Merge branch 'main' into zebu-bicolor

_config.yml

@@ -164,6 +164,7 @@ icon-tag:
   param-select: fas fa-filter
   param-text: fas fa-pencil-alt
   param-toggle: fas fa-toggle-on
+  point-right: fa fa-hand-o-right
   pref-info: fas fa-user
   pref-password: fas fa-unlock-alt
   pref-identities: far fa-id-card-o

topics/genome-annotation/tutorials/crispr-screen/data-library.yaml

@@ -13,43 +13,43 @@ items:
     - name: 'DOI: 10.5281/zenodo.5750854'
       description: latest
       items:
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/adapters_list.tsv
+      - url: https://zenodo.org/records/5750854/files/adapter_list.tsv
         src: url
         ext: tsv
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/brunello.tsv
+      - url: https://zenodo.org/records/5750854/files/brunello.tsv
         src: url
         ext: tsv
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/h.all.v7.4.symbols.gmt
+      - url: https://zenodo.org/records/5750854/files/h.all.v7.4.symbols.gmt
         src: url
         ext: tsv
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/kenji_mageck_count_summary.tsv
+      - url: https://zenodo.org/records/5750854/files/kenji_mageck_count_summary.tsv
         src: url
         ext: tsv
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/kenji_mageck_count_report.pdf
+      - url: https://zenodo.org/records/5750854/files/kenji_mageck_count_report.pdf
         src: url
         ext: tsv
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/kenji_mageck_sgrna_counts.tsv
+      - url: https://zenodo.org/records/5750854/files/kenji_mageck_sgrna_counts.tsv
         src: url
         ext: tsv
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/kenji_mageck_mle_design_matrix.tsv
+      - url: https://zenodo.org/records/5750854/files/kenji_mageck_mle_design_matrix.tsv
         src: url
         ext: tsv
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/T0-Control.fastq.gz
+      - url: https://zenodo.org/records/5750854/files/T0-Control.fastq.gz
         src: url
         ext: gz
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/T8-APR-246.fastq.gz
+      - url: https://zenodo.org/records/5750854/files/T8-APR-246.fastq.gz
         src: url
         ext: gz
         info: https://zenodo.org/record/5750854
-      - url: https://zenodo.org/api/files/6599878c-f569-41bf-a37a-2c6f3d2e67f9/T8-Vehicle.fastq.gz
+      - url: https://zenodo.org/records/5750854/files/T8-Vehicle.fastq.gz
         src: url
         ext: gz
         info: https://zenodo.org/record/5750854

topics/genome-annotation/tutorials/crispr-screen/tutorial.md

@@ -78,9 +78,9 @@ Here we will demonstrate analysing {CRISPR} screen using data from {% cite Fujih
 >    - Copy the following tabular data, paste it into the textbox and press <kbd>Build</kbd>
 >
 >      ```
->      T0-Control https://zenodo.org/record/5750854/files/T0-Control.fastq.gz
->      T8-APR-246 https://zenodo.org/record/5750854/files/T8-APR-246.fastq.gz
->      T8-Vehicle https://zenodo.org/record/5750854/files/T8-Vehicle.fastq.gz
+>      T0-Control https://zenodo.org/records/5750854/files/T0-Control.fastq.gz
+>      T8-APR-246 https://zenodo.org/records/5750854/files/T8-APR-246.fastq.gz
+>      T8-Vehicle https://zenodo.org/records/5750854/files/T8-Vehicle.fastq.gz
 >      ```
 >
 >    ![Rule-based Uploader](../../images/crispr-screen/crispr_rule_uploader.png)
@@ -118,7 +118,7 @@ With CRISPR screens we expect adapter sequence to be present, surrounding the gu
 >
 > 1. Import the adapters file from [Zenodo]({{ page.zenodo_link }}) or the Shared Data library (if available):
 >    ```
->    https://zenodo.org/record/5750854/files/adapter_list.tsv
+>    https://zenodo.org/records/5750854/files/adapter_list.tsv
 >    ```
 >    {% snippet faqs/galaxy/datasets_import_via_link.md %}
 >
@@ -246,7 +246,7 @@ To count how many guides we have for each gene, we need a library file that tell
 > <hands-on-title>Count guides per gene</hands-on-title>
 > 1. Import the sgRNA library file
 >    ```
->    https://zenodo.org/record/5750854/files/brunello.tsv
+>    https://zenodo.org/records/5750854/files/brunello.tsv
 >    ```
 >
 > 2. {% tool [MAGeCK count](toolshed.g2.bx.psu.edu/repos/iuc/mageck_count/mageck_count/0.5.9.2.4) %} with the following parameters:
@@ -259,9 +259,9 @@ To count how many guides we have for each gene, we need a library file that tell
 >
 > 3. We have been using 1% of reads from the samples. Import the MAGeCK count files (sgRNA counts, counts summary and plots pdf) for the full dataset so you can see what results for a real dataset looks like.
 >    ```
->    https://zenodo.org/record/5750854/files/kenji_mageck_sgrna_counts.tsv
->    https://zenodo.org/record/5750854/files/kenji_mageck_count_summary.tsv
->    https://zenodo.org/record/5750854/files/kenji_mageck_count_report.pdf
+>    https://zenodo.org/records/5750854/files/kenji_mageck_sgrna_counts.tsv
+>    https://zenodo.org/records/5750854/files/kenji_mageck_count_summary.tsv
+>    https://zenodo.org/records/5750854/files/kenji_mageck_count_report.pdf
 >    ```
 >
 {: .hands_on}

topics/single-cell/faqs/dimension_reduction.md

@@ -3,7 +3,7 @@ redirect_from:
 - /topics/transcriptomics/faqs/dimension_reduction
 
 title: Why do we do dimension reduction and then clustering? Why not just cluster on the actual data?
-area: gene
+area: Analysis
 box_type: tip
 layout: faq
 contributors: [rahmot]

topics/single-cell/faqs/gene_profile.md

@@ -3,7 +3,7 @@ redirect_from:
 - /topics/transcriptomics/faqs/gene_profile
 
 title: What exactly is a ‘Gene profile’?
-area: gene
+area: Interpretation
 box_type: tip
 layout: faq
 contributors: [rahmot]

topics/single-cell/faqs/notebook_warning.md

@@ -0,0 +1,9 @@
+---
+title: Notebook-based tutorials can give different outputs
+area: Analysis
+box_type: warning
+layout: faq
+contributors: [hexhowells, nomadscientist]
+---
+
+The nature of coding pulls the most recent tools to perform tasks. This can - and often does - change the outputs of an analysis. Be prepared, as you are unlikely to get outputs identical to a tutorial if you are running it in a programming environment like a Jupyter Notebook or R-Studio. That's ok! The outputs should still be pretty close.

topics/single-cell/faqs/techniques.md

@@ -3,7 +3,7 @@ redirect_from:
 - /topics/transcriptomics/faqs/techniques
 
 title: Can RNA-seq techniques be applied to scRNA-seq?
-area: Single-Cell RNA
+area: Analysis
 box_type: tip
 layout: faq
 contributors: [nomadscientist,mtekman,rahmot]

topics/single-cell/faqs/umi.md

@@ -3,7 +3,7 @@ redirect_from:
 - /topics/transcriptomics/faqs/umi
 
 title: Are UMIs not actually unique?
-area: Single-Cell RNA
+area: Analysis
 box_type: tip
 layout: faq
 contributors: [nomadscientist,mtekman]

topics/single-cell/faqs/variable_genes.md

@@ -3,7 +3,7 @@ redirect_from:
 - /topics/transcriptomics/faqs/variable_genes
 
 title: Why do we only consider highly variable genes?
-area: gene
+area: Analysis
 box_type: tip
 layout: faq
 contributors: [rahmot]

topics/single-cell/index.md

@@ -13,6 +13,6 @@ Check out workflows shared by users like you!
 
 If you want to help us behind the scenes, from testing workflows and tutorials to building tools, join our Galaxy Single Cell Community of Practice!
 
- - <i class="fa fa-hand-o-right" aria-hidden="true"></i> [Community of Practice](https://galaxyproject.org/projects/singlecell/)
- - <i class="fa fa-comments-o" aria-hidden="true"></i> [Matrix Chat Forum](https://matrix.to/#/#usegalaxy-eu_single-cell-workflows:gitter.im)
- - <i class="fa fa-envelope" aria-hidden="true"> </i> [Mailing List](https://lists.galaxyproject.org/lists/single-cell-cop.lists.galaxyproject.org/)
+ - {% icon point-right %}  [Community of Practice](https://galaxyproject.org/projects/singlecell/)
+ - {% icon feedback %}  [Matrix Chat Forum](https://matrix.to/#/#usegalaxy-eu_single-cell-workflows:gitter.im)
+ - {% icon email %}  [Mailing List](https://lists.galaxyproject.org/lists/single-cell-cop.lists.galaxyproject.org/)

topics/single-cell/tutorials/scrna-case-jupyter_basic-pipeline/tutorial.md

@@ -50,9 +50,8 @@ notebook:
   snippet: topics/single-cell/tutorials/scrna-case-jupyter_basic-pipeline/preamble.md
 
