Merge pull request #4424 from nomadscientist/faq_common_sc_text

Faq common sc text
galaxyproject · Oct 17, 2023 · 4427b4b · 4427b4b
2 parents 373c662 + 3151c00
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diff --git a/_config.yml b/_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

diff --git a/topics/single-cell/faqs/dimension_reduction.md b/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]

diff --git a/topics/single-cell/faqs/gene_profile.md b/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]

diff --git a/topics/single-cell/faqs/notebook_warning.md b/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.
diff --git a/topics/single-cell/faqs/techniques.md b/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]

diff --git a/topics/single-cell/faqs/umi.md b/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]

diff --git a/topics/single-cell/faqs/variable_genes.md b/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]

diff --git a/topics/single-cell/index.md b/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/)
diff --git a/topics/single-cell/tutorials/scrna-case-jupyter_basic-pipeline/tutorial.md b/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
 

diff --git a/topics/single-cell/tutorials/scrna-case_FilterPlotandExploreRStudio/preamble.md b/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}

diff --git a/topics/single-cell/tutorials/scrna-case_FilterPlotandExploreRStudio/tutorial.md b/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:
 

diff --git a/topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/preamble.md b/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 }}
 >    ```

diff --git a/topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md b/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

diff --git a/topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md b/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:

diff --git a/topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md b/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?

diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/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.