Merge pull request galaxyproject#4357 from galaxyproject/nomadscienti…

…st-patch-1-1

Minor change

topics/single-cell/metadata.yaml

@@ -18,22 +18,22 @@ editorial_board:
 subtopics:
   - id: scintroduction
     title: "Introduction"
-    description: "Start here if you are new to single cell analysis in Galaxy"
+    description: "Start here if you are new to single cell analysis in Galaxy and want to learn the concepts."
   - id: firstsc
     title: "Your first analysis"
-    description: "Start here if you are new to single cell analysis in Galaxy"
+    description: "Start here if you are new to single cell analysis in Galaxy and want to try analysing data."
   - id: single-cell-CS
     title: "Case study"
     description: "These tutorials take you from raw scRNA sequencing reads to inferred trajectories to replicate a published analysis. The data is messy. The decisions are tough. The interpretation is meaningful. Come here to advance your single cell skills! Note that you get two options for inferring trajectories."
   - id: single-cell-CS-code
     title: "Case study: Reloaded"
     description: "These tutorials let you follow the same case study analysis of real, messy data but in a programming environment, hosted on Galaxy. So if you want more flexibility, but the same guided steps as the Case Study, you can skip the Case Study and start here instead. Alternatively, try these after completing the Case Study for an easier jump to a coding environment."
-  - id: deconvo
-    title: "Deconvolution"
-    description: "These tutorials infer cell compositions from bulk RNA-seq data using a scRNA-seq reference"
   - id: end-to-end
     title: "End-to-end scRNA-seq Analyses"
     description: "These tutorials use different methods to analyse scRNA-seq samples"
+  - id: deconvo
+    title: "Deconvolution"
+    description: "These tutorials infer cell compositions from bulk RNA-seq data using a scRNA-seq reference"
   - id: scmultiomics
     title: "Multiomic Analyses"
     description: "This section lets you build on mere scRNA analyses into a multiomic future!"

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

@@ -1,6 +1,6 @@
 # Introduction
 
-This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch to R. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started!
+This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch to R. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started!
 
 > <comment-title></comment-title>
 > This tutorial is significantly based on the [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/introduction/).
@@ -14,7 +14,7 @@ First, we need to retrieve the appropriate data. We will continue to work on the
 > <hands-on-title>Optional data upload into Galaxy history</hands-on-title>
 >
 > You have three options for importing the input data into a Galaxy history.
-> 
+>
 > 1. You can import a history from: [input history](https://usegalaxy.eu/u/wendi.bacon.training/h/cs4trajectories--monocle3--rstudio---input); Import the files from [Zenodo]({{ page.zenodo_link }}); or Import the files from the shared data library (`GTN - Material` -> `{{ page.topic_name }}`
 >     -> `{{ page.title }}`):
 >

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

@@ -57,7 +57,7 @@ contributions:
 
 This tutorial is a follow-up to the ['Single-cell RNA-seq: Case Study']({% link topics/single-cell/index.md %}). We will use the same sample from the previous tutorials. If you haven’t done them yet, it’s highly recommended that you go through them to get an idea how to [prepare a single cell matrix]({% link topics/single-cell/tutorials/scrna-case_alevin/tutorial.md %}), [combine datasets]({% link topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md %}) and [filter, plot and process scRNA-seq data]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) to get the data in the form we’ll be working on today.
 
-In this tutorial we will perform trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/). You can find out more about the theory behind trajectory analysis in our [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). We have already analysed the trajectory of our sample using the ScanPy toolkit in another tutorial: [Trajectory Analysis using Python (Jupyter Notebook) in Galaxy]({% link topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md %}). However, trajectory analysis is quite sensitive and some methods work better for specific datasets. In this tutorial, you will perform the same steps but using a different method for inferring trajectories. You will then compare the results, usability and outcomes! Sounds exciting, let’s dive into that!
+In this tutorial we will perform trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/). You can find out more about the theory behind trajectory analysis in our [slide deck]({% link topics/single-cell/tutorials/scrna-trajectories/slides.html %}). We have already analysed the trajectory of our sample using the ScanPy toolkit in another tutorial: [Trajectory Analysis using Python (Jupyter Notebook) in Galaxy]({% link topics/single-cell/tutorials/scrna-case_JUPYTER-trajectories/tutorial.md %}). However, trajectory analysis is quite sensitive and some methods work better for specific datasets. In this tutorial, you will perform the same steps but using a different method for inferring trajectories. You will then compare the results, usability and outcomes! Sounds exciting, let’s dive into that!
 
 {% snippet faqs/galaxy/tutorial_mode.md %}
 
@@ -621,7 +621,7 @@ Here we used a priori knowledge regarding the marker genes. If we wanted to appr
 >
 > > <solution-title></solution-title>
 > >
-> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs](https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.html#findmarkers). These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three!
+> > By looking at the table, you might give the 5 top gene IDs expressed in DP-M1. To save you some time and make the analysis more readable, we converted the gene IDs to gene names and they are as follows: Rps17, Rpl41, Rps26, Rps29, Rps28. They are all ribosomal! [You can do this yourself if you want by following this section of a previous tutorial that [uses the gene names in one object to add to a table of Ensembl IDs]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}#findmarkers). These ribosomal differences might be due to housekeeping background, cell cycling, or even something more bioligically interesting...or all three!
 > > The plot also indicates other specifically expressed genes, such as Hmgb2, Pclaf, Rpl13, Rps19, Ybx1, Ncl, Hsp90ab1, Npm1.
 > >
 > > Whenever you want to explore what might be the function of a particular cluster or why it branches out from the trajectory, check the top markers for that cluster to draw biological conclusions. Thank you Maths!

topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html → topics/single-cell/tutorials/scrna-trajectories/slides.html

@@ -17,7 +17,9 @@
 redirect_from:
   - /topics/transcriptomics/tutorials/scrna-case_monocle3-trajectories/slides
 
-subtopic: single-cell-CS
+subtopic: scintroduction
+priority: 4
+
 key_points:
   - "Trajectory analysis in pseudotime is a powerful way to get insight into the differentiation and development of cells."
   - "There are multiple methods and algorithms used in trajectory analysis and depending on the dataset, some might work better than others."
@@ -509,7 +511,7 @@
 
 - Monocle3 in RStudio (coming soon)
 
-<!--- 
+<!---
 ([Trajectory Analysis: Monocle3 in RStudio]({% link topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md %}))
 -->