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‎CHANGELOG.rst‎

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Changelog
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*********
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4-
1.13.0 (in development)
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-----------------------
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1.13.0 (2022-04-02)
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-------------------
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* :issue:`37`: Add new methods :meth:`cross_association_table`, :meth:`cross_association_heatmap`, and :meth:`cross_association_regplot`.
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‎docs/dokdo_api.rst‎

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.. autofunction:: cross_association_heatmap
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cross_association_regplot
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-------------------------
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.. currentmodule:: dokdo.api.cross_association
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.. autofunction:: cross_association_regplot
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regplot
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-------
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‎docs/images/cross_association_regplot.png‎

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‎dokdo/api/cross_association.py‎

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@@ -52,31 +52,39 @@ def cross_association_table(
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pandas.DataFrame
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Cross-association table.
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See Also
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--------
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dokdo.api.cross_association.cross_association_heatmap
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dokdo.api.cross_association.cross_association_regplot
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Examples
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--------
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Below example is taken from a `tutorial <https://microbiome.github.io/
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tutorials/Heatmap.html>`__ by Leo Lahti and Sudarshan Shetty et al.
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>>> import pandas as pd
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>>> import dokdo
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>>> otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
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>>> lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
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>>> df = dokdo.cross_association_table(
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... otu, lipids, normalize='log10', nsig=1
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... )
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>>> df.head(10)
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taxon target corr pval adjp
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0 Ruminococcus gnavus et rel. TG(54:5).2 0.716496 4.516954e-08 0.002284
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1 Uncultured Bacteroidetes TG(56:2).1 -0.698738 1.330755e-07 0.002345
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2 Moraxellaceae PC(40:3e) -0.694186 1.733720e-07 0.002345
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3 Ruminococcus gnavus et rel. TG(50:4) 0.691191 2.058030e-07 0.002345
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4 Lactobacillus plantarum et rel. PC(40:3) -0.687798 2.493210e-07 0.002345
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5 Ruminococcus gnavus et rel. TG(54:6).1 0.683580 3.153275e-07 0.002345
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6 Ruminococcus gnavus et rel. TG(54:4).2 0.682030 3.434292e-07 0.002345
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7 Ruminococcus gnavus et rel. TG(52:5) 0.680622 3.709485e-07 0.002345
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8 Helicobacter PC(40:3) -0.673201 5.530595e-07 0.003108
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9 Moraxellaceae PC(38:4).1 -0.670050 6.530463e-07 0.003302
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.. code:: python3
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import pandas as pd
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import dokdo
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otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
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lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
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df = dokdo.cross_association_table(
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otu, lipids, normalize='log10', nsig=1
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)
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df.head(10)
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# Will print:
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# taxon target corr pval adjp
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# 0 Ruminococcus gnavus et rel. TG(54:5).2 0.716496 4.516954e-08 0.002284
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# 1 Uncultured Bacteroidetes TG(56:2).1 -0.698738 1.330755e-07 0.002345
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# 2 Moraxellaceae PC(40:3e) -0.694186 1.733720e-07 0.002345
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# 3 Ruminococcus gnavus et rel. TG(50:4) 0.691191 2.058030e-07 0.002345
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# 4 Lactobacillus plantarum et rel. PC(40:3) -0.687798 2.493210e-07 0.002345
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# 5 Ruminococcus gnavus et rel. TG(54:6).1 0.683580 3.153275e-07 0.002345
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# 6 Ruminococcus gnavus et rel. TG(54:4).2 0.682030 3.434292e-07 0.002345
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# 7 Ruminococcus gnavus et rel. TG(52:5) 0.680622 3.709485e-07 0.002345
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# 8 Helicobacter PC(40:3) -0.673201 5.530595e-07 0.003108
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# 9 Moraxellaceae PC(38:4).1 -0.670050 6.530463e-07 0.003302
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"""
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if isinstance(artifact, Artifact):
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feats = artifact.view(pd.DataFrame)
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seaborn.matrix.ClusterGrid
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A ClusterGrid instance.
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See Also
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--------
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dokdo.api.cross_association.cross_association_table
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dokdo.api.cross_association.cross_association_regplot
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Examples
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--------
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Below example is taken from a `tutorial <https://microbiome.github.io/
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tutorials/Heatmap.html>`__ by Leo Lahti and Sudarshan Shetty et al.
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>>> import pandas as pd
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>>> import dokdo
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>>> otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
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>>> lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
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>>> dokdo.cross_association_heatmap(
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... otu, lipids, normalize='log10', nsig=1,
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... figsize=(15, 15), cmap='vlag', marksig=True,
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... annot_kws={'fontsize': 6, 'ha': 'center', 'va': 'center'}
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... )
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.. code:: python3
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import dokdo
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import matplotlib.pyplot as plt
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%matplotlib inline
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import pandas as pd
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otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
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lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
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dokdo.cross_association_heatmap(
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otu, lipids, normalize='log10', nsig=1,
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figsize=(15, 15), cmap='vlag', marksig=True,
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annot_kws={'fontsize': 6, 'ha': 'center', 'va': 'center'}
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)
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.. image:: images/cross_association_heatmap_1.png
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"""
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-------
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matplotlib.axes.Axes
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Axes object with the plot drawn onto it.
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See Also
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--------
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dokdo.api.cross_association.cross_association_table
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dokdo.api.cross_association.cross_association_heatmap
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Examples
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--------
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.. code:: python3
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import dokdo
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import matplotlib.pyplot as plt
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%matplotlib inline
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import pandas as pd
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otu = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/otu.csv', index_col=0)
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lipids = pd.read_csv('/Users/sbslee/Desktop/dokdo/data/miscellaneous/lipids.csv', index_col=0)
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dokdo.cross_association_regplot(otu, lipids, 'Ruminococcus gnavus et rel.', 'TG(54:5).2')
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plt.tight_layout()
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.. image:: images/cross_association_regplot.png
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"""
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df = pd.concat([artifact[taxon], target[name]], axis=1)
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