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Use autodocs?
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@actapia
actapia committed Aug 26, 2024
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16 changes: 16 additions & 0 deletions .github/workflows/markdown-autodocs.yml
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name: markdown-autodocs

on:
push:
branches: ["main", "autodocs"]

jobs:
auto-update-readme:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Markdown autodocs
uses: dineshsonachalam/markdown-autodocs@v1.0.7
with:
output_file_paths: '[./docs/tutorials/reads2tree/README.md]'
commit_message: Update code examples in tutorials.
159 changes: 22 additions & 137 deletions docs/tutorials/reads2tree/README.md
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Expand Up @@ -243,46 +243,15 @@ ls "$TUTORIAL_DIR/rna_clique_out/graph.pkl"
### Getting a tree

If you want a tree, you can create one using RNA-clique and Biopython. The code
below, also found in `docs/tutorial/make_tree.py`, computes the distance matrix
from the `graph.pkl` and `od2/*.pkl` files and constructs a tree using the
neighbor-joining algorithm. The tree is also rooted at its midpoint. The tree is
saved to `nj_tree.tree`, and a visualization is saved to `nj_tree.svg` in the
`rna_clique_out` directory.

```python
import os
import Bio.Phylo

from pathlib import Path
from Bio.Phylo.TreeConstruction import DistanceTreeConstructor, DistanceMatrix
from matplotlib import pyplot as plt
from filtered_distance import SampleSimilarity
from phylo_utils import tril_jagged, draw_tree
from path_to_sample import path_to_sample

rna_clique_out_dir = Path(os.environ["TUTORIAL_DIR"]) / "rna_clique_out"

def main():
similarity_computer = SampleSimilarity.from_filenames(
rna_clique_out_dir / "graph.pkl",
(rna_clique_out_dir / "od2").glob("*.pkl")
)
nj_tree = DistanceTreeConstructor().nj(
DistanceMatrix(
[path_to_sample(p) for p in similarity_computer.samples],
tril_jagged(similarity_computer.get_dissimilarity_matrix())
)
)
nj_tree.root_at_midpoint()
for c in nj_tree.get_nonterminals():
c.name = None
Bio.Phylo.write(nj_tree, rna_clique_out_dir / "nj_tree.tree", "newick")
draw_tree(nj_tree)
plt.savefig(rna_clique_out_dir / "nj_tree.svg")

if __name__ == "__main__":
main()
```
below, also found in `docs/tutorials/reads2tree/make_tree.py`, computes the
distance matrix from the `graph.pkl` and `od2/*.pkl` files and constructs a tree
using the neighbor-joining algorithm. The tree is also rooted at its
midpoint. The tree is saved to `nj_tree.tree`, and a visualization is saved to
`nj_tree.svg` in the `rna_clique_out` directory.

<!-- MARKDOWN-AUTO-DOCS:START (CODE:src=./make_tree.py) -->
<!-- MARKDOWN-AUTO-DOCS:END -->


The script requires some modules found in the root of the RNA-clique repository,
so you can run it as follows:
Expand All @@ -299,62 +268,12 @@ stored at `$RNA_CLIQUE/docs/tutorials/reads2tree/tall_fescue_accs.csv`.

The code below draws a 3D and 2D PCoA plot and stores the results as SVG files
in the `rna_clique_out` directory as `pcoa_3d.svg` and `pcoa_2d.svg`,
respectively. The code can also be found at `docs/tutorial/make_pcoa.py`.

```python
import os
from pathlib import Path

import skbio as skb
import pandas as pd
from matplotlib import pyplot as plt

from IPython import embed
from filtered_distance import SampleSimilarity
from path_to_sample import path_to_sample

tutorial_doc_dir = Path(os.environ["RNA_CLIQUE"]) / "docs" / "tutorials"
rna_clique_out_dir = Path(os.environ["TUTORIAL_DIR"]) / "rna_clique_out"

def main():
sample_metadata = pd.read_csv(tutorial_doc_dir / "tall_fescue_accs.csv")
similarity_computer = SampleSimilarity.from_filenames(
rna_clique_out_dir / "graph.pkl",
(rna_clique_out_dir / "od2").glob("*.pkl")
)
dis_df = similarity_computer.get_dissimilarity_df().rename(
index=path_to_sample,
columns=path_to_sample,
)
# 3D PCoA
pcoa_results = skb.stats.ordination.pcoa(
skb.DistanceMatrix(dis_df, ids=dis_df.columns)
)
pcoa_results.plot(
df=sample_metadata.set_index("accession"),
column="genotype",
)
plt.savefig(rna_clique_out_dir / "pcoa_3d.svg")
# 2D PCoA
pcoa_results_2d = skb.stats.ordination.pcoa(
skb.DistanceMatrix(dis_df, ids=dis_df.columns),
number_of_dimensions=2
)
plt.figure()
for g, df in sample_metadata.join(
pcoa_results_2d.samples[["PC1","PC2"]],
"accession"
).groupby("genotype"):
plt.scatter(df["PC1"], df["PC2"], label=g)
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.legend()
plt.savefig(rna_clique_out_dir / "pcoa_2d.svg")


if __name__ == "__main__":
main()
```
respectively. The code can also be found at
`docs/tutorials/reads2tree/make_pcoa.py`.

<!-- MARKDOWN-AUTO-DOCS:START (CODE:src=./make_pcoa.py) -->
<!-- MARKDOWN-AUTO-DOCS:END -->


The example can be run as follows from the root of the RNA-clique repository.

Expand All @@ -369,47 +288,13 @@ distance matrix as a heatmap. The function uses the Seaborn `heatmap` function
behind the scenes, and arbitrary arguments given to `draw_heatmap` will be
passed to Seaborn.

The code below is also found in `docs/tutorials/reads2tree/make_heatmap.py`. It draws a
heatmap and saves the resulting figure in the `rna_clique_out` directory as
`distance_heatmap.svg`.

```python
import os
from pathlib import Path

import pandas as pd
from matplotlib import pyplot as plt

from filtered_distance import SampleSimilarity
from path_to_sample import path_to_sample
from heatmap import draw_heatmap

tutorial_doc_dir = Path(os.environ["RNA_CLIQUE"]) / "docs" / "tutorials"
rna_clique_out_dir = Path(os.environ["TUTORIAL_DIR"]) / "rna_clique_out"

def main():
sample_metadata = pd.read_csv(tutorial_doc_dir / "tall_fescue_accs.csv")
similarity_computer = SampleSimilarity.from_filenames(
rna_clique_out_dir / "graph.pkl",
(rna_clique_out_dir / "od2").glob("*.pkl")
)
dis_df = similarity_computer.get_dissimilarity_df().rename(
index=path_to_sample,
columns=path_to_sample,
)
draw_heatmap(
dis_df,
sample_metadata=sample_metadata,
sample_name_column="accession",
order_by="genotype",
cmap="mako_r"
)
plt.savefig(rna_clique_out_dir / "distance_heatmap.svg")


if __name__ == "__main__":
main()
```
The code below is also found in `docs/tutorials/reads2tree/make_heatmap.py`. It
draws a heatmap and saves the resulting figure in the `rna_clique_out` directory
as `distance_heatmap.svg`.

<!-- MARKDOWN-AUTO-DOCS:START (CODE:src=./make_heatmap.py) -->
<!-- MARKDOWN-AUTO-DOCS:END -->


To generate a heatmap using this code, you can run the Python script as follows
from the RNA-clique repository root.
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