From 03a7cf167ed69f6f3c57480dbc6bf9d537ffb5a4 Mon Sep 17 00:00:00 2001 From: timonschlegel Date: Thu, 20 Jun 2024 10:25:25 +0200 Subject: [PATCH 01/12] Add files via upload --- .../faqs/index.md | 3 + .../tutorial.bib | 42 + .../tutorial.md | 934 ++++++++++++++++++ .../workflows/index.md | 3 + .../workflows/main_workflow.ga | 1 + 5 files changed, 983 insertions(+) create mode 100644 topics/single-cell/tutorials/scatac-batch-correction-snapatac2/faqs/index.md create mode 100644 topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib create mode 100644 topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md create mode 100644 topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/index.md create mode 100644 topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/faqs/index.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/faqs/index.md new file mode 100644 index 00000000000000..9ce3fe4fce824b --- /dev/null +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/faqs/index.md @@ -0,0 +1,3 @@ +--- +layout: faq-page +--- diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib new file mode 100644 index 00000000000000..9206b0b6e4cae4 --- /dev/null +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib @@ -0,0 +1,42 @@ + +# 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{Batut2018, + doi = {10.1016/j.cels.2018.05.012}, + url = {https://doi.org/10.1016/j.cels.2018.05.012}, + year = {2018}, + month = jun, + publisher = {Elsevier {BV}}, + volume = {6}, + number = {6}, + pages = {752--758.e1}, + author = {B{\'{e}}r{\'{e}}nice Batut and Saskia Hiltemann and Andrea Bagnacani and Dannon Baker and Vivek Bhardwaj and + Clemens Blank and Anthony Bretaudeau and Loraine Brillet-Gu{\'{e}}guen and Martin {\v{C}}ech and John Chilton + and Dave Clements and Olivia Doppelt-Azeroual and Anika Erxleben and Mallory Ann Freeberg and Simon Gladman and + Youri Hoogstrate and Hans-Rudolf Hotz and Torsten Houwaart and Pratik Jagtap and Delphine Larivi{\`{e}}re and + Gildas Le Corguill{\'{e}} and Thomas Manke and Fabien Mareuil and Fidel Ram{\'{i}}rez and Devon Ryan and + Florian Christoph Sigloch and Nicola Soranzo and Joachim Wolff and Pavankumar Videm and Markus Wolfien and + Aisanjiang Wubuli and Dilmurat Yusuf and James Taylor and Rolf Backofen and Anton Nekrutenko and Bj\"{o}rn Gr\"{u}ning}, + title = {Community-Driven Data Analysis Training for Biology}, + journal = {Cell Systems} +} + +@online{gtn-website, + author = {GTN community}, + title = {GTN Training Materials: Collection of tutorials developed and maintained by the worldwide Galaxy community}, + url = {https://training.galaxyproject.org}, + urldate = {2021-03-24} +} diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md new file mode 100644 index 00000000000000..a26b71c8334b05 --- /dev/null +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -0,0 +1,934 @@ +--- +layout: tutorial_hands_on + +title: Multi-sample batch correction with Harmony and SnapATAC2 +zenodo_link: '' +questions: +- Which biological questions are addressed by the tutorial? +- Which bioinformatics techniques are important to know for this type of data? +objectives: +- The learning objectives are the goals of the tutorial +- They will be informed by your audience and will communicate to them and to yourself + what you should focus on during the course +- They are single sentences describing what a learner should be able to do once they + have completed the tutorial +- You can use Bloom's Taxonomy to write effective learning objectives +time_estimation: 3H +key_points: +- The take-home messages +- They will appear at the end of the tutorial +contributors: +- contributor1 +- contributor2 + +--- + + +# Introduction + + + +General introduction about the topic and then an introduction of the +tutorial (the questions and the objectives). It is nice also to have a +scheme to sum up the pipeline used during the tutorial. The idea is to +give to trainees insight into the content of the tutorial and the (theoretical +and technical) key concepts they will learn. + +You may want to cite some publications; this can be done by adding citations to the +bibliography file (`tutorial.bib` file next to your `tutorial.md` file). These citations +must be in bibtex format. If you have the DOI for the paper you wish to cite, you can +get the corresponding bibtex entry using [doi2bib.org](https://doi2bib.org). + +With the example you will find in the `tutorial.bib` file, you can add a citation to +this article here in your tutorial like this: +{% raw %} `{% cite Batut2018 %}`{% endraw %}. +This will be rendered like this: {% cite Batut2018 %}, and links to a +[bibliography section](#bibliography) which will automatically be created at the end of the +tutorial. + + +**Please follow our +[tutorial to learn how to fill the Markdown]({{ site.baseurl }}/topics/contributing/tutorials/create-new-tutorial-content/tutorial.html)** + +> +> +> In this tutorial, we will cover: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +# Title for your first section + +Give some background about what the trainees will be doing in the section. +Remember that many people reading your materials will likely be novices, +so make sure to explain all the relevant concepts. + +## Title for a subsection +Section and subsection titles will be displayed in the tutorial index on the left side of +the page, so try to make them informative and concise! + +# Hands-on Sections +Below are a series of hand-on boxes, one for each tool in your workflow file. +Often you may wish to combine several boxes into one or make other adjustments such +as breaking the tutorial into sections, we encourage you to make such changes as you +see fit, this is just a starting point :) + +Anywhere you find the word "***TODO***", there is something that needs to be changed +depending on the specifics of your tutorial. + +have fun! + +## Get data + +> Data Upload +> +> 1. Create a new history for this tutorial +> 2. Import the files from [Zenodo]({{ page.zenodo_link }}) or from +> the shared data library (`GTN - Material` -> `{{ page.topic_name }}` +> -> `{{ page.title }}`): +> +> ``` +> +> ``` +> ***TODO***: *Add the files by the ones on Zenodo here (if not added)* +> +> ***TODO***: *Remove the useless files (if added)* +> +> {% 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} + +# Title of the section usually corresponding to a big step in the analysis + +It comes first a description of the step: some background and some theory. +Some image can be added there to support the theory explanation: + +![Alternative text](../../images/image_name "Legend of the image") + +The idea is to keep the theory description before quite simple to focus more on the practical part. + +***TODO***: *Consider adding a detail box to expand the theory* + +> More details about the theory +> +> But to describe more details, it is possible to use the detail boxes which are expandable +> +{: .details} + +A big step can have several subsections or sub steps: + + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Import data fragment files and compute basic QC metrics, using 'pp.import_data'` +> - {% icon param-collection %} *"Fragment file, optionally compressed with gzip or zstd"*: `output` (Input dataset collection) +> - {% icon param-file %} *"A tabular file containing chromosome names and sizes"*: `output` (Input dataset) +> - *"Number of unique fragments threshold used to filter cells"*: `1000` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Compute the TSS enrichment score (TSSe) for each cell, using 'metrics.tsse'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - {% icon param-file %} *"GTF/GFF file containing the gene annotation"*: `output` (Input dataset) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Filter cell outliers based on counts and numbers of genes expressed, using 'pp.filter_cells'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - *"Minimum TSS enrichemnt score required for a cell to pass filtering"*: `7.0` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Generate cell by bin count matrix, using 'pp.add_tile_matrix'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - *"The size of consecutive genomic regions used to record the counts"*: `5000` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - *"Number of features to keep"*: `50000` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Compute probability of being a doublet using the scrublet algorithm, using 'pp.scrublet'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Remove doublets according to the doublet probability or doublet score, using 'pp.filter_doublets'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **Extract element identifiers** + +> Task description +> +> 1. {% tool [Extract element identifiers](toolshed.g2.bx.psu.edu/repos/iuc/collection_element_identifiers/collection_element_identifiers/0.0.2) %} with the following parameters: +> - {% icon param-file %} *"Dataset collection"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **Extract dataset** + +> Task description +> +> 1. {% tool [Extract dataset](__EXTRACT_DATASET__) %} with the following parameters: +> - {% icon param-file %} *"Input List"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - *"How should a dataset be selected?"*: `The first dataset` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **Select first** + +> Task description +> +> 1. {% tool [Select first](Show beginning1) %} with the following parameters: +> - *"Select first"*: `1` +> - {% icon param-file %} *"from"*: `output` (output of **Extract element identifiers** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **Filter collection** + +> Task description +> +> 1. {% tool [Filter collection](__FILTER_FROM_FILE__) %} with the following parameters: +> - {% icon param-file %} *"Input Collection"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - *"How should the elements to remove be determined?"*: `Remove if identifiers are PRESENT in file` +> - {% icon param-file %} *"Filter out identifiers present in"*: `out_file1` (output of **Select first** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **Manipulate AnnData** + +> Task description +> +> 1. {% tool [Manipulate AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.10.3+galaxy0) %} with the following parameters: +> - {% icon param-file %} *"Annotated data matrix"*: `output` (output of **Extract dataset** {% icon tool %}) +> - *"Function to manipulate the object"*: `Concatenate along the observations axis` +> - {% icon param-file %} *"Annotated data matrix to add"*: `output_filtered` (output of **Filter collection** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata` (output of **Manipulate AnnData** {% icon tool %}) +> - *"Number of features to keep"*: `50000` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Clustering** + +> Task description +> +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> - *"Dimension reduction and Clustering"*: `Perform dimension reduction using Laplacian Eigenmap, using 'tl.spectral'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Clustering** + +> Task description +> +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Plotting** + +> Task description +> +> 1. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - *"Color"*: `batch` +> - *"Height of the plot"*: `500` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Preprocessing** + +> Task description +> +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Use harmonypy to integrate different experiments,using 'pp.harmony'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Clustering** + +> Task description +> +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - *"Use the indicated representation in `.obsm`"*: `X_spectral_harmony` +> - *"`adata.obs` key under which t add cluster labels"*: `umap_harmony` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Plotting** + +> Task description +> +> 1. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - *"Color"*: `batch` +> - *"Use the indicated representation in .obsm"*: `X_umap_harmony` +> - *"Height of the plot"*: `500` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Clustering** + +> Task description +> +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> - *"Dimension reduction and Clustering"*: `Compute a neighborhood graph of observations, using 'pp.knn'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - *"The key for the matrix"*: `X_spectral_harmony` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Clustering** + +> Task description +> +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> - *"Dimension reduction and Clustering"*: `Cluster cells into subgroups, using 'tl.leiden'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + +## Sub-step with **SnapATAC2 Plotting** + +> Task description +> +> 1. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - *"Color"*: `leiden` +> - *"Use the indicated representation in .obsm"*: `X_umap_harmony` +> - *"Height of the plot"*: `500` +> +> ***TODO***: *Check parameter descriptions* +> +> ***TODO***: *Consider adding a comment or tip box* +> +> > short description +> > +> > A comment about the tool or something else. This box can also be in the main text +> {: .comment} +> +{: .hands_on} + +***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* + +> +> +> 1. Question1? +> 2. Question2? +> +> > +> > +> > 1. Answer for question1 +> > 2. Answer for question2 +> > +> {: .solution} +> +{: .question} + + +## Re-arrange + +To create the template, each step of the workflow had its own subsection. + +***TODO***: *Re-arrange the generated subsections into sections or other subsections. +Consider merging some hands-on boxes to have a meaningful flow of the analyses* + +# Conclusion + +Sum up the tutorial and the key takeaways here. 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"workflow_outputs": []}}, "tags": [], "uuid": "17d12481-2345-4f95-bd53-d2b91565223e", "version": 10} \ No newline at end of file From e3fa450a44341d0b570f710267c1d563ad59ad7f Mon Sep 17 00:00:00 2001 From: timonschlegel Date: Mon, 24 Jun 2024 15:28:40 +0000 Subject: [PATCH 02/12] Metadata and data input --- CONTRIBUTORS.yaml | 4 + .../tutorial.bib | 29 +++- .../tutorial.md | 137 ++++++++---------- 3 files changed, 90 insertions(+), 80 deletions(-) diff --git a/CONTRIBUTORS.yaml b/CONTRIBUTORS.yaml index a623e6a580fde7..c64ce01e8ff2b0 100644 --- a/CONTRIBUTORS.yaml +++ b/CONTRIBUTORS.yaml @@ -2160,6 +2160,10 @@ thomaswollmann: - deNBI - elixir-europe +timonschlegel: + name: Timon Schlegel + joined: 2024-05 + timothygriffin: name: Timothy J. Griffin email: tgriffin@umn.edu diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib index 9206b0b6e4cae4..3dd810474ee008 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib @@ -33,7 +33,34 @@ @article{Batut2018 title = {Community-Driven Data Analysis Training for Biology}, journal = {Cell Systems} } - +@article{Korsunsky2019, + title = {Fast, sensitive and accurate integration of single-cell data with Harmony}, + volume = {16}, + ISSN = {1548-7105}, + url = {http://dx.doi.org/10.1038/s41592-019-0619-0}, + DOI = {10.1038/s41592-019-0619-0}, + number = {12}, + journal = {Nature Methods}, + publisher = {Springer Science and Business Media LLC}, + author = {Korsunsky, Ilya and Millard, Nghia and Fan, Jean and Slowikowski, Kamil and Zhang, Fan and Wei, Kevin and Baglaenko, Yuriy and Brenner, Michael and Loh, Po-ru and Raychaudhuri, Soumya}, + year = {2019}, + month = nov, + pages = {1289–1296} +} +@article{Zhang2024, + title = {A fast, scalable and versatile tool for analysis of single-cell omics data}, + volume = {21}, + ISSN = {1548-7105}, + url = {http://dx.doi.org/10.1038/s41592-023-02139-9}, + DOI = {10.1038/s41592-023-02139-9}, + number = {2}, + journal = {Nature Methods}, + publisher = {Springer Science and Business Media LLC}, + author = {Zhang, Kai and Zemke, Nathan R. and Armand, Ethan J. and Ren, Bing}, + year = {2024}, + month = jan, + pages = {217–227} +} @online{gtn-website, author = {GTN community}, title = {GTN Training Materials: Collection of tutorials developed and maintained by the worldwide Galaxy community}, diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index a26b71c8334b05..c7509160081675 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -2,24 +2,40 @@ layout: tutorial_hands_on title: Multi-sample batch correction with Harmony and SnapATAC2 +subtopic: scmultiomics +priority: 3 +level: Intermediate zenodo_link: '' questions: -- Which biological questions are addressed by the tutorial? -- Which bioinformatics techniques are important to know for this type of data? +- Why is batch correction important when analyzing data from multiple samples? +- How is batch correction performed on single cell ATAC-seq data? objectives: -- The learning objectives are the goals of the tutorial -- They will be informed by your audience and will communicate to them and to yourself - what you should focus on during the course -- They are single sentences describing what a learner should be able to do once they - have completed the tutorial -- You can use Bloom's Taxonomy to write effective learning objectives +- Perform batch correction on a collection of single cell ATAC-seq data +- Learn how Harmony integrates different samples time_estimation: 3H key_points: -- The take-home messages -- They will appear at the end of the tutorial +- Batch