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# Copyright (c) 2015-2018 University of Oxford
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from __future__ import annotations
import collections
import logging
import typing
import numpy as np
import _tskit
import tskit
import tskit.trees as trees
import tskit.util as util
[docs]class Variant:
"""
A variant in a tree sequence, describing the observed genetic variation
among samples for a given site. A variant consists of (a) a tuple of
**alleles** listing the potential allelic states which samples at this site
can possess; (b) an array of **genotypes** mapping sample IDs to the observed
alleles (c) a reference to the :class:`Site` at which the Variant has been decoded
and (d) an array of **samples** giving the node id to which the each element of
the genotypes array corresponds.
After creation a Variant is not yet decoded, and has no genotypes.
To decode a Variant, call the :meth:`decode` method. The Variant class will then
use a Tree, internal to the Variant, to seek to the position of the site and
decode the genotypes at that site. It is therefore much more efficient to visit
sites in sequential genomic order, either in a forwards or backwards direction,
than to do so randomly.
Each element in the ``alleles`` tuple is a string, representing an
observed allelic state that may be seen at this site. The ``alleles`` tuple,
which is guaranteed not to contain any duplicates, is generated in one of two
ways. The first (and default) way is for ``tskit`` to generate the encoding on
the fly while generating genotypes. In this case, the first element of this
tuple is guaranteed to be the same as the site's ``ancestral_state`` value.
Note that allelic values may be listed that are not referred to by any
samples. For example, if we have a site that is fixed for the derived state
(i.e., we have a mutation over the tree root), all genotypes will be 1, but
the alleles list will be equal to ``('0', '1')``. Other than the
ancestral state being the first allele, the alleles are listed in
no particular order, and the ordering should not be relied upon
(but see the notes on missing data below).
The second way is for the user to define the mapping between
genotype values and allelic state strings using the
``alleles`` parameter to the :meth:`TreeSequence.variants` method.
In this case, there is no indication of which allele is the ancestral state,
as the ordering is determined by the user.
The ``genotypes`` represent the observed allelic states for each sample,
such that ``var.alleles[var.genotypes[j]]`` gives the string allele
for sample ID ``j``. Thus, the elements of the genotypes array are
indexes into the ``alleles`` list. The genotypes are provided in this
way via a numpy numeric array to enable efficient calculations. To obtain a
(less efficient) array of allele strings for each sample, you can use e.g.
``np.asarray(variant.alleles)[variant.genotypes]``.
When :ref:`missing data<sec_data_model_missing_data>` is present at a given
site, the property ``has_missing_data`` will be True, at least one element
of the ``genotypes`` array will be equal to ``tskit.MISSING_DATA``, and the
last element of the ``alleles`` array will be ``None``. Note that in this
case ``variant.num_alleles`` will **not** be equal to
``len(variant.alleles)``. The rationale for adding ``None`` to the end of
the ``alleles`` list is to help code that does not handle missing data
correctly fail early rather than introducing subtle and hard-to-find bugs.
As ``tskit.MISSING_DATA`` is equal to -1, code that decodes genotypes into
allelic values without taking missing data into account would otherwise
incorrectly output the last allele in the list.
:param TreeSequence tree_sequence: The tree sequence to which this variant
belongs.
:param array_like samples: An array of node IDs for which to generate
genotypes, or None for all sample nodes. Default: None.
:param bool isolated_as_missing: If True, the genotype value assigned to
missing samples (i.e., isolated samples without mutations) is
:data:`.MISSING_DATA` (-1). If False, missing samples will be
assigned the allele index for the ancestral state.
Default: True.
:param tuple alleles: A tuple of strings defining the encoding of
alleles as integer genotype values. At least one allele must be provided.
If duplicate alleles are provided, output genotypes will always be
encoded as the first occurrence of the allele. If None (the default),
the alleles are encoded as they are encountered during genotype
generation.
