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#!/usr/bin/env python |
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# -*- coding: UTF-8 -*- |
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""" |
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Compute semantic similarities between GO terms. Borrowed from book chapter from |
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Christophe Dessimoz (thanks). For details, please check out: |
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notebooks/semantic_similarity.ipynb |
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""" |
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import math |
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from collections import Counter |
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class TermCounts: |
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''' |
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TermCounts counts the term counts for each |
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''' |
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def __init__(self, go, annots): |
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''' |
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Initialise the counts and |
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''' |
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# Backup |
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self._go = go |
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# Initialise the counters |
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self._counts = Counter() |
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self._aspect_counts = Counter() |
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# Fill the counters... |
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self._count_terms(go, annots) |
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def _count_terms(self, go, annots): |
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''' |
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Fills in the counts and overall aspect counts. |
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''' |
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for gene, terms in annots.items(): |
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# Make a union of all the terms for a gene, if term parents are |
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# propagated but they won't get double-counted for the gene |
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allterms = set(terms) |
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for go_id in terms: |
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allterms |= go[go_id].get_all_parents() |
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for p in allterms: |
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self._counts[p] += 1 |
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for go_id, c in self._counts.items(): |
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# Group by namespace |
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namespace = go[go_id].namespace |
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self._aspect_counts[namespace] += c |
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def get_count(self, go_id): |
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''' |
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Returns the count of that GO term observed in the annotations. |
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''' |
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return self._counts[go_id] |
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def get_total_count(self, aspect): |
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''' |
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Gets the total count that's been precomputed. |
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''' |
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return self._aspect_counts[aspect] |
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def get_term_freq(self, go_id): |
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''' |
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Returns the frequency at which a particular GO term has |
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been observed in the annotations. |
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''' |
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try: |
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namespace = self._go[go_id].namespace |
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freq = float(self.get_count(go_id)) / float(self.get_total_count(namespace)) |
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#print self.get_count(go_id), self.get_total_count(namespace), freq |
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except ZeroDivisionError: |
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freq = 0 |
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return freq |
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def ic(go_id, termcounts): |
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''' |
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Calculates the information content of a GO term. |
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''' |
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# Get the observed frequency of the GO term |
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freq = termcounts.get_term_freq(go_id) |
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# Calculate the information content (i.e., -log("freq of GO term") |
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return -1.0 * math.log(freq) if freq else 0 |
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def resnik_sim(go_id1, go_id2, go, termcounts): |
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''' |
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Computes Resnik's similarity measure. |
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''' |
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msca = deepest_common_ancestor([go_id1, go_id2], go) |
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return ic(msca, termcounts) |
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def lin_sim(go_id1, go_id2, go, termcounts): |
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''' |
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Computes Lin's similarity measure. |
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''' |
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sim_r = resnik_sim(go_id1, go_id2, go, termcounts) |
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return (-2*sim_r)/(ic(go_id1, termcounts) + ic(go_id2, termcounts)) |
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def common_parent_go_ids(terms, go): |
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''' |
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This function finds the common ancestors in the GO |
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tree of the list of terms in the input. |
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''' |
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# Find candidates from first |
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rec = go[terms[0]] |
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candidates = rec.get_all_parents() |
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candidates.update({terms[0]}) |
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# Find intersection with second to nth term |
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for term in terms[1:]: |
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rec = go[term] |
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parents = rec.get_all_parents() |
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parents.update({term}) |
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# Find the intersection with the candidates, and update. |
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candidates.intersection_update(parents) |
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return candidates |
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def deepest_common_ancestor(terms, go): |
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''' |
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This function gets the nearest common ancestor |
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using the above function. |
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Only returns single most specific - assumes unique exists. |
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''' |
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# Take the element at maximum depth. |
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return max(common_parent_go_ids(terms, go), key=lambda t: go[t].depth) |
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def min_branch_length(go_id1, go_id2, go): |
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''' |
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Finds the minimum branch length between two terms in the GO DAG. |
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''' |
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# First get the deepest common ancestor |
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dca = deepest_common_ancestor([go_id1, go_id2], go) |
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# Then get the distance from the DCA to each term |
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dca_depth = go[dca].depth |
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d1 = go[go_id1].depth - dca_depth |
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d2 = go[go_id2].depth - dca_depth |
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# Return the total distance - i.e., to the deepest common ancestor and back. |
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return d1 + d2 |
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def semantic_distance(go_id1, go_id2, go): |
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''' |
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Finds the semantic distance (minimum number of connecting branches) |
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between two GO terms. |
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''' |
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return min_branch_length(go_id1, go_id2, go) |
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def semantic_similarity(go_id1, go_id2, go): |
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''' |
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Finds the semantic similarity (inverse of the semantic distance) |
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between two GO terms. |
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''' |
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return 1.0 / float(semantic_distance(go_id1, go_id2, go)) |
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tanghaibao /
goatools
