library.yaml new file mode 100644 index 00000000000000..ba935f5c1131de --- /dev/null +++ b/topics/statistics/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 diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/faqs/index.md b/topics/statistics/tutorials/multiGSEA-tutorial/faqs/index.md new file mode 100644 index 00000000000000..9ce3fe4fce824b --- /dev/null +++ b/topics/statistics/tutorials/multiGSEA-tutorial/faqs/index.md @@ -0,0 +1,3 @@ +--- +layout: faq-page +--- diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.bib b/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.bib new file mode 100644 index 00000000000000..da3de5ec9b1cea --- /dev/null +++ b/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.bib @@ -0,0 +1,26 @@ + +# 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} +} + + diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.md b/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.md new file mode 100644 index 00000000000000..d3605601126a71 --- /dev/null +++ b/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.md @@ -0,0 +1,104 @@ +--- +layout: tutorial_hands_on + +title: Using MultiGSEA +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. +contributors: +- stehling + + +--- + + +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 Quiros et al. (Quirós et al. 2017). 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 cover: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +# Preparing the Data + +To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the file type TSV . These datasets contain columns for feature Symbol, logFC pValue and adj.p-values. We'll use example data provided on Zenodo. + +## Get data + +> ### Data Upload +> +> 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: +> - **transcriptomics.tsv** +> - **proteomics.tsv** +> - **metabolomics.tsv** +> + +# Running MultiGSEA + +In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrichment on the uploaded datasets. + +>## Selecting parameters +> Task description +> +> 1. Select the tool {% tool [multiGSEA](toolshed.g2.bx.psu.edu/repos/iuc/multigsea/multigsea/1.12.0+galaxy0) %} in Galaxy. +> 2. Configure the input parameters as follows: +> - *"Select transcriptomics data"*: `Enabled` +> - {% icon param-file %} *"Transcriptomics data"*: `Transcriptomics` +> - *"Select proteomics data"*: `Enabled` +> - {% icon param-file %} *"Proteomics data"*: `Proteomics` +> - *"Select metabolomics data"*: `Enabled` +> - {% icon param-file %} *"Metabolomics data"*: `Metabolomics` +> 3. You can also choose the Gene ID format for every data set. In this tutorial we will use the preset "SYMBOL" for transcriptomics and proteomics. For metabolomics we use HMDB. +> 4. Select in **Supported organisms** the organism of which the data is about. In our case we select `Homo sapiens (Human)`. +> 5. **Pathway databases**: Select relevant databases. For the tutorial we choose `KEGG` +> 6. **Combine p-values method**: Choose a method (here `Stouffer` for balanced weighting). +> 7. **P-value correction method** (for controlling false discovery rate): Choose `Holm`. +> 8. Click on `Run Tool` +> +> {% snippet faqs/galaxy/tools_run.md %} +{: .hands_on} + +--- + + +> +> +> 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? +> +> > +> > +> > 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. 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\ No newline at end of file From 3b174482d08766461eb1bceeed352eb08c130e5a Mon Sep 17 00:00:00 2001 From: Thorben Date: Sat, 7 Dec 2024 14:55:07 +0100 Subject: [PATCH 2/8] Switched topic to proteomics->multiomics, smaller changes according to comments --- CONTRIBUTORS.yaml | 4 ++++ metadata/lang/multiomics.yml | 14 ++++++++++++++ metadata/multi-omics.yaml | 13 +++++++++++++ .../multiGSEA-tutorial/data-library.yaml | 0 .../multiGSEA-tutorial/faqs/index.md | 0 .../tutorials/multiGSEA-tutorial/tutorial.bib | 0 .../tutorials/multiGSEA-tutorial/tutorial.md | 19 +++++++++++++++---- .../multiGSEA-tutorial/workflows/index.md | 0 .../workflows/main_workflow.ga | 0 topics/statistics/index.md | 2 +- topics/statistics/metadata.yaml | 2 +- 11 files changed, 48 insertions(+), 6 deletions(-) create mode 100644 metadata/lang/multiomics.yml create mode 100644 metadata/multi-omics.yaml rename topics/{statistics => proteomics}/tutorials/multiGSEA-tutorial/data-library.yaml (100%) rename topics/{statistics => proteomics}/tutorials/multiGSEA-tutorial/faqs/index.md (100%) rename topics/{statistics => proteomics}/tutorials/multiGSEA-tutorial/tutorial.bib (100%) rename topics/{statistics => proteomics}/tutorials/multiGSEA-tutorial/tutorial.md (90%) rename topics/{statistics => proteomics}/tutorials/multiGSEA-tutorial/workflows/index.md (100%) rename topics/{statistics => proteomics}/tutorials/multiGSEA-tutorial/workflows/main_workflow.ga (100%) diff --git a/CONTRIBUTORS.yaml b/CONTRIBUTORS.yaml index 56beb6993b621d..776bc96b347f9a 100644 --- a/CONTRIBUTORS.yaml +++ b/CONTRIBUTORS.yaml @@ -2710,3 +2710,7 @@ tbrown91: email: brown@izw-berlin.de orcid: 0000-0001-8293-4816 joined: 2024-08 + +tStehling: + name: Thorben Stehling + joined: 2024-11 diff --git a/metadata/lang/multiomics.yml b/metadata/lang/multiomics.yml new file mode 100644 index 00000000000000..82cba865007826 --- /dev/null +++ b/metadata/lang/multiomics.yml @@ -0,0 +1,14 @@ +--- +name: "multiomics" +type: "use" +topic_type: field +title: "Multiomics" +summary: "Set of tutorials for tools that process or analyse multiple omics." + +tag_based: true + +gitter: galaxy-multiomics:matrix.org + +editorial_board: + - tStehling + \ No newline at end of file diff --git a/metadata/multi-omics.yaml b/metadata/multi-omics.yaml new file mode 100644 index 00000000000000..a08c7767cedc8f --- /dev/null +++ b/metadata/multi-omics.yaml @@ -0,0 +1,13 @@ +--- +name: "multi-omics" +type: "use" +topic_type: field +title: "Multiomics" +summary: "Set of tutorials for tools which process or analyse multiomics data." + +tag_based: true + +gitter: galaxy-multi-omics:matrix.org + +editorial_board: + - tStehling diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/data-library.yaml b/topics/proteomics/tutorials/multiGSEA-tutorial/data-library.yaml similarity index 100% rename from topics/statistics/tutorials/multiGSEA-tutorial/data-library.yaml rename to topics/proteomics/tutorials/multiGSEA-tutorial/data-library.yaml diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/faqs/index.md b/topics/proteomics/tutorials/multiGSEA-tutorial/faqs/index.md similarity index 100% rename from topics/statistics/tutorials/multiGSEA-tutorial/faqs/index.md rename to topics/proteomics/tutorials/multiGSEA-tutorial/faqs/index.md diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.bib b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.bib similarity index 100% rename from topics/statistics/tutorials/multiGSEA-tutorial/tutorial.bib rename to topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.bib diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.md b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md similarity index 90% rename from topics/statistics/tutorials/multiGSEA-tutorial/tutorial.md rename to topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md index d3605601126a71..3b514f015867e0 100644 --- a/topics/statistics/tutorials/multiGSEA-tutorial/tutorial.md +++ b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md @@ -2,6 +2,12 @@ layout: tutorial_hands_on title: 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? @@ -13,7 +19,7 @@ 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. contributors: -- stehling +- tStehling --- @@ -43,6 +49,7 @@ To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the > ### Data Upload > +> Getting datasets > 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: @@ -50,6 +57,13 @@ To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the > - **proteomics.tsv** > - **metabolomics.tsv** > +> URLs of the files +> - **transcriptomics.tsv** https://zenodo.org/api/records/14216972/files/transcriptome.tsv/content +> - **proteomics.tsv** https://zenodo.org/api/records/14216972/files/proteome.tsv/content +> - **metabolomics.tsv** https://zenodo.org/api/records/14216972/files/metabolome.tsv/content +> +{: .comment} +{: .hands_on} # Running MultiGSEA @@ -73,11 +87,8 @@ In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrich > 7. **P-value correction method** (for controlling false discovery rate): Choose `Holm`. > 8. Click on `Run Tool` > -> {% snippet faqs/galaxy/tools_run.md %} {: .hands_on} ---- - > > diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/workflows/index.md b/topics/proteomics/tutorials/multiGSEA-tutorial/workflows/index.md similarity index 100% rename from topics/statistics/tutorials/multiGSEA-tutorial/workflows/index.md rename to topics/proteomics/tutorials/multiGSEA-tutorial/workflows/index.md diff --git a/topics/statistics/tutorials/multiGSEA-tutorial/workflows/main_workflow.ga b/topics/proteomics/tutorials/multiGSEA-tutorial/workflows/main_workflow.ga similarity index 100% rename from topics/statistics/tutorials/multiGSEA-tutorial/workflows/main_workflow.ga rename to topics/proteomics/tutorials/multiGSEA-tutorial/workflows/main_workflow.ga diff --git a/topics/statistics/index.md b/topics/statistics/index.md index 0e1ac2bc902525..7a621862e709dd 100644 --- a/topics/statistics/index.md +++ b/topics/statistics/index.md @@ -1,4 +1,4 @@ --- layout: topic topic_name: 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zBn@DhotbT__L2JFnGe6z7-ibU6Xu=R6%V7uRCi3L;oHMq$7Zj+sfpwh0p)V z>J&bH(VATS=cdl+ip4e^aldJ|u-Q+MtoMdYZv@a4uZVDr`1{EwdrgidNI+L+=6#3JWe$?H*JU7ocL7>jq;foSfBew&_VA H00000#{#5u literal 0 HcmV?d00001 diff --git a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md index 3b514f015867e0..9dc500a0ef8cf0 100644 --- a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md +++ b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md @@ -30,6 +30,7 @@ The multiGSEA package was designed to run a robust GSEA-based 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 Quiros et al. (Quirós et al. 2017). 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. > @@ -43,7 +44,7 @@ This tutorial covers a simple example workflow illustrating how the multiGSEA pa # Preparing the Data -To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the file type TSV . These datasets contain columns for feature Symbol, logFC pValue and adj.p-values. We'll use example data provided on Zenodo. +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 @@ -58,7 +59,7 @@ To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the > - **metabolomics.tsv** > > URLs of the files -> - **transcriptomics.tsv** https://zenodo.org/api/records/14216972/files/transcriptome.tsv/content +> - **transcriptomics.tsv** https://zenodo.org/api/records/14216972/files/transcriptome.tsv/content > - **proteomics.tsv** https://zenodo.org/api/records/14216972/files/proteome.tsv/content > - **metabolomics.tsv** https://zenodo.org/api/records/14216972/files/metabolome.tsv/content > @@ -82,9 +83,10 @@ In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrich > - {% icon param-file %} *"Metabolomics data"*: `Metabolomics` > 3. You can also choose the Gene ID format for every data set. In this tutorial we will use the preset "SYMBOL" for transcriptomics and proteomics. For metabolomics we use HMDB. > 4. Select in **Supported organisms** the organism of which the data is about. In our case we select `Homo sapiens (Human)`. -> 5. **Pathway databases**: Select relevant databases. For the tutorial we choose `KEGG` -> 6. **Combine p-values method**: Choose a method (here `Stouffer` for balanced weighting). -> 7. **P-value correction method** (for controlling false discovery rate): Choose `Holm`. +> 5. **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 `KEGG` +> 6. **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, Loughin 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") +> 7. **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). > 8. Click on `Run Tool` > {: .hands_on} From 3376ceccfdb8b559000b9737b6c7ba9ea6532093 Mon Sep 17 00:00:00 2001 From: Saskia Hiltemann Date: Tue, 14 Jan 2025 16:17:18 +0100 Subject: [PATCH 4/8] remove obsolete files --- metadata/lang/multiomics.yml | 14 -------------- metadata/multi-omics.yaml | 13 ------------- 2 files changed, 27 deletions(-) delete mode 100644 metadata/lang/multiomics.yml delete mode 100644 metadata/multi-omics.yaml diff --git a/metadata/lang/multiomics.yml b/metadata/lang/multiomics.yml deleted file mode 100644 index 82cba865007826..00000000000000 --- a/metadata/lang/multiomics.yml +++ /dev/null @@ -1,14 +0,0 @@ ---- -name: "multiomics" -type: "use" -topic_type: field -title: "Multiomics" -summary: "Set of tutorials for tools that process or analyse multiple omics." - -tag_based: true - -gitter: galaxy-multiomics:matrix.org - -editorial_board: - - tStehling - \ No newline at end of file diff --git a/metadata/multi-omics.yaml b/metadata/multi-omics.yaml deleted file mode 100644 index a08c7767cedc8f..00000000000000 --- a/metadata/multi-omics.yaml +++ /dev/null @@ -1,13 +0,0 @@ ---- -name: "multi-omics" -type: "use" -topic_type: field -title: "Multiomics" -summary: "Set of tutorials for tools which process or analyse multiomics data." - -tag_based: true - -gitter: galaxy-multi-omics:matrix.org - -editorial_board: - - tStehling From 9554a799c513c8b827dee41f1b58aef1433d696d Mon Sep 17 00:00:00 2001 From: Saskia Hiltemann Date: Tue, 14 Jan 2025 16:26:22 +0100 Subject: [PATCH 5/8] fix broken boxes and tweak formatting of zenodo links --- .../tutorials/multiGSEA-tutorial/tutorial.md | 37 +++++++++---------- 1 file changed, 17 insertions(+), 20 deletions(-) diff --git a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md index 9dc500a0ef8cf0..2012b25c6bfb83 100644 --- a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md +++ b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md @@ -3,7 +3,7 @@ layout: tutorial_hands_on title: Using MultiGSEA subtopic: multi-omics -tags: +tags: - multi-omics - transcriptomics - proteomics @@ -48,30 +48,27 @@ To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the ## Get data -> ### Data Upload -> -> Getting datasets +### Data Upload + +> Getting datasets > 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: -> - **transcriptomics.tsv** -> - **proteomics.tsv** -> - **metabolomics.tsv** > -> URLs of the files -> - **transcriptomics.tsv** https://zenodo.org/api/records/14216972/files/transcriptome.tsv/content -> - **proteomics.tsv** https://zenodo.org/api/records/14216972/files/proteome.tsv/content -> - **metabolomics.tsv** https://zenodo.org/api/records/14216972/files/metabolome.tsv/content -> -{: .comment} +> {% snippet faqs/galaxy/histories_create_new.md %} +> +> 2. Import the datasets from [Zenodo]({{ page.zenodo_link }}) into your Galaxy instance: +> ``` +> https://zenodo.org/api/records/14216972/files/transcriptome.tsv +> https://zenodo.org/api/records/14216972/files/proteome.tsv +> https://zenodo.org/api/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. ->## Selecting parameters -> Task description +> Task description > > 1. Select the tool {% tool [multiGSEA](toolshed.g2.bx.psu.edu/repos/iuc/multigsea/multigsea/1.12.0+galaxy0) %} in Galaxy. > 2. Configure the input parameters as follows: @@ -85,7 +82,7 @@ In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrich > 4. Select in **Supported organisms** the organism of which the data is about. In our case we select `Homo sapiens (Human)`. > 5. **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 `KEGG` > 6. **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, Loughin 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](../../images/p-value.png "P-value methods") > 7. **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). > 8. Click on `Run Tool` > @@ -109,9 +106,9 @@ In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrich {: .