correction is important for integration of data from multiple experiments +- How harmony works +requirements: + - + type: "internal" + topic_name: single-cell + tutorials: + - scatac-preprocessing-tenx + - scatac-standard-processing-snapatac2 +tags: +- 10x +- epigenetics +abbreviations: + scATAC-seq: Single-cell Assay for Transposase-Accessible Chromatin using sequencing + QC: quality control + TSSe: transcription start site enrichment + TSS: transcription start sites + UMAP: Uniform Manifold Approximation and Projection contributors: -- contributor1 -- contributor2 +- timonschlegel +gitter: Galaxy-Training-Network/galaxy-single-cell + --- @@ -27,28 +43,23 @@ contributors: # Introduction +Performing biological experiments in replicates is one of the cornerstones of modern science. However, when integrating data from multiple single-cell sequencing experiments, technical confounders might impact the results. +To reduce technical confounders, such as different experimenters, experimental protocols, sequencing lanes or sequencing technologies, a batch correction might be beneficial. -General introduction about the topic and then an introduction of the -tutorial (the questions and the objectives). It is nice also to have a -scheme to sum up the pipeline used during the tutorial. The idea is to -give to trainees insight into the content of the tutorial and the (theoretical -and technical) key concepts they will learn. - -You may want to cite some publications; this can be done by adding citations to the -bibliography file (`tutorial.bib` file next to your `tutorial.md` file). These citations -must be in bibtex format. If you have the DOI for the paper you wish to cite, you can -get the corresponding bibtex entry using [doi2bib.org](https://doi2bib.org). +In this tutorial, we will perform batch correction on five datasets of {scATAC-seq} data with the algorithm *Harmony* ({% cite Korsunsky2019 %}) and the tool suite [**SnapATAC2**] (https://kzhang.org/SnapATAC2/version/2.5/index.html) ({% cite Zhang2024 %}). -With the example you will find in the `tutorial.bib` file, you can add a citation to -this article here in your tutorial like this: -{% raw %} `{% cite Batut2018 %}`{% endraw %}. -This will be rendered like this: {% cite Batut2018 %}, and links to a -[bibliography section](#bibliography) which will automatically be created at the end of the -tutorial. +{% snippet topics/single-cell/faqs/single_cell_omics.md %} +{% snippet faqs/galaxy/tutorial_mode.md %} -**Please follow our -[tutorial to learn how to fill the Markdown]({{ site.baseurl }}/topics/contributing/tutorials/create-new-tutorial-content/tutorial.html)** +> +> +> This tutorial is significantly based on ["Multi-sample Pipeline" tutorial from SnapATAC2](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). +> The data analysis is performed with the same tools shown in the tutorial [Single-cell ATAC-seq standard processing with SnapATAC2]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). +> - That tutorial also explains the steps of the ATAC-seq analysis with SnapATAC2 in more detail. +> - We recommend completing that tutorial before continuing with this one. +> +{: .comment} > > @@ -59,26 +70,9 @@ tutorial. > {: .agenda} -# Title for your first section - -Give some background about what the trainees will be doing in the section. -Remember that many people reading your materials will likely be novices, -so make sure to explain all the relevant concepts. - -## Title for a subsection -Section and subsection titles will be displayed in the tutorial index on the left side of -the page, so try to make them informative and concise! +# Data -# Hands-on Sections -Below are a series of hand-on boxes, one for each tool in your workflow file. -Often you may wish to combine several boxes into one or make other adjustments such -as breaking the tutorial into sections, we encourage you to make such changes as you -see fit, this is just a starting point :) - -Anywhere you find the word "***TODO***", there is something that needs to be changed -depending on the specifics of your tutorial. - -have fun! +The datasets for this tutorial are colon samples from multiple donors, provided by the [SnapATAC2 documentation](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). ## Get data @@ -90,50 +84,35 @@ have fun! > -> `{{ page.title }}`): > > ``` -> +> {{ page.zenodo_link }}/files/colon_multisample.tar +> {{ page.zenodo_link }}/files/chrom_sizes.txt +> {{ page.zenodo_link }}/files/gencode.v46.annotation.gtf.gz > ``` -> ***TODO***: *Add the files by the ones on Zenodo here (if not added)* -> -> ***TODO***: *Remove the useless files (if added)* > > {% 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" %} +> 3. Rename the datasets if necessary +> +> {% snippet faqs/galaxy/datasets_rename.md %} > -> 5. Add to each database a tag corresponding to ... +> 4. Check that the datatype of the `colon_multisample` files is set to `bed` > -> {% snippet faqs/galaxy/datasets_add_tag.md %} +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="datatypes" %} +> 5. Create a dataset collection with all `colon_multisample` datasets. +> +> {% snippet faqs/galaxy/collections_build_list.mdu name="Colon Multisample" %} > {: .hands_on} -# Title of the section usually corresponding to a big step in the analysis - -It comes first a description of the step: some background and some theory. -Some image can be added there to support the theory explanation: - -![Alternative text](../../images/image_name "Legend of the image") +# SnapATAC2 preprocessing and filtering -The idea is to keep the theory description before quite simple to focus more on the practical part. +With our data imported and the collection built, we can now begin the {scATAC-seq} data preprocessing with SnapATAC2. -***TODO***: *Consider adding a detail box to expand the theory* - -> More details about the theory -> -> But to describe more details, it is possible to use the detail boxes which are expandable -> -{: .details} +The first step is importing the datasets into an AnnData object with the tool *pp.import_data*. Next, the {TSSe} will be calculated. The {TSS} serves as a {QC} measurement to only filter droplets containing high-quality cells. -A big step can have several subsections or sub steps: - - -## Sub-step with **SnapATAC2 Preprocessing** - -> Task description +> Preprocessing and Filtering > > 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Import data fragment files and compute basic QC metrics, using 'pp.import_data'` From 63c81fb0ba4944ac080193ef31ecee3d06c33917 Mon Sep 17 00:00:00 2001 From: timonschlegel Date: Tue, 25 Jun 2024 09:56:50 +0000 Subject: [PATCH 03/12] preprocessing, filtering and concatenation --- .../tutorial.md | 474 ++++-------------- 1 file changed, 98 insertions(+), 376 deletions(-) diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index c7509160081675..d6d8ea1da8d312 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -93,16 +93,18 @@ The datasets for this tutorial are colon samples from multiple donors, provided > > {% snippet faqs/galaxy/datasets_import_from_data_library.md %} > -> 3. Rename the datasets if necessary -> +> 3. Rename the datasets +> - {% icon galaxy-pencil %} **Rename** the file `gencode.v46.annotation.gtf.gz` to `gene_annotation.gtf.gz` +> > {% snippet faqs/galaxy/datasets_rename.md %} > -> 4. Check that the datatype of the `colon_multisample` files is set to `bed` +> 4. Check that the datatypes of the `colon_multisample` files are set to `bed` > > {% snippet faqs/galaxy/datasets_change_datatype.md datatype="datatypes" %} +> > 5. Create a dataset collection with all `colon_multisample` datasets. > -> {% snippet faqs/galaxy/collections_build_list.mdu name="Colon Multisample" %} +> {% snippet faqs/galaxy/collections_build_list.mdu name="Colon Multisample Fragments" %} > {: .hands_on} @@ -110,436 +112,156 @@ The datasets for this tutorial are colon samples from multiple donors, provided With our data imported and the collection built, we can now begin the {scATAC-seq} data preprocessing with SnapATAC2. -The first step is importing the datasets into an AnnData object with the tool *pp.import_data*. Next, the {TSSe} will be calculated. The {TSS} serves as a {QC} measurement to only filter droplets containing high-quality cells. +The first step is importing the datasets into an AnnData object with the tool *pp.import_data*. Next, the {TSSe} will be calculated. The {TSS} serves as a {QC} measurement to selectively filter droplets containing high-quality cells. -> Preprocessing and Filtering +> Preprocessing and QC > > 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Import data fragment files and compute basic QC metrics, using 'pp.import_data'` -> - {% icon param-collection %} *"Fragment file, optionally compressed with gzip or zstd"*: `output` (Input dataset collection) -> - {% icon param-file %} *"A tabular file containing chromosome names and sizes"*: `output` (Input dataset) +> - {% icon param-collection %} *"Fragment file, optionally compressed with gzip or zstd"*: `Colon Multisample Fragments` (Input dataset collection) +> - {% icon param-file %} *"A tabular file containing chromosome names and sizes"*: `chrom_sizes.txt` (Input dataset) > - *"Number of unique fragments threshold used to filter cells"*: `1000` +> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Compute the TSS enrichment score (TSSe) for each cell, using 'metrics.tsse'` +> - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas` (dataset collection output of **pp.import_data** {% icon tool %}) +> - {% icon param-file %} *"GTF/GFF file containing the gene annotation"*: `gene_annotation` (Input dataset) > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} +> 2. Rename the generated collection to `Colon Multisample AnnDatas TSSe` or add the tag {% icon galaxy-tags %} `TSSe` to the collection: +> +> {% snippet faqs/galaxy/collections_add_tag.md %} > -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> +> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for plotting"*: `Plot the TSS enrichment vs. number of fragments density figure, using 'pl.tsse'` +> - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas TSSe` (output of **metrics.tsse** {% icon tool %}) +> 4. {% icon galaxy-eye %} Inspect a few exemplary `.png` outputs of the collection +> > -> 1. Question1? -> 2. Question2? +> ![TSSe plot against number of unique fragments]({% link path %}) +High-quality cells can be identified in the plot of {TSSe} scores against a number of unique fragments for each cell. > -> > +> > > > -> > 1. Answer for question1 -> > 2. Answer for question2 +> > 1. Where are high-quality cells located in a {TSSe} plot? +> > 2. Based on this plot, how should the filter be set? > > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Preprocessing** - -> Task description -> -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: -> - *"Method used for preprocessing"*: `Compute the TSS enrichment score (TSSe) for each cell, using 'metrics.tsse'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) -> - {% icon param-file %} *"GTF/GFF file containing the gene annotation"*: `output` (Input dataset) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} +> > > +> > > +> > > 1. The cells in the upper right are high-quality cells, enriched for {TSS}. Fragments in the lower left represent low-quality cells or empty droplets and should be filtered out. +> > > 2. Setting the minimum {TSSe} to 7.0 will filter out the lowest quality droplets without loosing too much data. +> > > +> > {: .solution} +> > +> {: .question} > {: .hands_on} -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* +## Filtering the count matrices -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} +The {TSSe} distributions show that the sample quality differs substantially between batches. In order to retain as much biological data as possible, we need to use a broad filter (f.ex. minimum TSSe = 7.0). -## Sub-step with **SnapATAC2 Preprocessing** +Low quality cells might also pass through the filter, but we can assume that these confounding factors will be corrected in later steps with *Harmony*. -> Task description +> Filtering > > 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Filter cell outliers based on counts and numbers of genes expressed, using 'pp.filter_cells'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas TSSe` (output of **metrics.tsse** {% icon tool %}) > - *"Minimum TSS enrichemnt score required for a cell to pass filtering"*: `7.0` > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Preprocessing** - -> Task description -> -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Generate cell by bin count matrix, using 'pp.add_tile_matrix'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas filtered` (output of **pp.filter_cells** {% icon tool %}) > - *"The size of consecutive genomic regions used to record the counts"*: `5000` > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Preprocessing** - -> Task description -> -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 3. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas tile_matrix` (output of **pp.add_tile_matrix** {% icon tool %}) > - *"Number of features to keep"*: `50000` > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description +> > Bin size and features > > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Preprocessing** - -> Task description -> -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> > - *pp.add_tile_matrix* divides the genome into a specified number of bins (f.ex. 5000). For each bin, the ATAC-seq reads of individual cells are checked if they are located that bin. This is counted as a feature and stored in `n_vars`. +> > - Increasing the bin size greatly reduces compute time at the cost of some biological data. +> > - *pp.select_features* checks the features identified by the previous tool and selects the features which are most accessible across all cells. +> > - The parameter *"Number of features to keep"* determines the upper limit of features which could be selected. +> > - Similarly too the *bin_size*, the Number of features to keep can also impact +> {: .details} +> +> 4. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Compute probability of being a doublet using the scrublet algorithm, using 'pp.scrublet'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas features` (output of **pp.select_features** {% icon tool %}) > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Preprocessing** - -> Task description -> -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 5. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Remove doublets according to the doublet probability or doublet score, using 'pp.filter_doublets'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} +> - {% icon param-file %} *"Annotated data matrix"*: `Colon Multisample AnnDatas scrublet` (output of **pp.scrublet** {% icon tool %}) > {: .hands_on} -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* +# Concatenate the Collection +Before we can continue with the analysis and batch correction. We need to extract the datasets out of the collection and merge them into a single AnnData object. -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} +## Extracting datasets from a collection -## Sub-step with **Extract element identifiers** - -> Task description -> -> 1. {% tool [Extract element identifiers](toolshed.g2.bx.psu.edu/repos/iuc/collection_element_identifiers/collection_element_identifiers/0.0.2) %} with the following parameters: -> - {% icon param-file %} *"Dataset collection"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} +First, we will extract the first dataset out of the collection using the Galaxy tool *Extract dataset*. -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* +Then, we will remove that first dataset from the collection through element identifiers. -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} +> +> Although we could manually remove the first dataset, the method shown here has the advantage that it can be implemented in a workflow. +{: .comment} -## Sub-step with **Extract dataset** -> Task description +> Extract datasets > -> 1. {% tool [Extract dataset](__EXTRACT_DATASET__) %} with the following parameters: -> - {% icon param-file %} *"Input List"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> 1. {% tool [Extract element from collection](__EXTRACT_DATASET__) %} with the following parameters: +> - {% icon param-collection %} *"Input List"*: `Colon Multisample AnnDatas filtered_doublets` (output of **pp.filter_doublets** {% icon tool %}) > - *"How should a dataset be selected?"