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Adding tutorial on trajectory analysis #4367
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Added link to the data file on Zenodo
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not sure this is necessary?
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Oops, I left an empty file there by mistake!
The accessibility linting errors will be fixed shortly in
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Thanks - I'm going to try and do the workflow tests! |
@MarisaJL |
...ials/scrna-case_trajectories/workflows/inferring-trajectories-with-scanpy-workflow-tests.yml
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Thanks so much for your patience with all the workflow testing changes @MarisaJL hopefully we'll be able to soon turn this into automated histories across the usegalaxy.* servers. |
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Fantastic work! made a couple small comments but from the gtn/formatting side this looks great
topics/single-cell/tutorials/scrna-case_trajectories/tutorial.md
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> > | ||
> > 1. The interesting change here is that our DP-M4 cells are now heading on a clear trajectory towards differentiation. It looks like we've got the correct ordering of cells from DN through the DP groups and on towards T-mature. We didn't see this in our previous plots. | ||
> > | ||
> > ![The M4 cluster is now closer to the DP L cluster](../../images/scrna-case_trajectories/TrajectoriesFDG3.png "FDG plot initiated from the PAGA plot") |
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this is fantastic alt text, short and tells the reader exactly what they should be getting out of this graphic.
The validation failures are due to things I caused, which were fixed in #4476 and can be ignored :) |
Thanks for your help! Co-authored-by: Helena <[email protected]>
@nomadscientist ready from my side if ok for you |
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Looks really good, I love the content.
I have a few nitpicking points about explaining some acronyms -- but feel free to ignore them if you believe that users would already be aware of what these acronyms mean at this point in the galaxy tutorials.
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It is easy to follow. Well done! I tried the tutorial with 1.8.1+galaxy9 and worked. With the latest version it fails but that's ok I guess because we have links to specific tool version.
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Let me add my changes, and we can maybe merge this today? |
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I turned my comments into commits
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## Re-draw force-directed graph (again!) | ||
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Force directed graphs can be initialised randomly, or we can prod it in the right direction. We'll prod it with our PAGA calculations. Note that you could also try prodding it with tSNE or UMAP. A lot of these tools can be used on top of each other or with each other in different ways, this tutorial is just one example. Similarly, you could be using any **obs** information for grouping, so could do this for *louvain* or *cell_type* for instance. |
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Force directed graphs can be initialised randomly, or we can prod it in the right direction. We'll prod it with our PAGA calculations. Note that you could also try prodding it with tSNE or UMAP. A lot of these tools can be used on top of each other or with each other in different ways, this tutorial is just one example. Similarly, you could be using any **obs** information for grouping, so could do this for *louvain* or *cell_type* for instance. | |
Force directed graphs can be initialised randomly, or we can prod it in the right direction. We'll prod it with our PAGA calculations. Note that you could also try prodding it with [tSNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) or [UMAP](https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction#Uniform_manifold_approximation_and_projection). A lot of these tools can be used on top of each other or with each other in different ways, this tutorial is just one example. Similarly, you could be using any **obs** information for grouping, so could do this for *louvain* or *cell_type* for instance. |
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Added two wikipedia links, but maybe not necessary.
Co-authored-by: mtekman <[email protected]>
This is mostly a copy of the existing Jupyter version of this tutorial.