Merge pull request #5474 from nomadscientist/reloaded_trajectory

update learning pathways for seurat and scanpy

learning-pathways/beyond_single_cell.md

@@ -19,27 +19,33 @@ description: |
   For support throughout these tutorials, join our Galaxy [single cell chat group on Matrix](https://matrix.to/#/#Galaxy-Training-Network_galaxy-single-cell:gitter.im) to ask questions!
 
 pathway:
-  - section: "Module 1: Case study"
+  - section: "Module 1: Preparing the dataset"
     description: |
-      These tutorials take you from raw scRNA sequencing reads to cell cluster plots to replicate a published analysis.
+      This tutorial takes you from the large files containing raw scRNA sequencing reads to a smaller, combined cell matrix.
     tutorials:
       - name: scrna-case_alevin
         topic: single-cell
       - name: scrna-case_alevin-combine-datasets
         topic: single-cell
+  - section: "Module 2: Generating cluster plots"
+    description: |
+      These tutorials take you from the pre-processed matrix to cluster plots and gene expression values. You can pick whether to follow the Scanpy or Seurat tutorials - they will accomplish the same thing and generate the same results, so follow whichever you prefer!
+    tutorials:
       - name: scrna-case_basic-pipeline
         topic: single-cell
+      - name: scrna-case_FilterPlotandExplore_SeuratTools
+        topic: single-cell
 
-  - section: "Module 2: Inferring trajectories"
+  - section: "Module 3: Inferring trajectories"
     description: |
-      This isn't strictly necessary, but if you want to infer trajectories - pseudotime relationships between cells - you can try out these tutorials with the same dataset.  Note that you get two options for inferring trajectories, you can choose either.
+      This isn't strictly necessary, but if you want to infer trajectories - pseudotime relationships between cells - you can try out these tutorials with the same dataset.  Again, you get two options for inferring trajectories, and you can choose either.
     tutorials:
       - name: scrna-case_trajectories
         topic: single-cell
       - name: scrna-case_monocle3-trajectories
         topic: single-cell
 
-  - section: "Module 3: Moving into coding environments"
+  - section: "Module 4: Moving into coding environments"
     description: |
       Did you know Galaxy can host coding environments? They don't have the same level of computational power as the easy-to-use Galaxy tools, but you can unlock the full freedom in your data analysis. You can install your favourite single-cell tool suite that is not available on Galaxy, export your data into these coding environments and run your analysis there. If you want your favourite tool suite as a Galaxy tool, you can always request [here](https://docs.google.com/spreadsheets/d/15hqgqA-RMDhXR-ylKhRF-Dab9Ij2arYSKiEVoPl2df4/edit?usp=sharing). Let's start with the basics of running these environments in Galaxy.
     tutorials:

learning-pathways/reloaded_single_cell.md

@@ -0,0 +1,64 @@
+---
+layout: learning-pathway
+tags: [advanced]
+cover-image: assets/images/wab-annotatedcells-2.png
+cover-image-alt: "Image of cells in different coloured clusters"
+type: use
+
+editorial_board:
+- nomadscientist
+- pavanvidem
+
+title: Applying single-cell RNA-seq analysis in Coding Environments
+description: |
+  Gone is the pre-annotated, high quality tutorial data - now you have real, messy data to deal with. You have decisions to make and parameters to decide. This learning pathway challenges you to replicate a published analysis as if this were your own dataset. You will perform this analysis in coding environments hosted on Galaxy, instead of Galaxy's button-based tool interface.
+
+  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: performing the analysis predominantly in R or in Python.
+
+  For support throughout these tutorials, join our Galaxy [single cell chat group on Matrix](https://matrix.to/#/#Galaxy-Training-Network_galaxy-single-cell:gitter.im) to ask questions!
+
+
+
+pathway:
+  - section: "Module 1: Coding environments in Galaxy"
+    description: |
+      Let's start with the basics of running a coding environments in Galaxy.
+    tutorials:
+      - name: jupyterlab
+        topic: galaxy-interface
+      - name: galaxy-intro-jupyter
+        topic: galaxy-interface
+      - name: rstudio
+        topic: galaxy-interface
+
+  - section: "Module 2: Preparing the dataset"
+    description: |
+      These tutorials take you from raw scRNA sequencing reads to a matrix ready for downstream analysis. Galaxy coding environments don't have the same level of computational power as the easy-to-use Galaxy tools, so in practice, dataset preparation is usually performed in the Galaxy user interface to process the dataset into something smaller, which can then be analysed in the coding environment. Nevertheless, the whole process can be performed in a coding environment.
+    tutorials:
+      - name: alevin-commandline
+        topic: single-cell
+
+  - section: "Module 3: Generating cluster plots"
+    description: |
+              These tutorials take you from the pre-processed matrix to cluster plots and gene expression values. You can pick whether to follow the Python (Scanpy) or R (Seurat) tutorial.
+    tutorials:
+      - name: scrna-case-jupyter_basic-pipeline
+        topic: single-cell
+      - name: scrna-case_FilterPlotandExploreRStudio
+        topic: single-cell
+
+  - section: "Module 4: Inferring trajectories"
+    description: |
+              This isn't strictly necessary, but if you want to infer trajectories - pseudotime relationships between cells - you can try out these tutorials with the same dataset. Again, you can choose whether to follow the Python (Scanpy) or R (Monocle) tutorial.
+    tutorials:
+     - name: scrna-case_JUPYTER-trajectories
+       topic: single-cell
+     - name: scrna-case_monocle3-rstudio
+       topic: single-cell
+
+  - section: "The End!"
+    description: |
+      And now you're done! You will find more features, tips and tricks in our general [Galaxy Single-cell Training page](/training-material/topics/single-cell/index.html).
+---
+
+Want to try scRNA-seq analysis in a coding environment? Follow this learning path!