Update tutorials #30
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Update tutorials #30
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gwarmstrong
reviewed
Jun 8, 2020
Update IBD-Tutorial-QIIME2-CLI.md
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@gwarmstrong Okay, this should be good to go now. |
gwarmstrong
approved these changes
Jul 21, 2020
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| **Output:** | ||
| ```bash | ||
| Saved DistanceMatrix % Properties('phylogenetic') to: IBD-2538/core-metric-output/unweighted-unifrac-distance.qza | ||
| Saved DistanceMatrix % Properties('phylogenetic') to: IBD-2538/core-metric-output/weighted-unifrac-distance.qza | ||
| Saved PCoAResults to: IBD-2538/core-metric-output/unweighted-unifrac-distance-pcoa.qza | ||
| Saved PCoAResults to: IBD-2538/core-metric-output/weighted-unifrac-distance-pcoa.qza | ||
| Saved Visualization to: IBD-2538/core-metric-output/unweighted-unifrac-distance-pcoa.qzv | ||
| Saved Visualization to: IBD-2538/core-metric-output/weighted-unifrac-distance-pcoa.qzv | ||
| ``` |
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I was confused by this at first. Maybe this is holdover from the jupyter notebook, where all of these bash commands could have been run in the same cell?
Can you put each output with the corresponding bash command? A la Q2 docs: https://docs.qiime2.org/2020.2/tutorials/moving-pictures/
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| # Compositional Tensor Factorization (CTF) Introduction | ||
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| In order to account for the correlation from a subject to thierself we will compositional tensor factorization (CTF). CTF builds on the ability to account for compositionality and sparsity using the robust center log-ratio transform covered in the RPCA tutorial (found [here](https://forum.qiime2.org/t/robust-aitchison-pca-beta-diversity-with-deicode)) but restructures and factors the data as a tensor. Here we will run CTF through _gemelli_ and explore/interpret the different results. |
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Change "thierself" to "theirself" here, again.
| Saved FeatureData[FeatureTrajectory] to: IBD-2538/ctf-results/state_feature_ordination.qza | ||
| ``` | ||
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| We will now cover the output files being: |
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Suggested change
| We will now cover the output files being: | |
| We will now cover the following output files: |
| mf.to_csv('IBD-2538/data/subject-metadata.tsv', sep='\t') | ||
| ``` | ||
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| With out `subject-metadata` table build we are not ready to plot with emperor. |
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| With out `subject-metadata` table build we are not ready to plot with emperor. | |
| With our `subject-metadata` table build we are now ready to plot with emperor. |
| * state_subject_ordination | ||
| * state_feature_ordination | ||
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| First, we will explore the `state_subject_ordination`. The subject trajectory has PC axes like a conventional ordination (i.e. PCoA) but with time as the second axis. This can be visualized through the existing q2-longitudinal plugin. |
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Maybe for each of the out put files, you could do sub-headings in like H4 or something? To make each artifact a little easier to find.
E.g.
state_subject_ordination
First, we will explore the state_subject_ordination. ...
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Added three tutorials on the exact same time series IBD dataset but using: