Analysing trees¶
There are a number of different ways we might want to analyse a single Tree.
Most involve some sort of traversal over the nodes, mutations, or branches in the tree.
tskit provides various way of traversing through a tree, and also some
built in phylogenetic algorithms such as Tree.map_mutations() which efficently
places mutations (“characters” in phylogenetic terminology) on a given tree.
Tree traversals¶
Given a single Tree, traversals in various orders are possible using the
nodes() iterator. For example, in the following tree we can visit the
nodes in different orders:
import tskit
from IPython.display import SVG, display
ts = tskit.load("data/tree_traversals.trees")
tree = ts.first()
display(SVG(tree.draw_svg()))
for order in ["preorder", "inorder", "postorder"]:
print(f"{order}:\t", list(tree.nodes(order=order)))
preorder: [7, 5, 0, 1, 2, 6, 3, 4]
inorder: [0, 5, 1, 2, 7, 3, 6, 4]
postorder: [0, 1, 2, 5, 3, 4, 6, 7]
Much of the time, the specific ordering of the nodes is not important and we can leave it out (defaulting to preorder traversal). For example, here we compute the total branch length of a tree:
total_branch_length = sum(tree.branch_length(u) for u in tree.nodes())
print(f"Total branch length: {total_branch_length}")
Total branch length: 10.0
Note that this is also available as the Tree.total_branch_length attribute.
Traversing upwards¶
For many applications it is useful to be able to traverse upwards from the
leaves. We can do this using the Tree.parent() method, which
returns the parent of a node. For example, we can traverse upwards from
each of the samples in the tree:
for u in tree.samples():
path = []
v = u
while v != tskit.NULL:
path.append(v)
v = tree.parent(v)
print(u, "->", path)
0 -> [0, 5, 7]
1 -> [1, 5, 7]
2 -> [2, 5, 7]
3 -> [3, 6, 7]
4 -> [4, 6, 7]
Traversals with information¶
Sometimes we will need to traverse down the tree while maintaining some information about the nodes that are above it. While this can be done using recursive algorithms, it is often more convenient and efficient to use an iterative approach. Here, for example, we define an iterator that yields all nodes in preorder along with their path length to root:
def preorder_dist(tree):
for root in tree.roots:
stack = [(root, 0)]
while len(stack) > 0:
u, distance = stack.pop()
yield u, distance
for v in tree.children(u):
stack.append((v, distance + 1))
print(list(preorder_dist(tree)))
[(7, 0), (6, 1), (4, 2), (3, 2), (5, 1), (2, 2), (1, 2), (0, 2)]
Networkx¶
Traversals and other network analysis can also be performed using the sizeable
networkx
library. This can be achieved by calling Tree.as_dict_of_dicts() to
convert a Tree instance to a format that can be imported by networkx to
create a graph:
import networkx as nx
g = nx.DiGraph(tree.as_dict_of_dicts())
print(sorted(g.edges))
[(5, 0), (5, 1), (5, 2), (6, 3), (6, 4), (7, 5), (7, 6)]
Traversing upwards in networkx¶
We can revisit the above examples and traverse upwards with networkx using a depth-first search algorithm:
g = nx.DiGraph(tree.as_dict_of_dicts())
for u in tree.samples():
path = [u] + [parent for parent, child, _ in
nx.edge_dfs(g, source=u, orientation="reverse")]
print(u, "->", path)
0 -> [0, 5, 7]
1 -> [1, 5, 7]
2 -> [2, 5, 7]
3 -> [3, 6, 7]
4 -> [4, 6, 7]
Calculating distances to the root¶
Similarly, we can yield the nodes of a tree along with their distance to the root in pre-order in networkx as well. Running this on the example above gives us the same result as before:
g = nx.DiGraph(tree.as_dict_of_dicts())
for root in tree.roots:
print(sorted(list(nx.shortest_path_length(g, source=root).items())))
[(0, 2), (1, 2), (2, 2), (3, 2), (4, 2), (5, 1), (6, 1), (7, 0)]
Finding nearest neighbors¶
If some samples in a tree are not at time 0, then finding the nearest neighbor of a sample is a bit more involved. Instead of writing our own traversal code we can again draw on a networkx algorithm. Let us start with an example tree with three samples that were sampled at different time points:
ts = tskit.load("data/different_time_samples.trees")
tree = ts.first()
SVG(tree.draw_svg(y_axis=True, time_scale="rank"))
The generation times for these nodes are as follows:
for u in tree.nodes():
print(f"Node {u}: time {tree.time(u)}")
Node 4: time 20.00539877826333
Node 2: time 20.0
Node 3: time 17.833492457579652
Node 0: time 0.0
Node 1: time 1.0
Note that samples 0 and 1 are about 35 generations apart from each other even though they were sampled at almost the same time. This is why samples 0 and 1 are closer to sample 2 than to each other.