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. \ No newline at end of file +> - Combine p-values from different omics layers to derive a unified perspective on pathway enrichment. From 3eec11310ee7da72010fe3099d7807afeb2991b5 Mon Sep 17 00:00:00 2001 From: Saskia Hiltemann Date: Tue, 14 Jan 2025 16:44:28 +0100 Subject: [PATCH 6/8] update formatting to GTN best practices --- .../tutorials/multiGSEA-tutorial/tutorial.md | 31 ++++++++++++------- 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md index 2012b25c6bfb83..f9d69d273cf44f 100644 --- a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md +++ b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md @@ -57,9 +57,9 @@ To perform pathway enrichment with MultiGSEA, you'll need omics datasets in the > > 2. Import the datasets from [Zenodo]({{ page.zenodo_link }}) into your Galaxy instance: > ``` -> https://zenodo.org/api/records/14216972/files/transcriptome.tsv -> https://zenodo.org/api/records/14216972/files/proteome.tsv -> https://zenodo.org/api/records/14216972/files/metabolome.tsv +> 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} @@ -70,25 +70,32 @@ In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrich > Task description > -> 1. Select the tool {% tool [multiGSEA](toolshed.g2.bx.psu.edu/repos/iuc/multigsea/multigsea/1.12.0+galaxy0) %} in Galaxy. -> 2. Configure the input parameters as follows: +> 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` -> 3. You can also choose the Gene ID format for every data set. In this tutorial we will use the preset "SYMBOL" for transcriptomics and proteomics. For metabolomics we use HMDB. -> 4. Select in **Supported organisms** the organism of which the data is about. In our case we select `Homo sapiens (Human)`. -> 5. **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 `KEGG` -> 6. **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, Loughin 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") -> 7. **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). -> 8. Click on `Run Tool` +> - {% icon param-select %} *"Metabolite ID format"*: `HMDB` +> - *"Supported organisms"*: `Homo sapiens (Human)`. +> - *"Pathway databases"*: `KEGG` +> - *"Combine p-values method"*: `Stouffer` +> - *"P-value correction method"*: `BH` +> +> > About the parameters +> > - **Pathway databases**: `KEGG`Databases often contain their own format in which pathway definitions are provided. So you can select a relevant > > database. For the tutorial we choose `KEGG` +> > - **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, Loughin 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} + > > > 1. What file format is required for the input data in MultiGSEA? From 781707852a256f0c413005706cec8c2c71a22734 Mon Sep 17 00:00:00 2001 From: Saskia Hiltemann Date: Tue, 14 Jan 2025 17:20:16 +0100 Subject: [PATCH 7/8] fix linting error --- .../tutorials/multiGSEA-tutorial/tutorial.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md index f9d69d273cf44f..8d36a3aaa38271 100644 --- a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md +++ b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md @@ -112,10 +112,11 @@ In this step, you'll use the MultiGSEA tool to perform GSEA-based pathway enrich > {: .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. + +# 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. From ab9b9d46bf5d013f922ec67efe087e10c9b98fbf Mon Sep 17 00:00:00 2001 From: Thorben Date: Wed, 15 Jan 2025 11:00:33 +0100 Subject: [PATCH 8/8] updated authorship and title --- topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md index 8d36a3aaa38271..d5f4b170d738f8 100644 --- a/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md +++ b/topics/proteomics/tutorials/multiGSEA-tutorial/tutorial.md @@ -1,7 +1,7 @@ --- layout: tutorial_hands_on -title: Using MultiGSEA +title: Multiomics data analysis using MultiGSEA subtopic: multi-omics tags: - multi-omics @@ -18,8 +18,9 @@ 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. -contributors: -- tStehling +contributions: + authorship: + - tStehling ---