*: `The first dataset` +> 2. {% tool [Extract element identifiers](toolshed.g2.bx.psu.edu/repos/iuc/collection_element_identifiers/collection_element_identifiers/0.0.2) %} with the following parameters: +> - {% icon param-collection %} *"Dataset collection"*: `Colon Multisample AnnDatas filtered_doublets` (output of **pp.filter_doublets** {% icon tool %}) > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **Select first** - -> Task description -> -> 1. {% tool [Select first](Show beginning1) %} with the following parameters: +> 3. {% tool [Select first](Show beginning1) %} with the following parameters: > - *"Select first"*: `1` -> - {% icon param-file %} *"from"*: `output` (output of **Extract element identifiers** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **Filter collection** - -> Task description -> -> 1. {% tool [Filter collection](__FILTER_FROM_FILE__) %} with the following parameters: -> - {% icon param-file %} *"Input Collection"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - {% icon param-file %} *"from"*: `Element identifiers` (output of **Extract element identifiers** {% icon tool %}) +> 4. {% tool [Filter collection](__FILTER_FROM_FILE__) %} with the following parameters: +> - {% icon param-collection %} *"Input Collection"*: `Colon Multisample AnnDatas filtered_doublets` (output of **pp.filter_doublets** {% icon tool %}) > - *"How should the elements to remove be determined?"*: `Remove if identifiers are PRESENT in file` -> - {% icon param-file %} *"Filter out identifiers present in"*: `out_file1` (output of **Select first** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} +> - {% icon param-file %} *"Filter out identifiers present in"*: `select_first` (output of **Select first** {% icon tool %}) +> 5. {% icon galaxy-pencil %} Rename the filtered collection `Colon Multisample 02-05` > {: .hands_on} -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **Manipulate AnnData** +## Concatenate AnnDatas -> Task description +> Concatenate > > 1. {% tool [Manipulate AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.10.3+galaxy0) %} with the following parameters: -> - {% icon param-file %} *"Annotated data matrix"*: `output` (output of **Extract dataset** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `colon_multisample_01` (output of **Extract dataset** {% icon tool %}) > - *"Function to manipulate the object"*: `Concatenate along the observations axis` -> - {% icon param-file %} *"Annotated data matrix to add"*: `output_filtered` (output of **Filter collection** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > +> - {% icon param-collection %} *"Annotated data matrix to add"*: `Colon Multisample 02-05` (output of **Filter collection** {% icon tool %}) +> - *"Join method"*: `Intersection of variables` +> - *"Key to add the batch annotation to obs"*: `batch` +> 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData` +> 3. {% icon galaxy-eye %} Inspect the general information of `Multisample AnnData` +> +> > > > -> > 1. Answer for question1 -> > 2. Answer for question2 +> > 1. Question1? +> > 2. Question2? > > -> {: .solution} +> > > +> > > +> > > 1. Answer for question1 +> > > 2. Answer for question2 +> > > +> > {: .solution} +> > +> {: .question} > -{: .question} +{: .hands_on} -## Sub-step with **SnapATAC2 Preprocessing** +# Dimension Reduction -> Task description +> Spectral embedding > > 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` From b44d86da309168114fe5257b82694e20006ca8f1 Mon Sep 17 00:00:00 2001 From: timonschlegel Date: Tue, 25 Jun 2024 13:48:51 +0000 Subject: [PATCH 04/12] batch correction and clustering --- .../tutorial.md | 390 ++++-------------- 1 file changed, 86 insertions(+), 304 deletions(-) diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index d6d8ea1da8d312..56d6b4b1f1e3fa 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -254,382 +254,164 @@ Then, we will remove that first dataset from the collection through element iden > > > 2. Answer for question2 > > > > > {: .solution} -> > -> {: .question} +> {: .question} > {: .hands_on} # Dimension Reduction +Through concatenation of all samples, the selected features were lost. To solve this issue, they can just be selected again, for all samples. > Spectral embedding > > 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata` (output of **Manipulate AnnData** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData` (output of **Manipulate AnnData** {% icon tool %}) > - *"Number of features to keep"*: `50000` -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Clustering** - -> Task description -> -> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Perform dimension reduction using Laplacian Eigenmap, using 'tl.spectral'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData Features` (output of **pp.select_features** {% icon tool %}) +> - *"distance metric"*: `cosine` > -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} +> 3. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData spectral` > {: .hands_on} -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* +## Control without batch correction -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Clustering** - -> Task description +> UMAP projection without batch correction > > 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Plotting** - -> Task description +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData spectral` (output of **tl.spectral** {% icon tool %}) > -> 1. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData UMAP` +> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData UMAP` (output of **tl.umap** {% icon tool %}) > - *"Color"*: `batch` > - *"Height of the plot"*: `500` > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> +> 4. {% icon galaxy-pencil %} Rename the generated image `spectral-UMAP-No Batch correction` > -> 1. Question1? -> 2. Question2? +> 5. {% icon galaxy-eye %} Inspect the `.png` output > -> > +> > > > -> > 1. Answer for question1 -> > 2. Answer for question2 +> > 1. Question1? +> > 2. Question2? > > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Preprocessing** - -> Task description -> -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: -> - *"Method used for preprocessing"*: `Use harmonypy to integrate different experiments,using 'pp.harmony'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* +> > > +> > > +> > > 1. Answer for question1 +> > > 2. Answer for question2 +> > > +> > {: .solution} +> {: .question} > -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} > {: .hands_on} -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* +# Batch correction with Harmony -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} +We have seen that there are significant batch effects affecting our samples. Now we will correct the technical confounders with *Harmony*. -## Sub-step with **SnapATAC2 Clustering** +> Harmony +> - Harmony explanation +> - Note: other methods also exist in the snapatac2 package +{: .details} -> Task description +> Batch correction and visualization > -> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> - *"Method used for preprocessing"*: `Use harmonypy to integrate different experiments,using 'pp.harmony'` +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData UMAP` (output of **tl.umap** {% icon tool %}) +> +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Preprocessing** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony` (output of **pp.harmony** {% icon tool %}) > - *"Use the indicated representation in `.obsm`"*: `X_spectral_harmony` > - *"`adata.obs` key under which t add cluster labels"*: `umap_harmony` > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* +> 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData harmony UMAP` > -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text +> > Key for cluster labels +> > - If you add the new *UMAP-embeddings* under the key `umap_harmony`, the non-batch corrected embeddings are still stored in the AnnData object. +> > - Alternatively, by leaving this parameter empty, the old embeddings will be overwritten. > {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Plotting** - -> Task description -> -> 1. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> +> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony UMAP` (output of **tl.umap** {% icon tool %}) > - *"Color"*: `batch` > - *"Use the indicated representation in .obsm"*: `X_umap_harmony` > - *"Height of the plot"*: `500` > -> ***TODO***: *Check parameter descriptions* +> 5. {% icon galaxy-pencil %} Rename the generated image `spectral-UMAP-harmony` +> +> 6. {% icon galaxy-eye %} Inspect the `.png` output > -> ***TODO***: *Consider adding a comment or tip box* +> > +> > +> > 1. Question1? +> > 2. Question2? +> > +> > > +> > > +> > > 1. Answer for question1 +> > > 2. Answer for question2 +> > > +> > {: .solution} +> {: .question} > -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} > {: .hands_on} -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} +# Clustering of the batch corrected samples -## Sub-step with **SnapATAC2 Clustering** - -> Task description +> Leiden clustering and visualization > > 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute a neighborhood graph of observations, using 'pp.knn'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony UMAP` (output of **tl.umap** {% icon tool %}) > - *"The key for the matrix"*: `X_spectral_harmony` > -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Clustering** - -> Task description -> -> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Cluster cells into subgroups, using 'tl.leiden'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) -> -> ***TODO***: *Check parameter descriptions* -> -> ***TODO***: *Consider adding a comment or tip box* -> -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony knn` (output of **pp.knn** {% icon tool %}) +> - *"Whether to use the Constant Potts Model (CPM) or modularity"*: `modularity` > -> 1. Question1? -> 2. Question2? -> -> > -> > -> > 1. Answer for question1 -> > 2. Answer for question2 -> > -> {: .solution} -> -{: .question} - -## Sub-step with **SnapATAC2 Plotting** - -> Task description -> -> 1. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> 3. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData harmony leiden` +> +> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` -> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **SnapATAC2 Clustering** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **tl.leiden** {% icon tool %}) > - *"Color"*: `leiden` > - *"Use the indicated representation in .obsm"*: `X_umap_harmony` > - *"Height of the plot"*: `500` > -> ***TODO***: *Check parameter descriptions* +> 5. {% icon galaxy-pencil %} Rename the generated image `spectral-UMAP-harmony-leiden` > -> ***TODO***: *Consider adding a comment or tip box* +> 6. {% icon galaxy-eye %} Inspect the `.png` output > -> > short description -> > -> > A comment about the tool or something else. This box can also be in the main text -> {: .comment} -> -{: .hands_on} - -***TODO***: *Consider adding a question to test the learners understanding of the previous exercise* - -> -> -> 1. Question1? -> 2. Question2? -> -> > +> > > > -> > 1. Answer for question1 -> > 2. Answer for question2 +> > 1. Question1? +> > 2. Question2? > > -> {: .solution} +> > > +> > > +> > > 1. Answer for question1 +> > > 2. Answer for question2 +> > > +> > {: .solution} +> {: .question} > -{: .question} - - -## Re-arrange - -To create the template, each step of the workflow had its own subsection. +> +{: .hands_on} -***TODO***: *Re-arrange the generated subsections into sections or other subsections. -Consider merging some hands-on boxes to have a meaningful flow of the analyses* # Conclusion -Sum up the tutorial and the key takeaways here. We encourage adding an overview image of the -pipeline used. \ No newline at end of file +In this tutorial we performed batch correction on five {scATAC-seq} samples from human colons. \ No newline at end of file From 32e513dd624a4807bffdf8199c97ca7eb82f7210 Mon Sep 17 00:00:00 2001 From: timonschlegel Date: Thu, 18 Jul 2024 07:36:06 +0000 Subject: [PATCH 05/12] images and updated tools --- .../batch-umap-harmony-leiden.png | Bin 0 -> 59641 bytes .../batch-umap-harmony-spectral.png | Bin 0 -> 60756 bytes .../batch-umap-no_harmony-spectral.png | Bin 0 -> 57104 bytes .../mnc-correct-scanorama-umap-spectral.png | Bin 0 -> 2154991 bytes .../tsse-colon01-colon02.png | Bin 0 -> 1001253 bytes .../data-library.yaml | 43 ++++++++ .../tutorial.md | 93 ++++++++++++------ 7 files changed, 107 insertions(+), 29 deletions(-) create mode 100644 topics/single-cell/images/scatac-batch-correction-snapatac2/batch-umap-harmony-leiden.png create mode 100644 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a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/data-library.yaml b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/data-library.yaml new file mode 100644 index 00000000000000..d199795e971643 --- /dev/null +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/data-library.yaml @@ -0,0 +1,43 @@ +--- +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: scATAC-seq standard processing with SnapATAC2 + items: + - name: 'DOI: 10.5281/zenodo.12683310' + description: latest + items: + - url: https://zenodo.org/api/records/12683310/files/colon_multisample_01.gz + src: url + ext: auto + info: https://zenodo.org/records/12683310 + - url: https://zenodo.org/api/records/12683310/files/colon_multisample_02.gz + src: url + ext: auto + info: https://zenodo.org/records/12683310 + - url: https://zenodo.org/api/records/12683310/files/colon_multisample_03.gz + src: url + ext: auto + info: https://zenodo.org/records/12683310 + - url: https://zenodo.org/api/records/12683310/files/colon_multisample_04.gz + src: url + ext: auto + info: https://zenodo.org/records/12683310 + - url: https://zenodo.org/api/records/12683310/files/colon_multisample_05.gz + src: url + ext: auto + info: https://zenodo.org/records/12683310 + - url: https://zenodo.org/api/records/12683310/files/gencode.v46.annotation.gtf.gz/ + src: url + ext: auto + info: https://zenodo.org/records/12683310 + - url: https://zenodo.org/api/records/12683310/files/chrom_sizes.txt/ + src: url + ext: auto + info: https://zenodo.org/records/12683310 \ No newline at end of file diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index 56d6b4b1f1e3fa..3292c9f28f48eb 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -5,7 +5,7 @@ title: Multi-sample batch correction with Harmony and SnapATAC2 subtopic: scmultiomics priority: 3 level: Intermediate -zenodo_link: '' +zenodo_link: https://zenodo.org/records/12683310 questions: - Why is batch correction important when analyzing data from multiple samples? - How is batch correction performed on single cell ATAC-seq data? @@ -72,7 +72,16 @@ In this tutorial, we will perform batch correction on five datasets of {scATAC-s # Data -The datasets for this tutorial are colon samples from multiple donors, provided by the [SnapATAC2 documentation](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). +The datasets for this tutorial are colon samples from multiple donors, provided by the [SnapATAC2 documentation](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). The `chrom_sizes` file and the `gene_annotation` file are identical to the previous tutorial [Single-cell ATAC-seq standard processing with SnapATAC2]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). +The five `colon_multisample` files have been generated by the **Cell Ranger ATAC 2.0.0** pipeline from 10X to generate a [*Fragments File*](https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/output/fragments). + + +> Chromosome sizes +> +> - A chromosome sizes file can be generated using the tool {% tool [Compute sequence length](toolshed.g2.bx.psu.edu/repos/devteam/fasta_compute_length/fasta_compute_length/1.0.3) %}. +> - The reference genome can either be selected from cached genomes or uploaded to the galaxy history. +> +{: .details} ## Get data @@ -80,11 +89,15 @@ The datasets for this tutorial are colon samples from multiple donors, provided > > 1. Create a new history for this tutorial > 2. Import the files from [Zenodo]({{ page.zenodo_link }}) or from -> the shared data library (`GTN - Material` -> `{{ page.topic_name }}` -> -> `{{ page.title }}`): +> the shared data library +> > > ``` -> {{ page.zenodo_link }}/files/colon_multisample.tar +> {{ page.zenodo_link }}/files/colon_multisample_01.gz +> {{ page.zenodo_link }}/files/colon_multisample_02.gz +> {{ page.zenodo_link }}/files/colon_multisample_03.gz +> {{ page.zenodo_link }}/files/colon_multisample_04.gz +> {{ page.zenodo_link }}/files/colon_multisample_05.gz > {{ page.zenodo_link }}/files/chrom_sizes.txt > {{ page.zenodo_link }}/files/gencode.v46.annotation.gtf.gz > ``` @@ -100,7 +113,7 @@ The datasets for this tutorial are colon samples from multiple donors, provided > > 4. Check that the datatypes of the `colon_multisample` files are set to `bed` > -> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="datatypes" %} +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="bed" %} > > 5. Create a dataset collection with all `colon_multisample` datasets. > @@ -110,18 +123,28 @@ The datasets for this tutorial are colon samples from multiple donors, provided # SnapATAC2 preprocessing and filtering -With our data imported and the collection built, we can now begin the {scATAC-seq} data preprocessing with SnapATAC2. +With our data imported and the collection built, we can now begin the {scATAC-seq} data preprocessing with SnapATAC2. The first step is importing the datasets into an AnnData object with the tool *pp.import_data*. Next, the {TSSe} will be calculated. The {TSS} serves as a {QC} measurement to selectively filter droplets containing high-quality cells. +> AnnData format +> +> - The [`AnnData`](https://anndata.readthedocs.io/en/latest/) format was initially developed for the [`Scanpy`](https://scanpy.readthedocs.io/en/stable/index.html) package and is now a widely