Trees#
Loading a tree from a file and visualizing it with ascii_art()#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
print(tr.ascii_art())
/-Human
/edge.0--|
/edge.1--| \-HowlerMon
| |
| \-Mouse
-root----|
|--NineBande
|
\-DogFaced
Writing a tree to a file#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
tr.write("data/temp.tree")
Getting the individual nodes of a tree by name#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
names = tr.get_node_names()
names[:4]
['root', 'edge.1', 'edge.0', 'Human']
names[4:]
names_nodes = tr.get_nodes_dict()
names_nodes["Human"]
Tree("Human;")
tr.get_node_matching_name("Mouse")
Tree("Mouse;")
Getting the name of a node (or a tree)#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
hu = tr.get_node_matching_name("Human")
tr.name
'root'
hu.name
'Human'
The object type of a tree and its nodes is the same#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
nodes = tr.get_nodes_dict()
hu = nodes["Human"]
type(hu)
cogent3.core.tree.PhyloNode
type(tr)
cogent3.core.tree.PhyloNode
Working with the nodes of a tree#
Get all the nodes, tips and edges
from cogent3 import load_tree
tr = load_tree("data/test.tree")
nodes = tr.get_nodes_dict()
for n in nodes.items():
print(n)
('root', Tree("(((Human,HowlerMon),Mouse),NineBande,DogFaced);"))
('edge.1', Tree("((Human,HowlerMon),Mouse);"))
('edge.0', Tree("(Human,HowlerMon);"))
('Human', Tree("Human;"))
('HowlerMon', Tree("HowlerMon;"))
('Mouse', Tree("Mouse;"))
('NineBande', Tree("NineBande;"))
('DogFaced', Tree("DogFaced;"))
only the terminal nodes (tips)
for n in tr.iter_tips():
print(n)
Human:0.0311054096183;
HowlerMon:0.0415847131449;
Mouse:0.277353608988;
NineBande:0.0939768158209;
DogFaced:0.113211053859;
for internal nodes (edges) we can use Newick format to simplify the output
from cogent3 import load_tree
tr = load_tree("data/test.tree")
for n in tr.iter_nontips():
print(n.get_newick())
((Human,HowlerMon),Mouse);
(Human,HowlerMon);
Getting the path between two tips or edges (connecting edges)#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
edges = tr.get_connecting_edges("edge.1", "Human")
for edge in edges:
print(edge.name)
edge.1
edge.0
Human
Getting the distance between two nodes#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
nodes = tr.get_nodes_dict()
hu = nodes["Human"]
mu = nodes["Mouse"]
hu.distance(mu)
hu.is_tip()
True
Getting the last common ancestor (LCA) for two nodes#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
nodes = tr.get_nodes_dict()
hu = nodes["Human"]
mu = nodes["Mouse"]
lca = hu.last_common_ancestor(mu)
lca
Tree("((Human,HowlerMon),Mouse);")
type(lca)
cogent3.core.tree.PhyloNode
Getting all the ancestors for a node#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
hu = tr.get_node_matching_name("Human")
for a in hu.ancestors():
print(a.name)
edge.0
edge.1
root
Getting all the children for a node#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
node = tr.get_node_matching_name("edge.1")
children = list(node.iter_tips()) + list(node.iter_nontips())
for child in children:
print(child.name)
Human
HowlerMon
Mouse
edge.0
Getting all the distances for a tree#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
dists = tr.get_distances()
We also show how to select a subset of distances involving just one species.
human_dists = [names for names in dists if "Human" in names]
for dist in human_dists:
print(dist, dists[dist])
('Human', 'HowlerMon') 0.0726901227632
('HowlerMon', 'Human') 0.0726901227632
('Human', 'Mouse') 0.3467553610937
('Mouse', 'Human') 0.3467553610937
('Human', 'NineBande') 0.18310641816450002
('NineBande', 'Human') 0.18310641816450002
('Human', 'DogFaced') 0.2023406562026
('DogFaced', 'Human') 0.2023406562026
Getting the two nodes that are farthest apart#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
tr.max_tip_tip_distance()
(np.float64(0.4102925130849), ('Mouse', 'DogFaced'))
Get the nodes within a given distance#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
hu = tr.get_node_matching_name("Human")
tips = hu.tips_within_distance(0.2)
for t in tips:
print(t)
HowlerMon:0.0415847131449;
NineBande:0.0939768158209;
Rerooting trees#
At a named node#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
print(tr.rooted_at("edge.0").ascii_art())
/-Human
|
-root----|--HowlerMon
|
| /-Mouse
\edge.0--|
| /-NineBande
\edge.1--|
\-DogFaced
