Created tutorial for MultiGSEA #5567
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I am unsure if the tutorial should be part of the |
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We are fine to move it to any other category, but none seems to fit yet. Me may create a |
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@bernt-matthias the transcriptomics topic has a "multi-omics" subsection, could we add it there for now? |
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@bernt-matthias we can then make "synthetic topic" by adding a "multi-omics" tag to all tutorials analyzing multi-omics data, and define the topic similar to the plants topic: https://github.com/galaxyproject/training-material/blob/main/metadata/plants.yaml Then the topic will be shown on the main page, since people interested in this tutorial may not naturally go to the proteomics topic, but tutorials themselves can live in multiple topics (as they do now) What do you think? |
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This sounds good to me. @tStehling could you move it? @shiltemann do you have some links for @tStehling on how to assign tags / assign the tutorial to the sub-topic? |
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Thanks a lot for your contribution @tStehling! I've left some comments below, but please let me know if anything is unclear, or if you would like some help doing it :)
absolutely, @tStehling : In the proteomics topics, the subsection id is And then simply add a tag of the same name. I will use this to create the "multiomics" synthetic topic later. To add a tag, add the following to the metadata of your tutorial (and feel free to add more tags in this list as you see fit) tags are shown as follows under the tutorial name , and can help users identify interesting tutorials
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@shiltemann thank you for the effort. |
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Very good @tStehling! Just a few mini comments, otherwise good to go from my side.
metadata/multi-omics.yaml
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| tag_based: true | ||
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| gitter: galaxy-multi-omics:matrix.org |
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This is different from the one above, and I think both do not exist ...
I'm also not sure if we should really create a separate room for it or if we should reuse one of the other rooms
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Thanks @tStehling! I pushed some formatting changes and left a couple small comments below
| > 8. Click on `Run Tool` | ||
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| {: .hands_on} | ||
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would it be useful to discuss the output of the tool here? again both on technical level (what is the format, what do the contents mean?) and biological (what can we learn from the output)
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Thanks for the comments. I will discuss with @tStehling tomorrow. |
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| # Preparing the Data | ||
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| 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. |
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Maybe Sebastian can tell us a few xrefs which methods can give the needed values for Transcriptomics, Metabolomics, and Proteomics.
| @@ -32,7 +32,9 @@ Pathway definitions can be downloaded from up to eight different pathway databas | |||
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| 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. | |||
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| 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. | |
| 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. |
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| > > <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, 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. |
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| > > - **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. | |
| > > - **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. |
| For more background information you can read following articles: | ||
| - Multiomics analysis by Quiros {% cite quiros2017multi %} | ||
| - Methods for combining p-values by Loughin {% cite LOUGHIN2004467 %} |
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| For more background information you can read following articles: | |
| - Multiomics analysis by Quiros {% cite quiros2017multi %} | |
| - Methods for combining p-values by Loughin {% cite LOUGHIN2004467 %} |
Added a tutorial for MultiGSEA tool.