accepted data format to +store annotated data matrices in a space-efficient manner. + +![Anndata format]({% link topics/single-cell/images/scatac-standard-snapatac2/anndata_schema.svg %} "AnnData format stores a count matrix X together with annotations of +observations (i.e. cells) obs, variables (i.e. genes) var and unstructured annotations uns.") +> +{: .details} + > Preprocessing and QC > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Import data fragment files and compute basic QC metrics, using 'pp.import_data'` > - {% icon param-collection %} *"Fragment file, optionally compressed with gzip or zstd"*: `Colon Multisample Fragments` (Input dataset collection) > - {% icon param-file %} *"A tabular file containing chromosome names and sizes"*: `chrom_sizes.txt` (Input dataset) > - *"Number of unique fragments threshold used to filter cells"*: `1000` -> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Compute the TSS enrichment score (TSSe) for each cell, using 'metrics.tsse'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas` (dataset collection output of **pp.import_data** {% icon tool %}) > - {% icon param-file %} *"GTF/GFF file containing the gene annotation"*: `gene_annotation` (Input dataset) @@ -130,19 +153,19 @@ The first step is importing the datasets into an AnnData object with the tool *p > > {% snippet faqs/galaxy/collections_add_tag.md %} > -> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for plotting"*: `Plot the TSS enrichment vs. number of fragments density figure, using 'pl.tsse'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas TSSe` (output of **metrics.tsse** {% icon tool %}) > 4. {% icon galaxy-eye %} Inspect a few exemplary `.png` outputs of the collection > -> -> ![TSSe plot against number of unique fragments]({% link path %}) +> ![TSSe plots against number of unique fragments]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/tsse-colon01-colon02.png %}"Examplary plots of TSSe from colon_multisample_01 and colon_multisample_02") +> High-quality cells can be identified in the plot of {TSSe} scores against a number of unique fragments for each cell. > > > > > > > 1. Where are high-quality cells located in a {TSSe} plot? -> > 2. Based on this plot, how should the filter be set? +> > 2. Based on these plots, how should the filter be set? > > > > > > > > @@ -163,17 +186,17 @@ Low quality cells might also pass through the filter, but we can assume that the > Filtering > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Filter cell outliers based on counts and numbers of genes expressed, using 'pp.filter_cells'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas TSSe` (output of **metrics.tsse** {% icon tool %}) > - *"Minimum TSS enrichemnt score required for a cell to pass filtering"*: `7.0` > -> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Generate cell by bin count matrix, using 'pp.add_tile_matrix'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas filtered` (output of **pp.filter_cells** {% icon tool %}) > - *"The size of consecutive genomic regions used to record the counts"*: `5000` > -> 3. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 3. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas tile_matrix` (output of **pp.add_tile_matrix** {% icon tool %}) > - *"Number of features to keep"*: `50000` @@ -184,14 +207,14 @@ Low quality cells might also pass through the filter, but we can assume that the > > - Increasing the bin size greatly reduces compute time at the cost of some biological data. > > - *pp.select_features* checks the features identified by the previous tool and selects the features which are most accessible across all cells. > > - The parameter *"Number of features to keep"* determines the upper limit of features which could be selected. -> > - Similarly too the *bin_size*, the Number of features to keep can also impact +> > - Similarly too the *bin_size*, the Number of Features to keep can also impact downstream clustering. This was visualized in the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection ). > {: .details} > -> 4. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 4. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Compute probability of being a doublet using the scrublet algorithm, using 'pp.scrublet'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas features` (output of **pp.select_features** {% icon tool %}) > -> 5. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 5. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Remove doublets according to the doublet probability or doublet score, using 'pp.filter_doublets'` > - {% icon param-file %} *"Annotated data matrix"*: `Colon Multisample AnnDatas scrublet` (output of **pp.scrublet** {% icon tool %}) > @@ -263,12 +286,12 @@ Through concatenation of all samples, the selected features were lost. To solve > Spectral embedding > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData` (output of **Manipulate AnnData** {% icon tool %}) > - *"Number of features to keep"*: `50000` > -> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Perform dimension reduction using Laplacian Eigenmap, using 'tl.spectral'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData Features` (output of **pp.select_features** {% icon tool %}) > - *"distance metric"*: `cosine` @@ -281,12 +304,12 @@ Through concatenation of all samples, the selected features were lost. To solve > UMAP projection without batch correction > -> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData spectral` (output of **tl.spectral** {% icon tool %}) > > 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData UMAP` -> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData UMAP` (output of **tl.umap** {% icon tool %}) > - *"Color"*: `batch` @@ -296,6 +319,8 @@ Through concatenation of all samples, the selected features were lost. To solve > > 5. {% icon galaxy-eye %} Inspect the `.png` output > +> ![UMAP plot before batch correction]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/batch-umap-no_harmony-spectral.png %}) +> > > > > > > 1. Question1? @@ -323,11 +348,11 @@ We have seen that there are significant batch effects affecting our samples. Now > Batch correction and visualization > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy1) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for preprocessing"*: `Use harmonypy to integrate different experiments,using 'pp.harmony'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData UMAP` (output of **tl.umap** {% icon tool %}) > -> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony` (output of **pp.harmony** {% icon tool %}) > - *"Use the indicated representation in `.obsm`"*: `X_spectral_harmony` @@ -340,7 +365,7 @@ We have seen that there are significant batch effects affecting our samples. Now > > - Alternatively, by leaving this parameter empty, the old embeddings will be overwritten. > {: .comment} > -> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony UMAP` (output of **tl.umap** {% icon tool %}) > - *"Color"*: `batch` @@ -351,6 +376,8 @@ We have seen that there are significant batch effects affecting our samples. Now > > 6. {% icon galaxy-eye %} Inspect the `.png` output > +> ![UMAP plot of Batch correction with Harmony]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/batch-umap-harmony-spectral.png %}) +> > > > > > > 1. Question1? @@ -367,24 +394,31 @@ We have seen that there are significant batch effects affecting our samples. Now > {: .hands_on} +> Batch correction with Scanorama and MNC-correct +> +> - Examplary outputs of the methods *Scanorama* and *MNC-correct* are shown here: +> ![Batch correction UMAP plots of MNC-correct with different settings and Scanorama]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/mnc-correct-scanorama-umap-spectral.png %} "UMAP plots of batch correction with different methods. \(a\) Batch correction with MNC-correct and default settings. \(b\) Batch correction with Scanorama \(c\) Batch correction with MNC-correct and 30 iterations.") +> +{: .details} + # Clustering of the batch corrected samples > Leiden clustering and visualization > -> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute a neighborhood graph of observations, using 'pp.knn'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony UMAP` (output of **tl.umap** {% icon tool %}) > - *"The key for the matrix"*: `X_spectral_harmony` > -> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy1) %} with the following parameters: +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Cluster cells into subgroups, using 'tl.leiden'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony knn` (output of **pp.knn** {% icon tool %}) > - *"Whether to use the Constant Potts Model (CPM) or modularity"*: `modularity` > > 3. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData harmony leiden` > -> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy1) %} with the following parameters: +> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **tl.leiden** {% icon tool %}) > - *"Color"*: `leiden` @@ -395,6 +429,7 @@ We have seen that there are significant batch effects affecting our samples. Now > > 6. {% icon galaxy-eye %} Inspect the `.png` output > +> ![UMAP plot of Batch-corrected leiden clusters]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/batch-umap-harmony-leiden.png %}) > > > > > > 1. Question1? From dd68e82ce19f009fea51bda53a9c084735a58cf9 Mon Sep 17 00:00:00 2001 From: Timon Schlegel Date: Fri, 2 Aug 2024 19:30:32 +0000 Subject: [PATCH 06/12] improvements to data upload and preprocessing --- .../histories_datasets_vs_collections.md | 2 +- .../tutorial.bib | 28 ++++++++ .../tutorial.md | 68 ++++++++++++------- 3 files changed, 72 insertions(+), 26 deletions(-) diff --git a/faqs/galaxy/histories_datasets_vs_collections.md b/faqs/galaxy/histories_datasets_vs_collections.md index 75066db99e67e1..7c7c4b88d942ce 100644 --- a/faqs/galaxy/histories_datasets_vs_collections.md +++ b/faqs/galaxy/histories_datasets_vs_collections.md @@ -25,7 +25,7 @@ There is a number of situations when simple collections are not sufficient to re **Nested collections** -Probably the most common example of this is pared end data when each sample is represented by two files: one containing forward reads and another containing reverse reads. In Galaxy you can create **nested** collection that reflects the hierarchy of the data. In the case of paired data Galaxy supports **paired** collections. +Probably the most common example of this is paired end data when each sample is represented by two files: one containing forward reads and another containing reverse reads. In Galaxy you can create **nested** collection that reflects the hierarchy of the data. In the case of paired data Galaxy supports **paired** collections. ![A paired collection is a container containing individual datasets and preserving their hierarchy]({% link shared/images/paired_collection.svg %}) diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib index 3dd810474ee008..5a9c8689eb6b87 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib @@ -47,6 +47,34 @@ @article{Korsunsky2019 month = nov, pages = {1289–1296} } +@article{Hie2019, + title = {Efficient integration of heterogeneous single-cell transcriptomes using Scanorama}, + volume = {37}, + ISSN = {1546-1696}, + url = {http://dx.doi.org/10.1038/s41587-019-0113-3}, + DOI = {10.1038/s41587-019-0113-3}, + number = {6}, + journal = {Nature Biotechnology}, + publisher = {Springer Science and Business Media LLC}, + author = {Hie, Brian and Bryson, Bryan and Berger, Bonnie}, + year = {2019}, + month = may, + pages = {685–691} +} +@article{Haghverdi2018, + title = {Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors}, + volume = {36}, + ISSN = {1546-1696}, + url = {http://dx.doi.org/10.1038/nbt.4091}, + DOI = {10.1038/nbt.4091}, + number = {5}, + journal = {Nature Biotechnology}, + publisher = {Springer Science and Business Media LLC}, + author = {Haghverdi, Laleh and Lun, Aaron T L and Morgan, Michael D and Marioni, John C}, + year = {2018}, + month = apr, + pages = {421–427} +} @article{Zhang2024, title = {A fast, scalable and versatile tool for analysis of single-cell omics data}, volume = {21}, diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index 3292c9f28f48eb..e823b26eb498d6 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -12,7 +12,7 @@ questions: objectives: - Perform batch correction on a collection of single cell ATAC-seq data - Learn how Harmony integrates different samples -time_estimation: 3H +time_estimation: 4H key_points: - Batch correction is important for integration of data from multiple experiments - How harmony works @@ -43,10 +43,10 @@ gitter: Galaxy-Training-Network/galaxy-single-cell # Introduction -Performing biological experiments in replicates is one of the cornerstones of modern science. However, when integrating data from multiple single-cell sequencing experiments, technical confounders might impact the results. -To reduce technical confounders, such as different experimenters, experimental protocols, sequencing lanes or sequencing technologies, a batch correction might be beneficial. +Performing biological experiments in replicates is a cornerstone of modern science. However, when integrating data from multiple single-cell sequencing experiments, technical confounders might impact the results. +To reduce technical confounders, such as different experimenters, experimental protocols, sequencing lanes or sequencing technologies, batch correction is often beneficial. -In this tutorial, we will perform batch correction on five datasets of {scATAC-seq} data with the algorithm *Harmony* ({% cite Korsunsky2019 %}) and the tool suite [**SnapATAC2**] (https://kzhang.org/SnapATAC2/version/2.5/index.html) ({% cite Zhang2024 %}). +In this tutorial, we will perform batch correction on five datasets of {scATAC-seq} data with the three algorithms *Harmony* ({% cite Korsunsky2019 %}), *Scanorama* ({% cite Hie2019 %}) and the *mutual nearest neighbor-based* ({% cite Haghverdi2018%}) algorithm *MNC-correct* ({% cite Zhang2024 %}). The {scATAC-seq} analysis will be performed with the tool suite [**SnapATAC2**](https://kzhang.org/SnapATAC2/version/2.5/index.html) ({% cite Zhang2024 %}). {% snippet topics/single-cell/faqs/single_cell_omics.md %} @@ -55,6 +55,7 @@ In this tutorial, we will perform batch correction on five datasets of {scATAC-s > > > This tutorial is significantly based on ["Multi-sample Pipeline" tutorial from SnapATAC2](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). +> > The data analysis is performed with the same tools shown in the tutorial [Single-cell ATAC-seq standard processing with SnapATAC2]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). > - That tutorial also explains the steps of the ATAC-seq analysis with SnapATAC2 in more detail. > - We recommend completing that tutorial before continuing with this one. @@ -72,7 +73,7 @@ In this tutorial, we will perform batch correction on five datasets of {scATAC-s # Data -The datasets for this tutorial are colon samples from multiple donors, provided by the [SnapATAC2 documentation](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). The `chrom_sizes` file and the `gene_annotation` file are identical to the previous tutorial [Single-cell ATAC-seq standard processing with SnapATAC2]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). +In this tutorial we will analyze colon samples from multiple donors, provided by the [SnapATAC2 documentation](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). The `chrom_sizes` file and the `gene_annotation` file are identical to the previous tutorial [Single-cell ATAC-seq standard processing with SnapATAC2]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). The five `colon_multisample` files have been generated by the **Cell Ranger ATAC 2.0.0** pipeline from 10X to generate a [*Fragments File*](https://support.10xgenomics.com/single-cell-atac/software/pipelines/latest/output/fragments). @@ -83,6 +84,11 @@ The five `colon_multisample` files have been generated by the **Cell Ranger ATAC > {: .details} +First we will import all datasets into Galaxy. Then we will build a dataset collection, containing all `colon_multisample` datasets. This will make the following analysis steps much simpler. + +{% snippet faqs/galaxy/histories_datasets_vs_collections.md %} + + ## Get data > Data Upload @@ -106,6 +112,14 @@ The five `colon_multisample` files have been generated by the **Cell Ranger ATAC > > {% snippet faqs/galaxy/datasets_import_from_data_library.md %} > +> > Large file sizes! +> > - The `colon_multisample` datasets are quite large. +> > - While uploading those datasets, change the {% icon galaxy-gear %}**Upload Configuration** to *Defer dataset resolution*. +> > - This will delete the datasets after the first analysis step and helps in reducing storage. +> > +> {: .warning} +> +> > 3. Rename the datasets > - {% icon galaxy-pencil %} **Rename** the file `gencode.v46.annotation.gtf.gz` to `gene_annotation.gtf.gz` > @@ -117,7 +131,7 @@ The five `colon_multisample` files have been generated by the **Cell Ranger ATAC > > 5. Create a dataset collection with all `colon_multisample` datasets. > -> {% snippet faqs/galaxy/collections_build_list.mdu name="Colon Multisample Fragments" %} +> {% snippet faqs/galaxy/collections_build_list.md name="Colon