For this nearest neighbor search we will be traversing up and down the tree, so it is easier to treat the tree as an undirected graph:
g = nx.Graph(tree.as_dict_of_dicts())
When converting the tree to a networkx graph the edges are annotated with their branch length:
for e in g.edges(data=True):
print(e)
(4, 2, {'branch_length': 0.0053987782633306836})
(4, 3, {'branch_length': 2.1719063206836786})
(3, 0, {'branch_length': 17.833492457579652})
(3, 1, {'branch_length': 16.833492457579652})
We can now use the “branch_length” field as a weight for a weighted shortest path search:
import collections
import itertools
# a dictionary of dictionaries to represent our distance matrix
dist_dod = collections.defaultdict(dict)
for source, target in itertools.combinations(tree.samples(), 2):
dist_dod[source][target] = nx.shortest_path_length(
g, source=source, target=target, weight="branch_length"
)
dist_dod[target][source] = dist_dod[source][target]
# extract the nearest neighbor of nodes 0, 1, and 2
nearest_neighbor_of = [min(dist_dod[u], key=dist_dod[u].get) for u in range(3)]
print(dict(zip(range(3), nearest_neighbor_of)))
{0: 2, 1: 2, 2: 1}
Parsimony¶
The Tree.map_mutations() method finds a parsimonious explanation for a
set of discrete character observations on the samples in a tree using classical
phylogenetic algorithms.
tree = tskit.load("data/parsimony_simple.trees").first()
alleles = ["red", "blue", "green"]
genotypes = [0, 0, 0, 0, 1, 2]
styles = [f".n{j} > .sym {{fill: {alleles[g]}}}" for j, g in enumerate(genotypes)]
display(SVG(tree.draw_svg(style="".join(styles))))
ancestral_state, mutations = tree.map_mutations(genotypes, alleles)
print("Ancestral state = ", ancestral_state)
for mut in mutations:
print(f"Mutation: node = {mut.node} derived_state = {mut.derived_state}")
Ancestral state = red
Mutation: node = 4 derived_state = blue
Mutation: node = 5 derived_state = green
So, the algorithm has concluded, quite reasonably, that the most parsimonious description of this state is that the ancestral state is red and there was a mutation to blue and green over nodes 4 and 5.
Building tables¶
One of the main uses of Tree.map_mutations() is to position mutations on a tree
to encode observed data. In the following example we show how a set
of tables can be updated using the Tables API; here we
infer the location of mutations in an simulated tree sequence of one tree,
given a set of site positions with their genotypes and allelic states:
import pickle
ts = tskit.load("data/parsimony_map.trees")
with open("data/parsimony_map.pickle", "rb") as file:
data = pickle.load(file) # Load saved variant data from a file
display(SVG(ts.draw_svg(size=(500, 300), time_scale="rank")))
print("Variant data: pos, genotypes & alleles as described by the ts.variants() iterator:")
for i, v in enumerate(data):
print(f"Site {i} (pos {v['pos']:7.4f}): alleles: {v['alleles']}, genotypes: {v['genotypes']}")
print()
tree = ts.first() # there's only one tree anyway
tables = ts.dump_tables()
