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Run a Fast Unifrac community analysis
=====================================
.. sectionauthor:: Justin Kuczynski
Below is a simple example of using the fast unifrac function.
first we import some tools
.. doctest::
>>> from cogent.parse.tree import DndParser
>>> from cogent.maths.unifrac.fast_unifrac import fast_unifrac
>>> from cogent.maths.unifrac.fast_tree import UniFracTreeNode
then we make a small example tree with tips B, C, D representing the relationship
between species B, C, and D
.. doctest::
>>> tree_str = "(B:0.2,(C:0.3,D:0.4)E:0.6)F;"
>>> tr = DndParser(tree_str, UniFracTreeNode)
>>> print tr.asciiArt() # doctest: +SKIP
/-B
-F-------|
| /-C
\E-------|
\-D
here's what the sample (rows) by sequence (cols) abundance matrix looks like::
... [10,11,0]
... [2,0,9]
... [2,2,2]
and here it is in dict format for unifrac
.. doctest::
>>> envs = {'B':{'sample1':10, 'sample2':2, 'sample3':2},
... 'C':{'sample1':11,'sample2':0, 'sample3':2},
... 'D':{'sample1':0, 'sample2':9, 'sample3':2}
... }
now we run unifrac::
>>> res = fast_unifrac(tr, envs)
>>> print res['distance_matrix'] # doctest: +SKIP
(array([[ 0. , 0.46666667, 0.26666667],
[ 0.46666667, 0. , 0.2 ],
[ 0.26666667, 0.2 , 0. ]]),
['sample1', 'sample2', 'sample3'])
the pcoa results are misleading for such a small dataset, but the distance
matrix is accurate
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