Multisample Fragments" %} > {: .hands_on} @@ -129,11 +143,10 @@ The first step is importing the datasets into an AnnData object with the tool *p > AnnData format > -> - The [`AnnData`](https://anndata.readthedocs.io/en/latest/) format was initially developed for the [`Scanpy`](https://scanpy.readthedocs.io/en/stable/index.html) package and is now a widely accepted data format to -store annotated data matrices in a space-efficient manner. - -![Anndata format]({% link topics/single-cell/images/scatac-standard-snapatac2/anndata_schema.svg %} "AnnData format stores a count matrix X together with annotations of -observations (i.e. cells) obs, variables (i.e. genes) var and unstructured annotations uns.") +> - The [`AnnData`](https://anndata.readthedocs.io/en/latest/) format was initially developed for the [`Scanpy`](https://scanpy.readthedocs.io/en/stable/index.html) package and is now a widely accepted data format to store annotated data matrices in a space-efficient manner. +> +> ![Anndata format]({% link topics/single-cell/images/scatac-standard-snapatac2/anndata_schema.svg %} "AnnData format stores a count matrix X together with annotations of +>observations (i.e. cells) obs, variables (i.e. genes) var and unstructured annotations uns.") > {: .details} @@ -182,8 +195,6 @@ High-quality cells can be identified in the plot of {TSSe} scores against a numb The {TSSe} distributions show that the sample quality differs substantially between batches. In order to retain as much biological data as possible, we need to use a broad filter (f.ex. minimum TSSe = 7.0). -Low quality cells might also pass through the filter, but we can assume that these confounding factors will be corrected in later steps with *Harmony*. - > Filtering > > 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: @@ -203,11 +214,11 @@ Low quality cells might also pass through the filter, but we can assume that the > > > Bin size and features > > -> > - *pp.add_tile_matrix* divides the genome into a specified number of bins (f.ex. 5000). For each bin, the ATAC-seq reads of individual cells are checked if they are located that bin. This is counted as a feature and stored in `n_vars`. +> > - *pp.add_tile_matrix* divides the genome into a specified number of bins, depending on the bin-size (f.ex. 5000bp). For each bin, the ATAC-seq reads of individual cells are checked to determine if a read is located in the bin. This is counted as a feature and stored as `n_vars` in the AnnData object. > > - Increasing the bin size greatly reduces compute time at the cost of some biological data. -> > - *pp.select_features* checks the features identified by the previous tool and selects the features which are most accessible across all cells. -> > - The parameter *"Number of features to keep"* determines the upper limit of features which could be selected. -> > - Similarly too the *bin_size*, the Number of Features to keep can also impact downstream clustering. This was visualized in the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection ). +> > - *pp.select_features* uses the previously identified features to select the most accessible features for further analysis. +> > - The parameter *"Number of features to keep"* determines the upper limit of features which can be selected. +> > - Similarly too the *bin_size*, the *Number of Features to keep* can also impact downstream clustering. This was visualized in the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection ). > {: .details} > > 4. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: @@ -221,19 +232,18 @@ Low quality cells might also pass through the filter, but we can assume that the {: .hands_on} # Concatenate the Collection -Before we can continue with the analysis and batch correction. We need to extract the datasets out of the collection and merge them into a single AnnData object. +Before we can continue with the analysis and batch correction, we need to extract the datasets out of the collection and merge them into a single AnnData object. ## Extracting datasets from a collection First, we will extract the first dataset out of the collection using the Galaxy tool *Extract dataset*. - Then, we will remove that first dataset from the collection through element identifiers. > -> Although we could manually remove the first dataset, the method shown here has the advantage that it can be implemented in a workflow. +> - It is also possible to manually remove a dataset from a collection. +> - However, the automatic method shown here can also be implemented in a workflow. Which makes this approach much better scalable. {: .comment} - > Extract datasets > > 1. {% tool [Extract element from collection](__EXTRACT_DATASET__) %} with the following parameters: @@ -265,16 +275,24 @@ Then, we will remove that first dataset from the collection through element iden > - *"Key to add the batch annotation to obs"*: `batch` > 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData` > 3. {% icon galaxy-eye %} Inspect the general information of `Multisample AnnData` +> +> > ``` +> > AnnData object with n_obs × n_vars = 34372 × 606219 +> > obs: 'n_fragment', 'frac_dup', 'frac_mito', 'tsse', 'doublet_probability', 'doublet_score', 'batch' +> > var: 'count-0', 'selected-0', 'count-1', 'selected-1', 'count-2', 'selected-2', 'count-3', 'selected-3', 'count-4', 'selected-4' +> > obsm: 'fragment_paired' +> > ``` +> > > > > > > -> > 1. Question1? -> > 2. Question2? +> > 1. How many colon cells are stored in this AnnData object? +> > 2. What do the different 'count-' and 'selected-' stand for? > > > > > > > > -> > > 1. Answer for question1 -> > > 2. Answer for question2 +> > > 1. There are 34372 cells. +> > > 2. 'count' and 'selected' are variable annotations indicating the detected and selected features. 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z{51aeq9n{GU;4iRg%b4zgZ}~w6`GQZ|3!>1KHd5+_@sBS;J<*v|Hhlk|3!CVDFpuu zC^9LHF8mi!-~V&!2$PF4`b>sZ!&((CE7OA}X@_zs@ ChQ+P` literal 0 HcmV?d00001 diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib index 5a9c8689eb6b87..574f682b8bff78 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.bib @@ -75,6 +75,20 @@ @article{Haghverdi2018 month = apr, pages = {421–427} } +@article{Luecken2021, + title = {Benchmarking atlas-level data integration in single-cell genomics}, + volume = {19}, + ISSN = {1548-7105}, + url = {http://dx.doi.org/10.1038/s41592-021-01336-8}, + DOI = {10.1038/s41592-021-01336-8}, + number = {1}, + journal = {Nature Methods}, + publisher = {Springer Science and Business Media LLC}, + author = {Luecken, Malte D. and B\"{u}ttner, M. and Chaichoompu, K. and Danese, A. and Interlandi, M. and Mueller, M. F. and Strobl, D. C. and Zappia, L. and Dugas, M. and Colomé-Tatché, M. and Theis, Fabian J.}, + year = {2021}, + month = dec, + pages = {41–50} +} @article{Zhang2024, title = {A fast, scalable and versatile tool for analysis of single-cell omics data}, volume = {21}, diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index e823b26eb498d6..39222466e46f74 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -57,8 +57,8 @@ In this tutorial, we will perform batch correction on five datasets of {scATAC-s > This tutorial is significantly based on ["Multi-sample Pipeline" tutorial from SnapATAC2](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). > > The data analysis is performed with the same tools shown in the tutorial [Single-cell ATAC-seq standard processing with SnapATAC2]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). -> - That tutorial also explains the steps of the ATAC-seq analysis with SnapATAC2 in more detail. -> - We recommend completing that tutorial before continuing with this one. +> That tutorial also explains the steps of the ATAC-seq analysis with SnapATAC2 in more detail. +> We recommend completing that tutorial before continuing with this one. > {: .comment} @@ -114,11 +114,10 @@ First we will import all datasets into Galaxy. Then we will build a dataset coll > > > Large file sizes! > > - The `colon_multisample` datasets are quite large. -> > - While uploading those datasets, change the {% icon galaxy-gear %}**Upload Configuration** to *Defer dataset resolution*. +> > - While uploading those datasets, change the {% icon galaxy-gear %} **Upload Configuration** to *Defer dataset resolution*. > > - This will delete the datasets after the first analysis step and helps in reducing storage. > > -> {: .warning} -> +> {: .warning} > > 3. Rename the datasets > - {% icon galaxy-pencil %} **Rename** the file `gencode.v46.annotation.gtf.gz` to `gene_annotation.gtf.gz` @@ -143,10 +142,9 @@ The first step is importing the datasets into an AnnData object with the tool *p > AnnData format > -> - The [`AnnData`](https://anndata.readthedocs.io/en/latest/) format was initially developed for the [`Scanpy`](https://scanpy.readthedocs.io/en/stable/index.html) package and is now a widely accepted data format to store annotated data matrices in a space-efficient manner. +> - The [**AnnData**](https://anndata.readthedocs.io/en/latest/) format was initially developed for the [**Scanpy**](https://scanpy.readthedocs.io/en/stable/index.html) package and is now a widely accepted data format to store annotated data matrices in a space-efficient manner. > -> ![Anndata format]({% link topics/single-cell/images/scatac-standard-snapatac2/anndata_schema.svg %} "AnnData format stores a count matrix X together with annotations of ->observations (i.e. cells) obs, variables (i.e. genes) var and unstructured annotations uns.") +> ![Anndata format]({% link topics/single-cell/images/scatac-standard-snapatac2/anndata_schema.svg %} "AnnData format stores a count matrix X together with annotations of observations (i.e. cells) obs, variables (i.e. genes) var and unstructured annotations uns.") > {: .details} @@ -214,8 +212,9 @@ The {TSSe} distributions show that the sample quality differs substantially betw > > > Bin size and features > > -> > - *pp.add_tile_matrix* divides the genome into a specified number of bins, depending on the bin-size (f.ex. 5000bp). For each bin, the ATAC-seq reads of individual cells are checked to determine if a read is located in the bin. This is counted as a feature and stored as `n_vars` in the AnnData object. +> > - *pp.add_tile_matrix* divides the genome into a specified number of bins, depending on the bin size (f.ex. 5000bp). For each bin, the ATAC-seq reads of individual cells are checked to determine if a read is located in the bin. This is counted as a feature and stored under `n_vars` in the AnnData object. > > - Increasing the bin size greatly reduces compute time at the cost of some biological data. +> > - For this reason, the bin size 5000bp has been selected for the colon datasets. In the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}), the lower bin size of 500bp was chosen, since fewer cells were analyzed. > > - *pp.select_features* uses the previously identified features to select the most accessible features for further analysis. > > - The parameter *"Number of features to keep"* determines the upper limit of features which can be selected. > > - Similarly too the *bin_size*, the *Number of Features to keep* can also impact downstream clustering. This was visualized in the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection ). @@ -241,7 +240,7 @@ Then, we will remove that first dataset from the collection through element iden > > - It is also possible to manually remove a dataset from a collection. -> - However, the automatic method shown here can also be implemented in a workflow. Which makes this approach much better scalable. +> - However, manually removing datasets can not be implemented in Galaxy workflows. In contrast, the automatic method shown here, can be represented in a workflow and is therefore much better scalable. {: .comment} > Extract datasets @@ -287,12 +286,14 @@ Then, we will remove that first dataset from the collection through element iden > > > > > > 1. How many colon cells are stored in this AnnData object? -> > 2. What do the different 'count-' and 'selected-' stand for? +> > 2. What does the 'batch' annotation represent? +> > 3. What do the different annotations for 'count-' and 'selected-' stand for? > > > > > > > > > > > 1. There are 34372 cells. -> > > 2. 'count' and 'selected' are variable annotations indicating the detected and selected features. The sample number (0-4) specifies the dataset which produced these annotations. +> > > 2. The cells are marked with the 'batch' annotation according to their sample number (0-4). This annotation will be used later by the batch correction algorithms to produce clusters from multiple samples. +> > > 3. 'count' and 'selected' are variable annotations indicating the detected and selected features. The sample number (0-4) specifies the dataset which produced these annotations. > > > > > {: .solution} > {: .question} @@ -300,7 +301,7 @@ Then, we will remove that first dataset from the collection through element iden {: .hands_on} # Dimension Reduction -Through concatenation of all samples, the selected features were lost. To solve this issue, they can just be selected again, for all samples. +Now that all samples have been concatenated into a single AnnData object, the most accessible features of the combined count matrix must be selected. The previously selected features were only dependent on the individual samples and can not be utilized for dimensionality reduction. Therefore the most accessible features will be selected once again and then dimensionality reduction through *matrix-free spectral embedding* will be performed. > Spectral embedding > @@ -311,7 +312,7 @@ Through concatenation of all samples, the selected features were lost. To solve > > 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Perform dimension reduction using Laplacian Eigenmap, using 'tl.spectral'` -> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData Features` (output of **pp.select_features** {% icon tool %}) +> - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData features` (output of **pp.select_features** {% icon tool %}) > - *"distance metric"*: `cosine` > > 3. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData spectral` @@ -319,6 +320,7 @@ Through concatenation of all samples, the selected features were lost. To solve {: .hands_on} ## Control without batch correction +Batch effects can be visualized in an {UMAP} projection of the data. For this, the different samples are colored according to their batch annotation (obs: 'batch'). > UMAP projection without batch correction > @@ -341,13 +343,13 @@ Through concatenation of all samples, the selected features were lost. To solve > > > > > -> > 1. Question1? -> > 2. Question2? +> > 1. How are batch effects visible in this plot? +> > 2. How would a plot without batch effects look like? > > > > > > > > -> > > 1. Answer for question1 -> > > 2. Answer for question2 +> > > 1. Batch effects are visible as parts of the projection where the different samples are not distributed evenly. For example the large group in the left center of the plot consists of seperate groups for each batch. It is very likely, that the differences between these groups are technical artefacts, due to batch effects and do not represent "real" biological differences between the cells of different samples. +> > > 2. A plot without batch effects does not exhibit groups of cells from a single sample. Instead, most parts of the projection would consist of evenly distributed cells from all samples. > > > > > {: .solution} > {: .question} @@ -355,15 +357,23 @@ Through concatenation of all samples, the selected features were lost. To solve > {: .hands_on} -# Batch correction with Harmony - -We have seen that there are significant batch effects affecting our samples. Now we will correct the technical confounders with *Harmony*. +# Batch correction +After confirming that batch effects have affected our samples, we should remove them with correction algorithms. **SnapATAC2** offers three algorithms for batch correction: *Harmony*, *MNC-correct* and *Scanorama*. In order to determine the best-suited algorithm for your specific dataset, the batch correction outputs of different algorithms should be compared. -> Harmony -> - Harmony explanation -> - Note: other methods also exist in the snapatac2 package +> Batch correction algorithms +> - Batch correction algorithms aim to correct the cell-by-feature count matrix against batch-specific differences between samples. +> - Batch effects can arise from many different technical sources: variation in sequencing lanes, plates, protocol, handling. Additionally, biological factors can also be regarded as batch effects, for example tissue types, species and inter-individual variation {% cite Luecken2021 % }. +> - Many different correction algorithms have been developed, although most methods for scATAC-seq have been adapted from scRNA-seq batch removal algorithms. +> - *Harmony* {% cite Korsunsky2019 % } is a principle component analysis (PCA)-based method which utilizes the previously generated lower-dimensional data, to assign cells into new clusters. The grouping of cells into clusters favors multi-sample clusters, in order to integrate the datasets. Linear correction factors are calculated for each batch and cluster, and the cells are moved to the corrected positions. The preceding steps are iterated, until optimal batch correction is achieved. +> ![Graphical abstract of Harmony batch correction]({% link topics/single-cell/images/harmony-graphical-abstract.png %}) +> - *Scanorama* {% cite Hie2019 % } performs panorama stiching, to find and merge overlapping cell types. +> ![Graphical abstract of Scanorama batch correction]({% link topics/single-cell/images/scanorama-graphical-abstract.png %}) +> - *MNC-correct* {% cite Zhang2024 % } is a modified version of a *mutual nearest neighbor* algorithm {% cite Haghverdi2018 % }. The algorithm calculates centroids for batch-specific clusters and identifies pairs of mutual nearest centroids (MNC) across batches. Correction vectors align the batches in the same plane. Additionally, *MNC-correct* can also be run iteratively, to find the most ideal corrections. +> ![Graphical abstract of MNC-correct batch correction]({% link topics/single-cell/images/mnn-graphical-abstract.png %}) +> {: .details} +We will use *Harmony* to correct for batch effects first. The other methods will be tested afterwards. > Batch correction and visualization > > 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: @@ -398,13 +408,13 @@ We have seen that there are significant batch effects affecting our samples. Now > > > > > -> > 1. Question1? -> > 2. Question2? +> > 1. How has *Harmony* changed the appearance of this plot? +> > 2. Are there still parts of the plot where batch effects weren't removed? > > > > > > > > -> > > 1. Answer for question1 -> > > 2. Answer for question2 +> > > 1. Most of the batch effects have been successfully removed by *Harmony*. Especially the center-left groups have now been merged into a single larger group, consisting of all batches. +> > > 2. Yes, for example in the upper-right corner batch-specific colors are not overlapping completely. However, it is possible that some samples simply contain fewer cells and are underrepresented in clusters of rare cell types. Therefore it can not be said for certain, if *Harmony* has removed all batch effects or not. > > > > > {: .solution} > {: .question} From 971cb4607a82a67d21c57b2d95825f979c6dbf57 Mon Sep 17 00:00:00 2001 From: Timon Schlegel Date: Wed, 7 Aug 2024 14:30:20 +0000 Subject: [PATCH 08/12] new workflow, image and tool versions --- .../batch-umap-harmony-leiden.png | Bin 59641 -> 64363 bytes .../batch-umap-harmony-spectral.png | Bin 60756 -> 64588 bytes .../batch-umap-no_harmony-spectral.png | Bin 57104 -> 75517 bytes .../tutorial.md | 104 +- .../workflows/main_workflow.ga | 1676 ++++++++++++++++- 5 files changed, 1746 insertions(+), 34 deletions(-) diff --git a/topics/single-cell/images/scatac-batch-correction-snapatac2/batch-umap-harmony-leiden.png b/topics/single-cell/images/scatac-batch-correction-snapatac2/batch-umap-harmony-leiden.png index 60b8cbe8678c877bce58f2d8f571d50d9481f629..c1e13bb1f5a37866abdf05fca540d6db0873d21b 100644 GIT binary patch literal 64363 zcmeEuWmr^Q+xE=RE#2K9sf6T6GYCV2G$>L^cMe@jm$Y<;bPtUvA>9qqB_;WdKDYP# ze(~%3{T|2W&oHdL_gYt;=XnvXsv?hrNr4Ff0B{r)WYhrwkS+iKJVQrCe6p&lF#!P3 z0TgAPYq}flXP`N2YF+kTetXT*79%Ca_UUpda;!85HuLBaR+vdIuAgibCr5;4`QOVx z4i5I;)T4fS8Yc(V4)Yf36iX5Roo7>fph@f29BeMJ@v{V*Xx@DFBqDNPkCB z0y^M7S2lc2=-(B@Ni^NbhX(D55VEnrR-S5Xh`k%-6 zyQTlL8~7^}+ZEzs_IZR5#gy_2>WNtExeNmTda}T9Swy z6iLd{7lzMR<*?r8oyFeK#M5!h-`d)GeAsfd)iPZ!<_}WppZ^3O14b(;{t0Cf*#5UN zvPNnjlB;_WF9D(vSQ5o1-hlPi#_Bh}TgY zX>oC}^->ejYGucLM3(P0hN7Zkk?JQ<2o7aP{k!=Ruj}pH=$Q2M_b?dXv3~uF@`KqL zRwKW=bMK>Ho=}qrRWwt~$JED-34bDD_Q0b?&8O#wiiH8&6WE!FI9AZdszvweLN7(aV}G<;~wmDf!>dSoJl!0b7;p z{)~SptuNVp{kyA0brC|N4BdG%Fk)cPC??LFu;S!MfzWjx!)3%)=-b^;m%)0;fWQ2~FR~4g7B|~I&rutR#JH$Kk5aST~ zGpq3`(q~dqi#WqaKS|T8)uugTt8yMK{NL>Y3uS7d$?0S(&CYCZs$9pr9qR{Vmsd!L zbiMC`sO05NVV0bNLKuOrVY9PZY-K-U6C=FB;Ue}cFbKW63Gv4fjBGfE6iGfT11u8k73KHvX2D1Q1Nr3 zfPeta50g#!)YKoUs(8sh$Pstuz&XxrT3krgO%|JvLfqv3tZl!JZlJf}yX#%Tvnc2-27Sz!7djo=!8W6Uo?d%(x9up)UtN2vb< zJIq)Te)Jq(^YZ@dvp~d}Tlk1IJ2U^T*~sEwYX#n|gwX@4G64%Vpe8h+k_+7iBisZT zTaT0gmw?V+Kju$pllp6#1~PC8q&#J`vVNcwu`;+bZ_*$ik|u~0(|~tuiJCx$&EyMA zppp(l1iS_gM43Wu7xM4z!oX|7bg%GZ+{oCTkXF&4h*2PYi3fBT!K=4m@{Dj(fOiWJ z7>E&rcv9^@!Sev&75@pIKQLZbpyuT9l;iUzgFu<6VyS?7VLG=;lJ9ZbN%@#D64b{# zK;$-{up=N)^m!co07}Qz?|*mVaa>KND&FILBN>C7L5&-lt04A8*&Q9TEDey6%;cXV z2M1C;MuG!VzJfBjU=xyn21oF*jh^*r62QF<#Vix_{yz08F3YO{Nlc$afX+K}O~%C7 zfJTm6MSE?x>w81HP{`Atft4+*^}Wv#(SB0xzaF>;cU%SC(+$P(0+fjZ&;>LQqzXu1 zxWZeoVy+8WD&9+gr}*jYkypkO=@eHkn0TF_EOPg0-jiu~mjbJWOPy>77Gviygw0du zQA3T4XnqH{_f*f+>yDb10ug)qZ;S+xD*@waWB4%!ba7|m0hO+>eAE>Z^Pqc&xgtr%Yrs(TStwJPHBJCy z!3^0bB|dCZ{&PlishbFDw!AV=;g*re&OyVnHck#2-jqu4v9KNgAv7hT!)?K)H-Ukz+VUxk+X*`gBhe=5Z#|`xB%cE!kn<*me zj)2FAXok*(c*n1u0X-dP3C4I#eptHe04HX;b2qLzVt!26d@nek`>Xo- zTGnO;;H|||zj7x=8?Ke_R09SeGBN)dQoy{`5VOPzIo@_i`NON&7%POWsv`bv5@yJybhX?c|EU$heDpE=_`*-9(C;YhIWfq@1)@0{RB>S9!~?pY1YA_& z6_SvS_mIfi>cZmC+1c$$BT;S2rs7qR;~6!cP!aPyR{W`0q8gOe*coY;#EuSodDC|O zbVVD}Uf2Iy;@k+8E8PAf&vQ+;JVxS(DMpak9t-uY6}ZW-&Ze}JFrs%OvW+kL*L*$p z`V1T^|1q`k*^jQwI*c&toJp-;m*Y8F^_p;L6%sw5#!k(-Mh!5Y z#{_$fYRO#XSOHFfb0rSQ8v=NH0qWi9Y;xZK>3fgW0X!%Na+2^ik{CCl0fT%)CG}`W zNrHnxBed5?$ouSbM|rAC4TUTh--si>W3A^dJpw^*38RP990@jdU+pjYTN;0np1vC> zetyzLKwGxd=Jl#444>D)b3=QYxT5v9oooJ$9QeR+wv2sW?IIb=;x>3PP(yLZmj0I? 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zuyYdtJ*(gRaIB@J#dfpu{aG24j6$$d>=ckf1JUsG5N$d@>It!eJ+{> zvV(A*?9M}c2-^BV0eR~udiHjA9nIbG(RU_aR#RJgiE(wL9Rr1Od2X=3F3bHv1%_62 z!%87Jt3GHrv=x6dJAidRgw;{Wy1Kf6 zMifMxaXiOFHTz3|_-z44n_z&N*vu2{YJH3RH&tY0^qHsLTa(0zpoSp(<&Yk#HL%VD zDM3%7I1DCQ@+UZ6pV*heVFnI(DvYB2-DKNW+|r*YRvP~7g5n!OR_$u!y0pM5ar3y( z0Op~ypwi7w`Y7*Vt%P|b3v0rS$UV9TTeHPr#@)>`F8pG9-~uS;9!&G {% snippet faqs/galaxy/datasets_import_from_data_library.md %} > > > Large file sizes! -> > - The `colon_multisample` datasets are quite large. -> > - While uploading those datasets, change the {% icon galaxy-gear %} **Upload Configuration** to *Defer dataset resolution*. -> > - This will delete the datasets after the first analysis step and helps in reducing storage. +> > - The `colon_multisample` datasets are quite large. The entire tutorial requires approximately 50 GB of storage. +> > - To reduce storage you can change the {% icon galaxy-gear %} **Upload Configuration** to *Defer dataset resolution* for the upload of the `colon_multisample` datasets. +> > - This will delete the datasets after the first analysis step and helps in reducing storage. +> > - You can also permanently delete datasets, which are no longer required. +> > +> > {% snippet faqs/galaxy/datasets_deleting.md %} > > > {: .warning} > @@ -150,12 +153,12 @@ The first step is importing the datasets into an AnnData object with the tool *p > Preprocessing and QC > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Import data fragment files and compute basic QC metrics, using 'pp.import_data'` > - {% icon param-collection %} *"Fragment file, optionally compressed with gzip or zstd"*: `Colon Multisample Fragments` (Input dataset collection) > - {% icon param-file %} *"A tabular file containing chromosome names and sizes"*: `chrom_sizes.txt` (Input dataset) > - *"Number of unique fragments threshold used to filter cells"*: `1000` -> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Compute the TSS enrichment score (TSSe) for each cell, using 'metrics.tsse'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas` (dataset collection output of **pp.import_data** {% icon tool %}) > - {% icon param-file %} *"GTF/GFF file containing the gene annotation"*: `gene_annotation` (Input dataset) @@ -164,7 +167,7 @@ The first step is importing the datasets into an AnnData object with the tool *p > > {% snippet faqs/galaxy/collections_add_tag.md %} > -> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: +> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the TSS enrichment vs. number of fragments density figure, using 'pl.tsse'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas TSSe` (output of **metrics.tsse** {% icon tool %}) > 4. {% icon galaxy-eye %} Inspect a few exemplary `.png` outputs of the collection @@ -195,17 +198,17 @@ The {TSSe} distributions show that the sample quality differs substantially betw > Filtering > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Filter cell outliers based on counts and numbers of genes expressed, using 'pp.filter_cells'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas TSSe` (output of **metrics.tsse** {% icon tool %}) > - *"Minimum TSS enrichemnt score required for a cell to pass filtering"*: `7.0` > -> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 2. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Generate cell by bin count matrix, using 'pp.add_tile_matrix'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas filtered` (output of **pp.filter_cells** {% icon tool %}) > - *"The size of consecutive genomic regions used to record the counts"*: `5000` > -> 3. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 3. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas tile_matrix` (output of **pp.add_tile_matrix** {% icon tool %}) > - *"Number of features to keep"*: `50000` @@ -220,11 +223,11 @@ The {TSSe} distributions show that the sample quality differs substantially betw > > - Similarly too the *bin_size*, the *Number of Features to keep* can also impact downstream clustering. This was visualized in the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection ). > {: .details} > -> 4. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 4. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Compute probability of being a doublet using the scrublet algorithm, using 'pp.scrublet'` > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas features` (output of **pp.select_features** {% icon tool %}) > -> 5. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 5. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Remove doublets according to the doublet probability or doublet score, using 'pp.filter_doublets'` > - {% icon param-file %} *"Annotated data matrix"*: `Colon Multisample AnnDatas scrublet` (output of **pp.scrublet** {% icon tool %}) > @@ -258,7 +261,7 @@ Then, we will remove that first dataset from the collection through element iden > - {% icon param-collection %} *"Input Collection"*: `Colon Multisample AnnDatas filtered_doublets` (output of **pp.filter_doublets** {% icon tool %}) > - *"How should the elements to remove be determined?"*: `Remove if identifiers are PRESENT in file` > - {% icon param-file %} *"Filter out identifiers present in"*: `select_first` (output of **Select first** {% icon tool %}) -> 5. {% icon galaxy-pencil %} Rename the filtered collection `Colon Multisample 02-05` +> 5. {% icon galaxy-pencil %} Rename the filtered collection to `Colon Multisample 02-05` > {: .hands_on} @@ -272,7 +275,19 @@ Then, we will remove that first dataset from the collection through element iden > - {% icon param-collection %} *"Annotated data matrix to add"*: `Colon Multisample 02-05` (output of **Filter collection** {% icon tool %}) > - *"Join method"*: `Intersection of variables` > - *"Key to add the batch annotation to obs"*: `batch` -> 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData` +> +> > Issues with the concatenation +> > - Depending on the size of the datasets, this operation can take a lot of time. With the `colon_multisample` datasets, the concatenation can run for 1h. +> > - For even larger datasets, the allocated memory of the tool might not be enough and the operation fails. In that case, you will receive the following error message: +> > +> > ``` +> > Fatal error: Exit code 137 () +> > ``` +> > - In such a case, please report the issue. The administrators can then increase the memory limit and the job will succeed. +> > {% snippet faqs/galaxy/analysis_troubleshooting_reporting.md %} +> {: .details} +> +> 2. {% icon galaxy-pencil %} Rename the AnnData output to `Multisample AnnData` > 3. {% icon galaxy-eye %} Inspect the general information of `Multisample AnnData` > > > ``` @@ -305,17 +320,17 @@ Now that all samples have been concatenated into a single AnnData object, the mo > Spectral embedding > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Perform feature selection, using 'pp.select_features'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData` (output of **Manipulate AnnData** {% icon tool %}) > - *"Number of features to keep"*: `50000` > -> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.6.4+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Perform dimension reduction using Laplacian Eigenmap, using 'tl.spectral'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData features` (output of **pp.select_features** {% icon tool %}) > - *"distance metric"*: `cosine` > -> 3. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData spectral` +> 3. {% icon galaxy-pencil %} Rename the AnnData output to `Multisample AnnData spectral` > {: .hands_on} @@ -324,18 +339,18 @@ Batch effects can be visualized in an {UMAP} projection of the data. For this, t > UMAP projection without batch correction > -> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.6.4+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData spectral` (output of **tl.spectral** {% icon tool %}) > -> 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData UMAP` -> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: +> 2. {% icon galaxy-pencil %} Rename the AnnData output to `Multisample AnnData UMAP` +> 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData UMAP` (output of **tl.umap** {% icon tool %}) > - *"Color"*: `batch` > - *"Height of the plot"*: `500` > -> 4. {% icon galaxy-pencil %} Rename the generated image `spectral-UMAP-No Batch correction` +> 4. {% icon galaxy-pencil %} Rename the generated image to `spectral-UMAP-No Batch correction` > > 5. {% icon galaxy-eye %} Inspect the `.png` output > @@ -376,31 +391,31 @@ After confirming that batch effects have affected our samples, we should remove We will use *Harmony* to correct for batch effects first. The other methods will be tested afterwards. > Batch correction and visualization > -> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.5.3+galaxy2) %} with the following parameters: +> 1. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for preprocessing"*: `Use harmonypy to integrate different experiments,using 'pp.harmony'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData UMAP` (output of **tl.umap** {% icon tool %}) > -> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.6.4+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute Umap, using 'tl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony` (output of **pp.harmony** {% icon tool %}) > - *"Use the indicated representation in `.obsm`"*: `X_spectral_harmony` > - *"`adata.obs` key under which t add cluster labels"*: `umap_harmony` > -> 2. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData harmony UMAP` +> 2. {% icon galaxy-pencil %} Rename the AnnData output to `Multisample AnnData harmony UMAP` > > > Key for cluster labels > > - If you add the new *UMAP-embeddings* under the key `umap_harmony`, the non-batch corrected embeddings are still stored in the AnnData object. > > - Alternatively, by leaving this parameter empty, the old embeddings will be overwritten. > {: .comment} > -> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: +> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony UMAP` (output of **tl.umap** {% icon tool %}) > - *"Color"*: `batch` > - *"Use the indicated representation in .obsm"*: `X_umap_harmony` > - *"Height of the plot"*: `500` > -> 5. {% icon galaxy-pencil %} Rename the generated image `spectral-UMAP-harmony` +> 5. {% icon galaxy-pencil %} Rename the generated image to `spectral-UMAP-harmony` > > 6. {% icon galaxy-eye %} Inspect the `.png` output > @@ -423,37 +438,50 @@ We will use *Harmony* to correct for batch effects first. The other methods will {: .hands_on} > Batch correction with Scanorama and MNC-correct -> +> - Other batch correction methods can also be selected with the tool {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %}: *pp.mnc_correct* and *pp.scanorama_integrate* +> - For *MNC-correct* the *Number of iterations* can be specified. In order to identify the optimal algorithm, it is recommended to test the different algorithms and settings. > - Examplary outputs of the methods *Scanorama* and *MNC-correct* are shown here: -> ![Batch correction UMAP plots of MNC-correct with different settings and Scanorama]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/mnc-correct-scanorama-umap-spectral.png %} "UMAP plots of batch correction with different methods. \(a\) Batch correction with MNC-correct and default settings. \(b\) Batch correction with Scanorama \(c\) Batch correction with MNC-correct and 30 iterations.") +> ![Batch correction UMAP plots of MNC-correct with different settings and Scanorama]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/mnc-correct-scanorama-umap-spectral.png %} "UMAP plots of batch correction with different methods. (a) Batch correction with MNC-correct and default settings. (b) Batch correction with Scanorama (c) Batch correction with MNC-correct and 30 iterations.") +> +> > > +> > +> > - Compare the plots with the output of *Harmony*. Which algorithm is best-suited for the colon datasets? +> > +> > > +> > > +> > > - *Harmony* has performed the best batch integration. In that plot, the fewest single-batch groups are visible. *Scanorama* and *MNC-correct* (with the default settings) did not integrate the batches as well as *Harmony*. *MNC-correct* with 30 iterations, on the other hand, did remove a lot of batch effects and could also be used to continue the analysis. +> > > +> > {: .solution} +> {: .question} > {: .details} # Clustering of the batch corrected samples +With the analysis can now continue with the same methods, shown in the [standard pathway]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). The batch-corrected embeddings are now clustered and visualized with the *leiden* algorithm. > Leiden clustering and visualization > -> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: +> 1. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.6.4+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Compute a neighborhood graph of observations, using 'pp.knn'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony UMAP` (output of **tl.umap** {% icon tool %}) > - *"The key for the matrix"*: `X_spectral_harmony` > -> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.5.3+galaxy2) %} with the following parameters: +> 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.6.4+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Cluster cells into subgroups, using 'tl.leiden'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony knn` (output of **pp.knn** {% icon tool %}) > - *"Whether to use the Constant Potts Model (CPM) or modularity"*: `modularity` > -> 3. {% icon galaxy-pencil %} Rename the AnnData output `Multisample AnnData harmony leiden` +> 3. {% icon galaxy-pencil %} Rename the AnnData output to `Multisample AnnData harmony leiden` > -> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.5.3+galaxy2) %} with the following parameters: +> 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the UMAP embedding, using 'pl.umap'` > - {% icon param-file %} *"Annotated data matrix"*: `anndata_out` (output of **tl.leiden** {% icon tool %}) > - *"Color"*: `leiden` > - *"Use the indicated representation in .obsm"*: `X_umap_harmony` > - *"Height of the plot"*: `500` > -> 5. {% icon galaxy-pencil %} Rename the generated image `spectral-UMAP-harmony-leiden` +> 5. {% icon galaxy-pencil %} Rename the generated image to `spectral-UMAP-harmony-leiden` > > 6. {% icon galaxy-eye %} Inspect the `.png` output > @@ -474,7 +502,17 @@ We will use *Harmony* to correct for batch effects first. The other methods will > {: .hands_on} +After integrating the different datasets and clustering the cells, the scATAC-seq analysis can now continue with downstream analysis. As part of the downstream analysis, the clusters can be [annotated]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#Cell-cluster-annotation ) and differential peak analysis can be performed. +> +> +> Differential peak analysis is already available on Galaxy. However, there is no GTN training yet available. Until such a tutorial is available, you can visit the **SnapATAC2** documentation for a [tutorial on Differential peak analysis](https://kzhang.org/SnapATAC2/version/2.6/tutorials/diff.html). +> +> The tools are available in Galaxy under {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_peaks_and_motif/snapatac2_peaks_and_motif/2.6.4+galaxy1) %} +> +{: .comment} + # Conclusion +{% icon congratulations %} Well done, you’ve made it to the end! You might want to consult your results with this [control history](https://usegalaxy.eu/u/timonschlegel/h/multisample-batch-correction-with-harmony-and-snapatac2), or check out the [full workflow](https://usegalaxy.eu/u/timonschlegel/w/multisample-batch-correction-with-snapatac2-and-harmony) for this tutorial. -In this tutorial we performed batch correction on five {scATAC-seq} samples from human colons. \ No newline at end of file +In this tutorial we have integrated five {scATAC-seq} colon samples. To achieve this, we have assembled a scalable Galaxy workflow and have compared different batch integration algorithms, to identify the best-suited method for our data. Finally, we have assigned the cells into clusters, to prepare the data for downstream analysis. \ No newline at end of file diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga index 2de76e06918b8b..534110c9d8f296 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga @@ -1 +1,1675 @@ -{"a_galaxy_workflow": "true", "annotation": "This Workflow takes a dataset collection of single-cell ATAC-seq fragments and performs:\n- preprocessing\n- filtering\n- concatenation\n- dimension reduction\n- batch correction\n- leiden clustering", "comments": [{"child_steps": [23, 22, 24], "color": "none", "data": {"title": "Clustering Analysis of batch-corrected Datasets"}, "id": 6, "position": [5166.299999999999, 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9dc2e8d9c7db88b6c03feda4e267ccff79e63cf4 Mon Sep 17 00:00:00 2001 From: Timon Schlegel Date: Thu, 8 Aug 2024 10:56:38 +0000 Subject: [PATCH 09/12] fixed citation and inspect anndata --- .../tutorial.md | 106 ++++++++++-------- 1 file changed, 58 insertions(+), 48 deletions(-) diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index e9aa6d4f2cdd72..7f86b55b2ccbe6 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -7,15 +7,15 @@ priority: 3 level: Intermediate zenodo_link: https://zenodo.org/records/12683310 questions: -- Why is batch correction important when analyzing data from multiple samples? +- Why is batch correction important during the analysis of data from multiple samples? - How is batch correction performed on single cell ATAC-seq data? objectives: -- Perform batch correction on a collection of single cell ATAC-seq data -- Learn how Harmony integrates different samples +- Perform batch correction on a dataset collection of multiple single cell ATAC-seq samples. +- Learn how Harmony and other batch correction algorithms remove batch effects. time_estimation: 4H key_points: -- Batch correction is important for integration of data from multiple experiments -- How harmony works +- Batch correction is important for the integration of data from multiple experiments +- Batch correction algorithms identify similar cells and move them closer together through appropriate correction vectors. requirements: - type: "internal" @@ -43,7 +43,7 @@ gitter: Galaxy-Training-Network/galaxy-single-cell # Introduction -Performing biological experiments in replicates is a cornerstone of modern science. However, when integrating data from multiple single-cell sequencing experiments, technical confounders might impact the results. +Performing experiments in replicates is a cornerstone of modern biological science. However, when integrating data from multiple single-cell sequencing experiments, technical confounders might impact the results. To reduce technical confounders, such as different experimenters, experimental protocols, sequencing lanes or sequencing technologies, batch correction is often beneficial. In this tutorial, we will perform batch correction on five datasets of {scATAC-seq} data with the three algorithms *Harmony* ({% cite Korsunsky2019 %}), *Scanorama* ({% cite Hie2019 %}) and the *mutual nearest neighbor-based* ({% cite Haghverdi2018%}) algorithm *MNC-correct* ({% cite Zhang2024 %}). The {scATAC-seq} analysis will be performed with the tool suite [**SnapATAC2**](https://kzhang.org/SnapATAC2/version/2.5/index.html) ({% cite Zhang2024 %}). @@ -54,7 +54,7 @@ In this tutorial, we will perform batch correction on five datasets of {scATAC-s > > -> This tutorial is significantly based on ["Multi-sample Pipeline" tutorial from SnapATAC2](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html). +> This tutorial is significantly based on the ["Multi-sample Pipeline" tutorial](https://kzhang.org/SnapATAC2/version/2.5/tutorials/integration.html) from **SnapATAC2**. > > The data analysis is performed with the same tools shown in the tutorial [Single-cell ATAC-seq standard processing with SnapATAC2]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %} ). > That tutorial also explains the steps of the ATAC-seq analysis with SnapATAC2 in more detail. @@ -84,7 +84,7 @@ The five `colon_multisample` files have been generated by the **Cell Ranger ATAC > {: .details} -First we will import all datasets into Galaxy. Then we will build a dataset collection, containing all `colon_multisample` datasets. This will make the following analysis steps much simpler. +First we will import the datasets into Galaxy. Then we will build a dataset collection, containing all `colon_multisample` datasets. This will make the following analysis steps much simpler. {% snippet faqs/galaxy/histories_datasets_vs_collections.md %} @@ -131,7 +131,7 @@ First we will import all datasets into Galaxy. Then we will build a dataset coll > > {% snippet faqs/galaxy/datasets_change_datatype.md datatype="bed" %} > -> 5. Create a dataset collection with all `colon_multisample` datasets. +> 5. Create a dataset collection with all `colon_multisample` datasets and rename the collection to `Colon Multisample Fragments`. > > {% snippet faqs/galaxy/collections_build_list.md name="Colon Multisample Fragments" %} > @@ -141,7 +141,7 @@ First we will import all datasets into Galaxy. Then we will build a dataset coll With our data imported and the collection built, we can now begin the {scATAC-seq} data preprocessing with SnapATAC2. -The first step is importing the datasets into an AnnData object with the tool *pp.import_data*. Next, the {TSSe} will be calculated. The {TSS} serves as a {QC} measurement to selectively filter droplets containing high-quality cells. +The first step is importing the datasets into an AnnData object with the tool *pp.import_data*. Next, the {TSSe} will be calculated. The {TSS} serve as a {QC} measurement to selectively filter droplets containing high-quality cells. > AnnData format > @@ -163,9 +163,7 @@ The first step is importing the datasets into an AnnData object with the tool *p > - {% icon param-collection %} *"Annotated data matrix"*: `Colon Multisample AnnDatas` (dataset collection output of **pp.import_data** {% icon tool %}) > - {% icon param-file %} *"GTF/GFF file containing the gene annotation"*: `gene_annotation` (Input dataset) > -> 2. Rename the generated collection to `Colon Multisample AnnDatas TSSe` or add the tag {% icon galaxy-tags %} `TSSe` to the collection: -> -> {% snippet faqs/galaxy/collections_add_tag.md %} +> 2. Rename the generated collection to `Colon Multisample AnnDatas TSSe`. > > 3. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.6.4+galaxy1) %} with the following parameters: > - *"Method used for plotting"*: `Plot the TSS enrichment vs. number of fragments density figure, using 'pl.tsse'` @@ -217,10 +215,10 @@ The {TSSe} distributions show that the sample quality differs substantially betw > > > > - *pp.add_tile_matrix* divides the genome into a specified number of bins, depending on the bin size (f.ex. 5000bp). For each bin, the ATAC-seq reads of individual cells are checked to determine if a read is located in the bin. This is counted as a feature and stored under `n_vars` in the AnnData object. > > - Increasing the bin size greatly reduces compute time at the cost of some biological data. -> > - For this reason, the bin size 5000bp has been selected for the colon datasets. In the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}), the lower bin size of 500bp was chosen, since fewer cells were analyzed. +> > - For this reason, the bin size 5000bp has been selected for the colon datasets. In the [previous tutorial]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection), the lower bin size of 500bp was chosen, since fewer cells were analyzed. > > - *pp.select_features* uses the previously identified features to select the most accessible features for further analysis. > > - The parameter *"Number of features to keep"* determines the upper limit of features which can be selected. -> > - Similarly too the *bin_size*, the *Number of Features to keep* can also impact downstream clustering. This was visualized in the [previous tutorial] ( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection ). +> > - Similarly to the *bin_size*, the *Number of Features to keep* can also impact downstream clustering. This was demonstrated in the [previous tutorial]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#feature-selection ). > {: .details} > > 4. {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %} with the following parameters: @@ -237,13 +235,13 @@ The {TSSe} distributions show that the sample quality differs substantially betw Before we can continue with the analysis and batch correction, we need to extract the datasets out of the collection and merge them into a single AnnData object. ## Extracting datasets from a collection - -First, we will extract the first dataset out of the collection using the Galaxy tool *Extract dataset*. -Then, we will remove that first dataset from the collection through element identifiers. +This is achieved in two steps: +1. The first dataset is extracted from the collection using *Extract dataset* +2. Afterwards, the first dataset will be removed from the collection. This is done by extracting element identifiers and filtering the collection with the name of the first dataset. > > - It is also possible to manually remove a dataset from a collection. -> - However, manually removing datasets can not be implemented in Galaxy workflows. In contrast, the automatic method shown here, can be represented in a workflow and is therefore much better scalable. +> - However, manually removing datasets can not be implemented in Galaxy workflows. In contrast the automatic method, shown here, can be represented in a workflow and is therefore much better scalable. {: .comment} > Extract datasets @@ -280,16 +278,25 @@ Then, we will remove that first dataset from the collection through element iden > > - Depending on the size of the datasets, this operation can take a lot of time. With the `colon_multisample` datasets, the concatenation can run for 1h. > > - For even larger datasets, the allocated memory of the tool might not be enough and the operation fails. In that case, you will receive the following error message: > > -> > ``` +> > ``` > > Fatal error: Exit code 137 () -> > ``` +> > ``` > > - In such a case, please report the issue. The administrators can then increase the memory limit and the job will succeed. +> > > > {% snippet faqs/galaxy/analysis_troubleshooting_reporting.md %} +> > > {: .details} > > 2. {% icon galaxy-pencil %} Rename the AnnData output to `Multisample AnnData` > 3. {% icon galaxy-eye %} Inspect the general information of `Multisample AnnData` > +> > Inspecting AnnData objects +> > * Many toolsets producing outputs in *AnnData* formats in Galaxy, provide the general information by default: +> > * Click on the name of the dataset in the history to expand it. The general Anndata information will be given in the expanded box. +> > * Alternatively, expand the dataset and click on {% icon details %}*Dataset Details*. Scroll to Job Information and inspect the Tool Standard Output. +> > * If a tool does not provide the general AnnData information, or a more specific query is required, the tool {% tool [Inspect AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_inspect/anndata_inspect/0.10.3+galaxy0) %} can also be selected. +> {: .tip} +> > > ``` > > AnnData object with n_obs × n_vars = 34372 × 606219 > > obs: 'n_fragment', 'frac_dup', 'frac_mito', 'tsse', 'doublet_probability', 'doublet_score', 'batch' @@ -316,7 +323,7 @@ Then, we will remove that first dataset from the collection through element iden {: .hands_on} # Dimension Reduction -Now that all samples have been concatenated into a single AnnData object, the most accessible features of the combined count matrix must be selected. The previously selected features were only dependent on the individual samples and can not be utilized for dimensionality reduction. Therefore the most accessible features will be selected once again and then dimensionality reduction through *matrix-free spectral embedding* will be performed. +Now that all samples have been concatenated into a single AnnData object, the most accessible features of the combined count matrix must be selected. The previously selected features were dependent on the individual samples and can not be utilized for dimensionality reduction. Therefore the most accessible features will be selected once again and then the dimensionality of the data will be reduced through *matrix-free spectral embedding*. > Spectral embedding > @@ -363,7 +370,7 @@ Batch effects can be visualized in an {UMAP} projection of the data. For this, t > > > > > > > > -> > > 1. Batch effects are visible as parts of the projection where the different samples are not distributed evenly. For example the large group in the left center of the plot consists of seperate groups for each batch. It is very likely, that the differences between these groups are technical artefacts, due to batch effects and do not represent "real" biological differences between the cells of different samples. +> > > 1. Batch effects are visible as parts of the projection where the different samples are not distributed evenly. For example the large mass in the upper right corner of the plot consists of seperate groups for each batch. It is very likely, that the differences between these groups are technical artefacts, due to batch effects and do not represent "real" biological