# Infer the sites and mutations from the variants.
for variant in data:
ancestral_state, mutations = tree.map_mutations(variant["genotypes"], variant['alleles'])
site_id = tables.sites.add_row(variant['pos'], ancestral_state=ancestral_state)
info = f"Site {site_id}: parsimony sets ancestral state to {ancestral_state}"
parent_offset = len(tables.mutations)
for mut in mutations:
parent = mut.parent
if parent != tskit.NULL:
parent += parent_offset
mut_id = tables.mutations.add_row(
site_id, node=mut.node, parent=parent, derived_state=mut.derived_state)
info += f", and places mutation {mut.id} to {mut.derived_state} above node {mut.node}"
print(info)
new_ts = tables.tree_sequence()
Variant data: pos, genotypes & alleles as described by the ts.variants() iterator:
Site 0 (pos 8.3726): alleles: ('G', 'A'), genotypes: [1 1 0 0 0 0]
Site 1 (pos 24.4759): alleles: ('T', 'C'), genotypes: [0 0 0 0 1 1]
Site 2 (pos 34.3178): alleles: ('G', 'T'), genotypes: [0 0 1 1 0 0]
Site 3 (pos 39.2118): alleles: ('G', 'C'), genotypes: [0 0 1 1 0 0]
Site 4 (pos 44.0257): alleles: ('G', 'C'), genotypes: [1 1 0 0 0 0]
Site 5 (pos 48.0932): alleles: ('C', 'G'), genotypes: [0 0 1 1 0 0]
Site 6 (pos 68.4830): alleles: ('C', 'G'), genotypes: [0 0 1 1 0 0]
Site 7 (pos 69.4755): alleles: ('A', 'C'), genotypes: [0 0 0 0 1 1]
Site 8 (pos 71.2330): alleles: ('C', 'T'), genotypes: [1 1 0 0 0 0]
Site 9 (pos 71.9150): alleles: ('G', 'T'), genotypes: [0 0 0 0 0 1]
Site 0: parsimony sets ancestral state to G, and places mutation -1 to A above node 6
Site 1: parsimony sets ancestral state to T, and places mutation -1 to C above node 8
Site 2: parsimony sets ancestral state to G, and places mutation -1 to T above node 7
Site 3: parsimony sets ancestral state to G, and places mutation -1 to C above node 7
Site 4: parsimony sets ancestral state to G, and places mutation -1 to C above node 6
Site 5: parsimony sets ancestral state to C, and places mutation -1 to G above node 7
Site 6: parsimony sets ancestral state to C, and places mutation -1 to G above node 7
Site 7: parsimony sets ancestral state to A, and places mutation -1 to C above node 8
Site 8: parsimony sets ancestral state to C, and places mutation -1 to T above node 6
Site 9: parsimony sets ancestral state to G, and places mutation -1 to T above node 5
mut_labels = {} # An array of labels for the mutations
for mut in new_ts.mutations(): # Make pretty labels showing the change in state
site = new_ts.site(mut.site)
older_mut = mut.parent >= 0 # is there an older mutation at the same position?
prev = new_ts.mutation(mut.parent).derived_state if older_mut else site.ancestral_state
mut_labels[site.id] = f"{mut.id}: {prev}→{mut.derived_state}"
display(SVG(new_ts.draw_svg(size=(500, 300), mutation_labels=mut_labels, time_scale="rank")))
Parsimony and missing data¶
The Hartigan parsimony algorithm in Tree.map_mutations() can also take missing data
into account when finding a set of parsimonious state transitions. We do this by
specifying the special value tskit.MISSING_DATA (-1) as the state, which is
treated by the algorithm as “could be anything”.
For example, here we state that sample 0 is missing, and use the colour white to indicate this:
tree = tskit.load("data/parsimony_simple.trees").first()
alleles = ["red", "blue", "green", "white"]
genotypes = [tskit.MISSING_DATA, 0, 0, 0, 1, 2]
styles = [f".n{j} > .sym {{fill: {alleles[g]}}}" for j, g in enumerate(genotypes)]
display(SVG(tree.draw_svg(style="".join(styles))))
ancestral_state, mutations = tree.map_mutations(genotypes, alleles)
print("Ancestral state = ", ancestral_state)
for mut in mutations:
print(f"Mutation: node = {mut.node} derived_state = {mut.derived_state}")
Ancestral state = red
Mutation: node = 4 derived_state = blue
Mutation: node = 5 derived_state = green
The algorithm decided, again, quite reasonably, that the most parsimonious explanation for the input data is the same as before. Thus, if we used this information to fill out mutation table as above, we would impute the missing value for 0 as red.
The output of the algorithm can be a little surprising at times. Consider this example::
tree = msprime.simulate(6, random_seed=42).first()
alleles = ["red", "blue", "white"]
genotypes = [1, -1, 0, 0, 0, 0]
node_colours = {j: alleles[g] for j, g in enumerate(genotypes)}
ancestral_state, mutations = tree.map_mutations(genotypes, alleles)
print("Ancestral state = ", ancestral_state)
for mut in mutations:
print(f"Mutation: node = {mut.node} derived_state = {mut.derived_state}")
SVG(tree.draw(node_colours=node_colours))
Ancestral state = red
Mutation: node = 6 derived_state = blue
Note that this is putting a mutation to blue over node 6, not node 0 as we might expect. Thus, we impute here that node 1 is blue. It is important to remember that the algorithm is minimising the number of state transitions; this may not correspond always to what we might consider the most parsimonious explanation.