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Update the tutorial output with the changes made in PR #36 & #38. (#40)
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* update tutotial outputs

* fix docstring in the Q2 test code from PR #35 brought up by @fedarko
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cameronmartino authored and ElDeveloper committed Jul 8, 2019
1 parent d33a03a commit 57822c9
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5 changes: 1 addition & 4 deletions deicode/q2/tests/test_method.py
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Expand Up @@ -58,10 +58,7 @@ def setUp(self):
create_test_table())

def test_qiime2_rpca(self):
"""Tests that the Q2 and standalone RPCA results match.
Also validates against ground truth "expected" results.
"""
"""Tests that the Q2 and standalone RPCA results match."""

tstdir = "test_output"
# Run DEICODE through QIIME 2 (specifically, the Artifact API)
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Binary file added ipynb/tutorials/etc/img10.png
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13 changes: 3 additions & 10 deletions ipynb/tutorials/moving-pictures.ipynb
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Expand Up @@ -161,7 +161,7 @@
"source": [
"Biplots are exploratory visualization tools that allow us to represent the features (i.e. taxonomy or OTUs) that strongly influence the principal component axis as arrows. The interpretation of the compositional biplot differs slightly from classical biplot interpretation (we can view the qzv file at [view.qiime2](https://view.qiime2.org). The important features with regard to sample clusters are not a single arrow but by the log ratio between features represented by arrows pointing in different directions. A visualization tool for these log ratios is coming soon to QIIME. \n",
"\n",
"![](etc/img8.png)\n",
"![](etc/img10.png)\n",
"\n",
"From this visualization we noticed that BodySite seems to explain the clusters well. We can run [PERMANOVA](https://docs.qiime2.org/2019.1/plugins/available/diversity/beta-group-significance/) on the distances to get a statistical significance for this. "
]
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"source": [
"Indeed we can now see that the clusters we saw in the biplot were significant by viewing the BodySite_significance.qzv at [view.qiime2](https://view.qiime2.org).\n",
"\n",
"![](etc/img9.png)"
"![](etc/img11.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
Expand All @@ -221,7 +214,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.7"
}
},
"nbformat": 4,
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4 changes: 2 additions & 2 deletions ipynb/tutorials/moving-pictures.md
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Expand Up @@ -116,7 +116,7 @@ qiime emperor biplot \

Biplots are exploratory visualization tools that allow us to represent the features (i.e. taxonomy or OTUs) that strongly influence the principal component axis as arrows. The interpretation of the compositional biplot differs slightly from classical biplot interpretation (we can view the qzv file at [view.qiime2](https://view.qiime2.org). The important features with regard to sample clusters are not a single arrow but by the log ratio between features represented by arrows pointing in different directions. A visualization tool for these log ratios is coming soon to QIIME.

![](etc/img8.png)
![](etc/img10.png)

From this visualization we noticed that BodySite seems to explain the clusters well. We can run [PERMANOVA](https://docs.qiime2.org/2019.1/plugins/available/diversity/beta-group-significance/) on the distances to get a statistical significance for this.

Expand All @@ -131,7 +131,7 @@ From this visualization we noticed that BodySite seems to explain the clusters w

Indeed we can now see that the clusters we saw in the biplot were significant by viewing the BodySite_significance.qzv at [view.qiime2](https://view.qiime2.org).

![](etc/img9.png)
![](etc/img11.png)

## Citation

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