 ---
-> <warning-title>Remember: Notebook-based tutorials can give different outputs!</warning-title>
-> The nature of coding pulls the most recent tools to perform tasks. This can - and often does - change the outputs of an analysis. Be prepared, as you are unlikely to get outputs identical to this tutorial. That's ok! The outputs should still be pretty close (the basic interpretation has survived 5 years of analytical updates and counting...).
-{: .warning}
+
+{% snippet topics/single-cell/faqs/notebook_warning.md %}
 
 # Install libraries
 

topics/single-cell/tutorials/scrna-case_FilterPlotandExploreRStudio/preamble.md

@@ -55,17 +55,8 @@ This also alleviates the necessity to convert the AnnData object into a Seurat o
 >
 {: .hands_on}
 
-# Important tips for easier analysis
-
-{% snippet faqs/galaxy/tutorial_mode.md %}
-
-> <comment-title></comment-title>
-> - The Galaxy tool search panel sometimes doesn't find the tools we need from the thousands available.
-> - You'll have a much easier time selecting tools from the panel (if you aren't using tutorial mode!) if you are on the [https://humancellatlas.usegalaxy.eu](https://humancellatlas.usegalaxy.eu)
-{: .comment}
-
 # Open RStudio in Galaxy
-You now should have imported the matrix.mtx, genes.tsv, barcodes.tsv, and exp_design.tsv files into your Galaxy history. For the rest of the workflow, let's move onto RStudio and get coding!
+You now should have imported the `matrix.mtx`, `genes.tsv`, `barcodes.tsv`, and `exp_design.tsv` files into your Galaxy history. For the rest of the workflow, let's move onto RStudio and get coding!
 > <hands-on-title>Open RStudio in Galaxy</hands-on-title>
 > Run {% tool [RStudio](interactive_tool_rstudio)%}
 {: .hands_on}

topics/single-cell/tutorials/scrna-case_FilterPlotandExploreRStudio/tutorial.md

@@ -55,6 +55,8 @@ notebook:
   snippet: topics/single-cell/tutorials/scrna-case_FilterPlotandExploreRStudio/preamble.md
 ---
 
+{% snippet topics/single-cell/faqs/notebook_warning.md %}
+
 # Setting your environment
 First thing's first, we need to load the packages we will be using. In order to use any functions of a package, we must first call the library of that package. In your console (likely in the lower left corner of your RStudio window), run the following lines of code to do so:
 

topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/preamble.md

@@ -52,15 +52,6 @@ We've provided you with experimental data to analyse from a mouse dataset of fet
 >
 {: .hands_on}
 
-# Important tips for easier analysis
-
-{% snippet faqs/galaxy/tutorial_mode.md %}
-
-> <comment-title></comment-title>
-> - The Galaxy tool search panel sometimes doesn't find the tools we need from the thousands available.
-> - You'll have a much easier time selecting tools from the panel (if you aren't using tutorial mode!) if you are on the [https://humancellatlas.usegalaxy.eu](https://humancellatlas.usegalaxy.eu)
-{: .comment}
-
 ## Filtering for T-cells
 
 One problem with our current dataset is that it's not just T-cells: we found in the previous tutorial that it also contains macrophages. This is a problem, because trajectory analysis will generally try to find relationships between all the cells in the sample. We need to remove those cell types to analyse the trajectory.
@@ -120,7 +111,7 @@ You have two options for how to proceed with this tutorial - either you download
 >
 > 1. Open a Terminal in JupyterLab with File -> New -> Terminal
 >
-> 2. Run 
+> 2. Run
 >    ```
 >    wget {{ ipynbpath }}
 >    ```

topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md

@@ -52,6 +52,8 @@ notebook:
 
 From now on, you can view this tutorial in the Jupyter notebook, which will allow you to read the material and simultaneously execute the code cells! You may have to change certain numbers in the code blocks, so do read carefully. The tutorial is adapted from the [Scanpy Trajectory inference tutorial](https://scanpy-tutorials.readthedocs.io/en/latest/paga-paul15.html).
 