differences between the cells of different samples. > > > 2. A plot without batch effects does not exhibit groups of cells from a single sample. Instead, most parts of the projection would consist of evenly distributed cells from all samples. > > > > > {: .solution} @@ -377,13 +384,18 @@ After confirming that batch effects have affected our samples, we should remove > Batch correction algorithms > - Batch correction algorithms aim to correct the cell-by-feature count matrix against batch-specific differences between samples. -> - Batch effects can arise from many different technical sources: variation in sequencing lanes, plates, protocol, handling. Additionally, biological factors can also be regarded as batch effects, for example tissue types, species and inter-individual variation {% cite Luecken2021 % }. +> - Batch effects can arise from many different technical sources: variation in sequencing lanes, plates, protocol, handling. Additionally, biological factors can also be regarded as batch effects, for example tissue types, species and inter-individual variation {% cite Luecken2021 %}. > - Many different correction algorithms have been developed, although most methods for scATAC-seq have been adapted from scRNA-seq batch removal algorithms. -> - *Harmony* {% cite Korsunsky2019 % } is a principle component analysis (PCA)-based method which utilizes the previously generated lower-dimensional data, to assign cells into new clusters. The grouping of cells into clusters favors multi-sample clusters, in order to integrate the datasets. Linear correction factors are calculated for each batch and cluster, and the cells are moved to the corrected positions. The preceding steps are iterated, until optimal batch correction is achieved. +> - *Harmony* {% cite Korsunsky2019 %} is a principle component analysis (PCA)-based method which utilizes the previously generated lower-dimensional data, to assign cells into new clusters. The grouping of cells into clusters favors multi-sample clusters, in order to integrate the datasets. Linear correction factors are calculated for each batch and cluster, and the cells are moved to the corrected positions. The preceding steps are iterated, until optimal batch correction is achieved. +> > ![Graphical abstract of Harmony batch correction]({% link topics/single-cell/images/harmony-graphical-abstract.png %}) -> - *Scanorama* {% cite Hie2019 % } performs panorama stiching, to find and merge overlapping cell types. +> +> - *Scanorama* {% cite Hie2019 %} performs panorama stiching, to find and merge overlapping cell types. +> > ![Graphical abstract of Scanorama batch correction]({% link topics/single-cell/images/scanorama-graphical-abstract.png %}) -> - *MNC-correct* {% cite Zhang2024 % } is a modified version of a *mutual nearest neighbor* algorithm {% cite Haghverdi2018 % }. The algorithm calculates centroids for batch-specific clusters and identifies pairs of mutual nearest centroids (MNC) across batches. Correction vectors align the batches in the same plane. Additionally, *MNC-correct* can also be run iteratively, to find the most ideal corrections. +> +> - *MNC-correct* {% cite Zhang2024 %} is a modified version of a *mutual nearest neighbor* algorithm {% cite Haghverdi2018 %}. The algorithm calculates centroids for batch-specific clusters and identifies pairs of mutual nearest centroids (MNC) across batches. Correction vectors align the batches in the same plane. Additionally, *MNC-correct* can also be run iteratively, to find the most ideal corrections. +> > ![Graphical abstract of MNC-correct batch correction]({% link topics/single-cell/images/mnn-graphical-abstract.png %}) > {: .details} @@ -405,7 +417,7 @@ We will use *Harmony* to correct for batch effects first. The other methods will > > > Key for cluster labels > > - If you add the new *UMAP-embeddings* under the key `umap_harmony`, the non-batch corrected embeddings are still stored in the AnnData object. -> > - Alternatively, by leaving this parameter empty, the old embeddings will be overwritten. +> > - Alternatively, by leaving this parameter empty, the old embeddings will be written over. > {: .comment} > > 4. {% tool [SnapATAC2 Plotting](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_plotting/snapatac2_plotting/2.6.4+galaxy1) %} with the following parameters: @@ -439,13 +451,14 @@ We will use *Harmony* to correct for batch effects first. The other methods will > Batch correction with Scanorama and MNC-correct > - Other batch correction methods can also be selected with the tool {% tool [SnapATAC2 Preprocessing](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_preprocessing/snapatac2_preprocessing/2.6.4+galaxy1) %}: *pp.mnc_correct* and *pp.scanorama_integrate* -> - For *MNC-correct* the *Number of iterations* can be specified. In order to identify the optimal algorithm, it is recommended to test the different algorithms and settings. +> - For *MNC-correct* the *Number of iterations* can also be selected. +> - In order to identify the optimal algorithm, it is recommended to test the different algorithms and settings. > - Examplary outputs of the methods *Scanorama* and *MNC-correct* are shown here: > ![Batch correction UMAP plots of MNC-correct with different settings and Scanorama]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/mnc-correct-scanorama-umap-spectral.png %} "UMAP plots of batch correction with different methods. (a) Batch correction with MNC-correct and default settings. (b) Batch correction with Scanorama (c) Batch correction with MNC-correct and 30 iterations.") > -> > > +> > > > -> > - Compare the plots with the output of *Harmony*. Which algorithm is best-suited for the colon datasets? +> > - Compare these plots with the output of *Harmony*. Which algorithm is best-suited for the colon datasets? > > > > > > > > @@ -467,6 +480,16 @@ With the analysis can now continue with the same methods, shown in the [standard > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony UMAP` (output of **tl.umap** {% icon tool %}) > - *"The key for the matrix"*: `X_spectral_harmony` > +> > Key for the matrix +> > +> > - The batch correction algorithms have individual keys for the matrix. +> > - *Harmony* has the key `X_spectral_harmony` +> > - *MNC-correct* has the key `X_spectral_mnn` +> > - *Scanorama* has the key `X_spectral_scanorama` +> > - The keys are stored in the AnnData object under 'obsm' +> > +> {: .tip} +> > 2. {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_clustering/snapatac2_clustering/2.6.4+galaxy1) %} with the following parameters: > - *"Dimension reduction and Clustering"*: `Cluster cells into subgroups, using 'tl.leiden'` > - {% icon param-file %} *"Annotated data matrix"*: `Multisample AnnData harmony knn` (output of **pp.knn** {% icon tool %}) @@ -486,28 +509,15 @@ With the analysis can now continue with the same methods, shown in the [standard > 6. {% icon galaxy-eye %} Inspect the `.png` output > > ![UMAP plot of Batch-corrected leiden clusters]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/batch-umap-harmony-leiden.png %}) -> > -> > -> > 1. Question1? -> > 2. Question2? -> > -> > > -> > > -> > > 1. Answer for question1 -> > > 2. Answer for question2 -> > > -> > {: .solution} -> {: .question} -> > {: .hands_on} -After integrating the different datasets and clustering the cells, the scATAC-seq analysis can now continue with downstream analysis. As part of the downstream analysis, the clusters can be [annotated]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#Cell-cluster-annotation ) and differential peak analysis can be performed. +After integrating the different datasets and clustering the cells, the scATAC-seq analysis can now continue with downstream analysis. As part of the downstream analysis, the clusters can be [annotated]( {% link topics/single-cell/tutorials/scatac-standard-processing-snapatac2/tutorial.md %}#cell-cluster-annotation ) and differential peak analysis can be performed. > > -> Differential peak analysis is already available on Galaxy. However, there is no GTN training yet available. Until such a tutorial is available, you can visit the **SnapATAC2** documentation for a [tutorial on Differential peak analysis](https://kzhang.org/SnapATAC2/version/2.6/tutorials/diff.html). +> The **SnapATAC2** tools for differential peak analysis are already accessible on Galaxy. However, there are no GTN trainings available yet. Until such a tutorial is uploaded, you can visit the **SnapATAC2** documentation for a [tutorial on differential peak analysis](https://kzhang.org/SnapATAC2/version/2.6/tutorials/diff.html). > -> The tools are available in Galaxy under {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_peaks_and_motif/snapatac2_peaks_and_motif/2.6.4+galaxy1) %} +> The tools are available in Galaxy under {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_peaks_and_motif/snapatac2_peaks_and_motif/2.6.4+galaxy1) %}. > {: .comment} @@ -515,4 +525,4 @@ After integrating the different datasets and clustering the cells, the scATAC-se # Conclusion {% icon congratulations %} Well done, you’ve made it to the end! You might want to consult your results with this [control history](https://usegalaxy.eu/u/timonschlegel/h/multisample-batch-correction-with-harmony-and-snapatac2), or check out the [full workflow](https://usegalaxy.eu/u/timonschlegel/w/multisample-batch-correction-with-snapatac2-and-harmony) for this tutorial. -In this tutorial we have integrated five {scATAC-seq} colon samples. To achieve this, we have assembled a scalable Galaxy workflow and have compared different batch integration algorithms, to identify the best-suited method for our data. Finally, we have assigned the cells into clusters, to prepare the data for downstream analysis. \ No newline at end of file +In this tutorial we have integrated five {scATAC-seq} colon samples. To achieve this, we have assembled a well-scalable Galaxy workflow and have compared different batch integration algorithms, to identify the best-suited method for our data. Finally, we have assigned the cells into clusters, to prepare the data for downstream analysis. \ No newline at end of file From 31985e00f97fc7010667a9d9ed10125a72010295 Mon Sep 17 00:00:00 2001 From: timonschlegel Date: Thu, 8 Aug 2024 17:20:46 +0200 Subject: [PATCH 10/12] correct tool name for snapatac2 peaks and motif --- .../tutorials/scatac-batch-correction-snapatac2/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index 7f86b55b2ccbe6..e3fdec323d12b7 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -517,7 +517,7 @@ After integrating the different datasets and clustering the cells, the scATAC-se > > The **SnapATAC2** tools for differential peak analysis are already accessible on Galaxy. However, there are no GTN trainings available yet. Until such a tutorial is uploaded, you can visit the **SnapATAC2** documentation for a [tutorial on differential peak analysis](https://kzhang.org/SnapATAC2/version/2.6/tutorials/diff.html). > -> The tools are available in Galaxy under {% tool [SnapATAC2 Clustering](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_peaks_and_motif/snapatac2_peaks_and_motif/2.6.4+galaxy1) %}. +> The tools are available in Galaxy under {% tool [SnapATAC2 Peaks and Motif](toolshed.g2.bx.psu.edu/repos/iuc/snapatac2_peaks_and_motif/snapatac2_peaks_and_motif/2.6.4+galaxy1) %}. > {: .comment} @@ -525,4 +525,4 @@ After integrating the different datasets and clustering the cells, the scATAC-se # Conclusion {% icon congratulations %} Well done, you’ve made it to the end! You might want to consult your results with this [control history](https://usegalaxy.eu/u/timonschlegel/h/multisample-batch-correction-with-harmony-and-snapatac2), or check out the [full workflow](https://usegalaxy.eu/u/timonschlegel/w/multisample-batch-correction-with-snapatac2-and-harmony) for this tutorial. -In this tutorial we have integrated five {scATAC-seq} colon samples. To achieve this, we have assembled a well-scalable Galaxy workflow and have compared different batch integration algorithms, to identify the best-suited method for our data. Finally, we have assigned the cells into clusters, to prepare the data for downstream analysis. \ No newline at end of file +In this tutorial we have integrated five {scATAC-seq} colon samples. To achieve this, we have assembled a well-scalable Galaxy workflow and have compared different batch integration algorithms, to identify the best-suited method for our data. Finally, we have assigned the cells into clusters, to prepare the data for downstream analysis. 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[PATCH 12/12] fixed technical issues --- .../tutorial.md | 2 +- .../workflows/main_workflow.ga | 160 ++++++++++-------- 2 files changed, 86 insertions(+), 76 deletions(-) diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md index d209f6c63443dd..3aefe85685c0f4 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/tutorial.md @@ -388,7 +388,7 @@ After confirming that batch effects have affected our samples, we should remove > - Many different correction algorithms have been developed, although most methods for scATAC-seq have been adapted from scRNA-seq batch removal algorithms. > - *Harmony* {% cite Korsunsky2019 %} is a principle component analysis (PCA)-based method which utilizes the previously generated lower-dimensional data, to assign cells into new clusters. The grouping of cells into clusters favors multi-sample clusters, in order to integrate the datasets. Linear correction factors are calculated for each batch and cluster, and the cells are moved to the corrected positions. The preceding steps are iterated, until optimal batch correction is achieved. > -> ![Graphical abstract of Harmony batch correction]({% link topics/single-cell/images/harmony-graphical-abstract.png %}) +> ![Graphical abstract of Harmony batch correction]({% link topics/single-cell/images/scatac-batch-correction-snapatac2/harmony-graphical-abstract.png %}) > > - *Scanorama* {% cite Hie2019 %} performs panorama stiching, to find and merge overlapping cell types. > diff --git a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga index 534110c9d8f296..0c6c3a4163a2cf 100644 --- a/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga +++ b/topics/single-cell/tutorials/scatac-batch-correction-snapatac2/workflows/main_workflow.ga @@ -4,43 +4,22 @@ "comments": [ { "child_steps": [ - 20, - 26, - 23 - ], - "color": "none", - "data": { - "title": "Batch correction with Harmony" - }, - "id": 5, - "position": [ - 4442.599999999999, - 306.9 - ], - "size": [ - 692.7, - 554.2 - ], - "type": "frame" - }, - { - "child_steps": [ - 2, - 1, - 0 + 30, + 27, + 31 ], "color": "none", "data": { - "title": "Inputs" + "title": "Clustering Analysis of batch-corrected Datasets" }, - "id": 0, + "id": 7, "position": [ - 0, - 380.19999999999993 + 5166.299999999999, + 309.79999999999995 ], "size": [ - 332, - 457 + 892, + 559 ], "type": "frame" }, @@ -70,58 +49,72 @@ }, { "child_steps": [ - 30, - 27, - 31 + 20, + 26, + 23 ], "color": "none", "data": { - "title": "Clustering Analysis of batch-corrected Datasets" + "title": "Batch correction with Harmony" }, - "id": 7, + "id": 5, "position": [ - 5166.299999999999, - 309.79999999999995 + 4442.599999999999, + 306.9 ], "size": [ - 892, - 559 + 692.7, + 554.2 ], "type": "frame" }, { "child_steps": [ - 5, - 4, - 3, - 6, - 7, - 8, - 9, - 10 + 18, + 19 ], "color": "none", "data": { - "title": "Preprocessing and Filtering" + "title": "No Batch Correction Control" }, - "id": 1, + "id": 4, "position": [ - 406.5, - 309.9 + 4102.9, + 0 ], "size": [ - 1738, - 604 + 569, + 249.1 + ], + "type": "frame" + }, + { + "child_steps": [ + 16, + 17 + ], + "color": "none", + "data": { + "title": "Dimension Reduction" + }, + "id": 3, + "position": [ + 3533.4, + 319.69999999999993 + ], + "size": [ + 691.1, + 572.3 ], "type": "frame" }, { "child_steps": [ - 13, - 15, 12, 14, - 11 + 11, + 13, + 15 ], "color": "none", "data": { @@ -140,46 +133,61 @@ }, { "child_steps": [ - 16, - 17 + 5, + 4, + 3, + 6, + 7, + 8, + 9, + 10 ], "color": "none", "data": { - "title": "Dimension Reduction" + "title": "Preprocessing and Filtering" }, - "id": 3, + "id": 1, "position": [ - 3533.4, - 319.69999999999993 + 406.5, + 309.9 ], "size": [ - 691.1, - 572.3 + 1738, + 604 ], "type": "frame" }, { "child_steps": [ - 18, - 19 + 0, + 1, + 2 ], "color": "none", "data": { - "title": "No Batch Correction Control" + "title": "Inputs" }, - "id": 4, + "id": 0, "position": [ - 4102.9, - 0 + 0, + 380.19999999999993 ], "size": [ - 569, - 249.1 + 332, + 457 ], "type": "frame" } ], + "creator": [ + { + "class": "Person", + "identifier": "0009-0001-3228-105X", + "name": "Timon Schlegel" + } + ], "format-version": "0.1", + "license": "CC-BY-4.0", "name": "Multisample Batch Correction with SnapATAC2 and Harmony", "report": { "markdown": "\n# Workflow Execution Report\n\n## Workflow Inputs\n```galaxy\ninvocation_inputs()\n```\n\n## Workflow Outputs\n```galaxy\ninvocation_outputs()\n```\n\n## Workflow\n```galaxy\nworkflow_display()\n```\n" @@ -1668,8 +1676,10 @@ } }, "tags": [ - "workflowhub" + "epigenetics", + "scATAC-seq", + "name:single-cell" ], - "uuid": "d7978271-a00a-41f3-b033-d9cbd857f30e", - "version": 28 + "uuid": "f7e4ef27-75ed-46b8-a3fe-3de91d2ff948", + "version": 30 } \ No newline at end of file

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