At the midpoint#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
print(tr.root_at_midpoint().ascii_art())
/-Mouse
|
-root----| /-Human
| /edge.0--|
| | \-HowlerMon
\edge.0.2|
| /-NineBande
\edge.1--|
\-DogFaced
print(tr.ascii_art())
/-Human
/edge.0--|
/edge.1--| \-HowlerMon
| |
| \-------- /-Mouse
-root----|
|--NineBande
|
\-DogFaced
Near a given tip#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
print(tr.ascii_art())
/-Human
/edge.0--|
/edge.1--| \-HowlerMon
| |
| \-Mouse
-root----|
|--NineBande
|
\-DogFaced
print(tr.rooted_with_tip("Mouse").ascii_art())
/-Human
/edge.0--|
| \-HowlerMon
|
-root----|--Mouse
|
| /-NineBande
\edge.1--|
\-DogFaced
Tree representations#
Newick format#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
tr.get_newick()
'(((Human,HowlerMon),Mouse),NineBande,DogFaced);'
tr.get_newick(with_distances=True)
'(((Human:0.0311054096183,HowlerMon:0.0415847131449):0.0382963424874,Mouse:0.277353608988):0.0197278502379,NineBande:0.0939768158209,DogFaced:0.113211053859);'
XML format#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
xml = tr.get_xml()
for line in xml.splitlines():
print(line)
<?xml version="1.0"?>
<clade>
<clade>
<param><name>length</name><value>0.0197278502379</value></param>
<clade>
<param><name>length</name><value>0.0382963424874</value></param>
<clade>
<name>Human</name>
<param><name>length</name><value>0.0311054096183</value></param>
</clade>
<clade>
<name>HowlerMon</name>
<param><name>length</name><value>0.0415847131449</value></param>
</clade>
</clade>
<clade>
<name>Mouse</name>
<param><name>length</name><value>0.277353608988</value></param>
</clade>
</clade>
<clade>
<name>NineBande</name>
<param><name>length</name><value>0.0939768158209</value></param>
</clade>
<clade>
<name>DogFaced</name>
<param><name>length</name><value>0.113211053859</value></param>
</clade>
</clade>
Tree traversal#
Here is the example tree for reference:
from cogent3 import load_tree
tr = load_tree("data/test.tree")
print(tr.ascii_art())
/-Human
/edge.0--|
/edge.1--| \-HowlerMon
| |
| \-Mouse
-root----|
|--NineBande
|
\-DogFaced
Preorder#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
for t in tr.preorder():
print(t.get_newick())
(((Human,HowlerMon),Mouse),NineBande,DogFaced);
((Human,HowlerMon),Mouse);
(Human,HowlerMon);
Human;
HowlerMon;
Mouse;
NineBande;
DogFaced;
Postorder#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
for t in tr.postorder():
print(t.get_newick())
Human;
HowlerMon;
(Human,HowlerMon);
Mouse;
((Human,HowlerMon),Mouse);
NineBande;
DogFaced;
(((Human,HowlerMon),Mouse),NineBande,DogFaced);
Selecting subtrees#
One way to do it#
from cogent3 import load_tree
tr = load_tree("data/test.tree")
for tip in tr.iter_nontips():
tip_names = tip.get_tip_names()
print(tip_names)
sub_tree = tr.get_sub_tree(tip_names)
print(sub_tree.ascii_art())
['Human', 'HowlerMon', 'Mouse']
/-Human
|
-root----|--HowlerMon
|
\-Mouse
['Human', 'HowlerMon']
/-Human
-root----|
\-HowlerMon
Tree manipulation methods#
Pruning the tree#
Remove internal nodes with only one child. Create new connections and branch lengths (if tree is a PhyloNode) to reflect the change.
from cogent3 import make_tree
simple_tree_string = "(B:0.2,(D:0.4)E:0.5)F;"
simple_tree = make_tree(simple_tree_string)
print(simple_tree.ascii_art())
/-B
-F-------|
\E------- /-D
simple_tree.prune()
print(simple_tree.ascii_art())
/-B
-F-------|
\-D
print(simple_tree)
(B:0.2,D:0.9)F;
Create a full unrooted copy of the tree#
from cogent3 import load_tree
tr1 = load_tree("data/test.tree")
print(tr1.get_newick())
(((Human,HowlerMon),Mouse),NineBande,DogFaced);
tr2 = tr1.unrooted_deepcopy()
print(tr2.get_newick())
(((Human,HowlerMon),Mouse),NineBande,DogFaced);
Transform tree into a bifurcating tree#
Add internal nodes so that every node has 2 or fewer children.
from cogent3 import make_tree
tree_string = "(B:0.2,H:0.2,(C:0.3,D:0.4,E:0.1)F:0.5)G;"
tr = make_tree(tree_string)
print(tr.ascii_art())
/-B
|
|--H
-G-------|
| /-C
| |
\F-------|--D
|
\-E
print(tr.bifurcating().ascii_art())
/-B
-G-------|
| /-H
\--------|
| /-C
\F-------|
| /-D
\--------|
\-E
Transform tree into a balanced tree#
Using a balanced tree can substantially improve performance of likelihood calculations. Note that the resulting tree has a different orientation with the effect that specifying clades or stems for model parameterisation should be done using the “outgroup_name” argument.
from cogent3 import load_tree
tr = load_tree("data/test.tree")
print(tr.ascii_art())
/-Human
/edge.0--|
/edge.1--| \-HowlerMon
| |
| \-Mouse
-root----|
|--NineBande
|
\-DogFaced
print(tr.balanced().ascii_art())
/-Human
/edge.0--|
| \-HowlerMon
|
-root----|--Mouse
|
| /-NineBande
\edge.1--|
\-DogFaced
Test two trees for same topology#
Branch lengths don’t matter.
from cogent3 import make_tree
tr1 = make_tree("(B:0.2,(C:0.2,D:0.2)F:0.2)G;")
tr2 = make_tree("((C:0.1,D:0.1)F:0.1,B:0.1)G;")
tr1.same_topology(tr2)
True
Measure topological distances between two trees#
A number of topological tree distance metrics are available. They include:
The Robinson-Foulds Distance for rooted trees.
The Matching Cluster Distance for rooted trees.
The Robinson-Foulds Distance for unrooted trees.
The Lin-Rajan-Moret Distance for unrooted trees.
There are several variations of the Robinson-Foulds metric in the literature. The definition used by cogent3 is the
cardinality of the symmetric difference of the sets of clades/splits in the two rooted/unrooted trees. Other definitions sometimes
divide this by two, or normalise it to the unit interval.
The Robinson-Foulds distance is quick to compute, but is known to saturate quickly. Moving a single leaf in a tree can maximise this metric.
The Matching Cluster and Lin-Rajan-Moret are two matching-based distances that are more statistically robust. Unlike the Robinson-Foulds distance which counts how many of the splits/clades are not exactly same, the matching-based distances measures the degree by which the splits/clades are different. The matching-based distances solve a min-weight matching problem, which for large trees may take longer to compute.
# Distance metrics for rooted trees
from cogent3 import make_tree
tr1 = make_tree(treestring="(a,(b,(c,(d,e))));")
tr2 = make_tree(treestring="(e,(d,(c,(b,a))));")
mc_distance = tr1.tree_distance(tr2, method="matching_cluster") # or method="mc" or method="matching"
rooted_rf_distance = tr1.tree_distance(tr2, method="rooted_robinson_foulds") # or method="rrf" or method="rf"
print("Matching Cluster Distance:", mc_distance)
print("Rooted Robinson Foulds Distance:", rooted_rf_distance)
Matching Cluster Distance: 10
Rooted Robinson Foulds Distance: 6
# Distance metrics for unrooted trees
from cogent3 import make_tree
tr1 = make_tree(treestring="(a,b,(c,(d,e)));")
tr2 = make_tree(treestring="((a,c),(b,d),e);")
lrm_distance = tr1.tree_distance(tr2, method="lin_rajan_moret") # or method="lrm" or method="matching"
unrooted_rf_distance = tr1.tree_distance(tr2, method="unrooted_robinson_foulds") # or method="urf" or method="rf"
print("Lin-Rajan-Moret Distance:", lrm_distance)
print("Unrooted Robinson Foulds Distance:", unrooted_rf_distance)
Lin-Rajan-Moret Distance: 3
Unrooted Robinson Foulds Distance: 4
Calculate each node’s maximum distance to a tip#
Sets each node’s “TipDistance” attribute to be the distance from that node to its most distant tip.
from cogent3 import make_tree
tr = make_tree("(B:0.2,(C:0.3,D:0.4)F:0.5)G;")
print(tr.ascii_art())
/-B
-G-------|
| /-C
\F-------|
\-D
tr.set_tip_distances()
for t in tr.preorder():
print(t.name, t.TipDistance)
G 0.9
B 0
F 0.4
C 0
D 0
Scale branch lengths in place to integers for ascii output#
from cogent3 import make_tree
tr = make_tree("(B:0.2,(C:0.3,D:0.4)F:0.5)G;")
print(tr)
(B:0.2,(C:0.3,D:0.4)F:0.5)G;
tr.scale_branch_lengths()
print(tr)
(B:22,(C:33,D:44)F:56)G;
Get tip-to-tip distances#
Get a distance matrix between all pairs of tips and a list of the tip nodes.
from cogent3 import make_tree
tr = make_tree("(B:3,(C:2,D:4)F:5)G;")
d, tips = tr.tip_to_tip_distances()
for i, t in enumerate(tips):
print(t.name, d[i])
B [ 0. 10. 12.]
C [10. 0. 6.]
D [12. 6. 0.]
Compare two trees using tip-to-tip distance matrices#
Score ranges from 0 (minimum distance) to 1 (maximum distance). The default is to use Pearson’s correlation, in which case a score of 0 means that the Pearson’s correlation was perfectly good (1), and a score of 1 means that the Pearson’s correlation was perfectly bad (-1).
Note: automatically strips out the names that don’t match.
from cogent3 import make_tree
tr1 = make_tree("(B:2,(C:3,D:4)F:5)G;")
tr2 = make_tree("(C:2,(B:3,D:4)F:5)G;")
tr1.compare_by_tip_distances(tr2)
np.float64(0.08352668213457076)