+{% snippet topics/single-cell/faqs/notebook_warning.md %}
+
 ## Install modules & activate them
 
 ```python

topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md

@@ -127,8 +127,6 @@ Inspect the {% icon galaxy-eye %} `Experimental Design` text file. This shows yo
 
 ## Concatenating objects
 
-{% snippet faqs/galaxy/tutorial_mode.md %}
-
 > <hands-on-title>Concatenating AnnData objects</hands-on-title>
 >
 > 1. {% tool [Manipulate AnnData](toolshed.g2.bx.psu.edu/repos/iuc/anndata_manipulate/anndata_manipulate/0.7.5+galaxy1) %} with the following parameters:

topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md

@@ -118,8 +118,6 @@ You can access the data for this tutorial in multiple ways:
 
 You have generated an annotated AnnData object from your raw scRNA-seq fastq files. However, you have only completed a 'rough' filter of your dataset - there will still be a number of 'cells' that are actually just background from empty droplets or simply low-quality. There will also be genes that could be sequencing artifacts or that appear with such low frequency that statistical tools will fail to analyse them. This background garbage of both cells and genes not only makes it harder to distinguish real biological information from the noise, but also makes it computationally heavy to analyse. These spurious reads take a lot of computational power to analyse! First on our agenda is to filter this matrix to give us cleaner data to extract meaningful insight from, and to allow faster analysis.
 
-{% snippet faqs/galaxy/tutorial_mode.md %}
-
 > <question-title></question-title>
 >
 > 1. What information is stored in your AnnData object? The last tool to generate this object counted the mitochondrial associated genes in your matrix. Where is that data stored?

topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md

@@ -56,6 +56,8 @@ notebook:
   snippet: topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md
 ---
 
+{% snippet topics/single-cell/faqs/notebook_warning.md %}
+
 ## Setting up the environment and file upload
 Once the installation is done, we should load the needed packages into our notebook. Navigate back to your `notebook`. If you are using our prepopulated notebook, you can follow the tutorial from there. Otherwise, input the following into your fresh notebook.
 

topics/statistics/tutorials/FNN/tutorial.md

@@ -339,7 +339,7 @@ dataset has 723 training examples, and our test dataset has 242 test examples. I
 
 > <hands-on-title>Model config</hands-on-title>
 >
-> - {% tool [Create a deep learning model architecture](toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/0.5.0) %}
+> - {% tool [Create a deep learning model architecture](toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_config/keras_model_config/1.0.10.0) %}
 >    - *"Select keras model type"*: `sequential`
 >    - *"input_shape"*: `(5,)`
 >    - In *"LAYER"*:
@@ -366,7 +366,7 @@ layers use ReLU activation function. The model config can be downloaded as a JSO
 
 > <hands-on-title>Model builder (Optimizer, loss function, and fit parameters)</hands-on-title>
 >
-> - {% tool [Create deep learning model](toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_builder/keras_model_builder/0.5.0) %}
+> - {% tool [Create deep learning model](toolshed.g2.bx.psu.edu/repos/bgruening/keras_model_builder/keras_model_builder/1.0.10.0) %}
 >    - *"Choose a building mode"*: `Build a training model`
 >    - *"Select the dataset containing model configuration"*: Select the *Keras Model Config* from the previous step.
 >    - *"Do classification or regression?"*: `KerasGRegressor`
@@ -391,7 +391,7 @@ batch_size decides the size of this subset (which we set to 50). The model build
 
 > <hands-on-title>Training the model</hands-on-title>
 >
-> - {% tool [Deep learning training and evaluation](toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.8.3) %}
+> - {% tool [Deep learning training and evaluation](toolshed.g2.bx.psu.edu/repos/bgruening/keras_train_and_eval/keras_train_and_eval/1.0.10.0) %}
 >    - *"Select a scheme"*: `Train and Validate`
 >    - *"Choose the dataset containing pipeline/estimator object"*: Select the *Keras Model Builder* from the previous step.
 >    - *"Select input type:"*: `tabular data`
@@ -406,14 +406,13 @@ batch_size decides the size of this subset (which we set to 50). The model build
 >
 {: .hands_on}
 
-The training step generates 3 datasets. 1) accuracy of the trained model, 2) the trained model, downloadable as a zip file, and 3) the trained
-model weights, downloadable as an hdf5 file. These files are needed for prediction in the next step.
+The training step generates 2 datasets. 1) accuracy of the trained model, 2) the trained model, in h5mlm format. These files are needed for prediction in the next step.
 
 ## Model Prediction
 
 > <hands-on-title>Testing the model</hands-on-title>
 >
-> - {% tool [Model Prediction](toolshed.g2.bx.psu.edu/repos/bgruening/model_prediction/model_prediction/1.0.8.3) %}
+> - {% tool [Model Prediction](toolshed.g2.bx.psu.edu/repos/bgruening/model_prediction/model_prediction/1.0.10.0) %}
 >    - *"Choose the dataset containing pipeline/estimator object"* : Select the trained model from the previous step.
 >    - *"Choose the dataset containing weights for the estimator above"* : Select the trained model weights from the previous step.
 >    - *"Select invocation method"*: `predict`