"""
def __init__(
self, tree_sequence, samples=None, isolated_as_missing=None, alleles=None
):
if isolated_as_missing is None:
isolated_as_missing = True
self.tree_sequence = tree_sequence
if samples is not None:
samples = util.safe_np_int_cast(samples, np.int32)
self._ll_variant = _tskit.Variant(
tree_sequence._ll_tree_sequence,
samples=samples,
isolated_as_missing=isolated_as_missing,
alleles=alleles,
)
def _check_decoded(self):
if self._ll_variant.site_id == tskit.NULL:
raise ValueError(
"This variant has not yet been decoded at a specific site, "
"call Variant.decode to set the site."
)
@property
def site(self) -> trees.Site:
"""
The Site object for the site at which this variant has been decoded.
"""
self._check_decoded()
return self.tree_sequence.site(self._ll_variant.site_id)
@property
def alleles(self) -> tuple[str | None, ...]:
"""
A tuple of the allelic values which samples can possess at the current
site. Unless an encoding of alleles is specified when creating this
variant instance, the first element of this tuple is always the site's
ancestral state.
"""
return self._ll_variant.alleles
@property
def samples(self) -> np.ndarray:
"""
A numpy array of the node ids whose genotypes will be returned
by the :meth:`genotypes` method.
"""
return self._ll_variant.samples
@property
def genotypes(self) -> np.ndarray:
"""
An array of indexes into the list ``alleles``, giving the
state of each sample at the current site.
"""
self._check_decoded()
return self._ll_variant.genotypes
@property
def isolated_as_missing(self) -> bool:
"""
True if isolated samples are decoded to missing data. If False, isolated
samples are decoded to the ancestral state.
"""
return self._ll_variant.isolated_as_missing
@property
def has_missing_data(self) -> bool:
"""
True if there is missing data for any of the
samples at the current site.
"""
alleles = self._ll_variant.alleles
return len(alleles) > 0 and alleles[-1] is None
@property
def num_missing(self) -> int:
"""
The number of samples with missing data at this site.
"""
return np.sum(self.genotypes == tskit.NULL)
@property
def num_alleles(self) -> int:
"""
The number of distinct alleles at this site. Note that this may
not be the same as the number of distinct values in the genotypes
array: firstly missing data is not counted as an allele, and secondly,
the site may contain mutations to alternative allele states (which are
counted in the number of alleles) without the mutation being inherited
by any of the samples.
"""
return len(self.alleles) - self.has_missing_data
# Deprecated alias to avoid breaking existing code.
@property
def position(self) -> float:
return self.site.position
# Deprecated alias to avoid breaking existing code.
@property
def index(self) -> int:
return self._ll_variant.site_id
# We need a custom eq for the numpy array
def __eq__(self, other) -> bool:
return (
isinstance(other, Variant)
and self.tree_sequence == other.tree_sequence
and self._ll_variant.site_id == other._ll_variant.site_id
and self._ll_variant.site_id != tskit.NULL
and self._ll_variant.alleles == other._ll_variant.alleles
and np.array_equal(self._ll_variant.genotypes, other._ll_variant.genotypes)
)
[docs] def decode(self, site_id) -> None:
"""
Decode the variant at the given site, setting the site ID, genotypes and
alleles to those of the site and samples of this Variant.
:param int site_id: The ID of the site to decode. This must be a valid site ID.
"""
self._ll_variant.decode(site_id)
[docs] def copy(self) -> Variant:
"""
Create a copy of this Variant. Note that calling :meth:`decode` on the
copy will fail as it does not take a copy of the internal tree.
:return: The copy of this Variant.
"""
variant_copy = Variant.__new__(Variant)
variant_copy.tree_sequence = self.tree_sequence
variant_copy._ll_variant = self._ll_variant.restricted_copy()
return variant_copy
[docs] def counts(self) -> typing.Counter[str | None]:
"""
Returns a :class:`python:collections.Counter` object providing counts for each
possible :attr:`allele <Variant.alleles>` at this site: i.e. the number of
samples possessing that allele among the set of samples specified when creating
this Variant (by default, this is all the sample nodes in the tree sequence).
Missing data is represented by an allelic state of ``None``.
:return: A counter of the number of samples associated with each allele.
"""
counts = collections.Counter()
if self.alleles[-1] is None:
# we have to treat the last element of the genotypes array as special
counts[None] = np.sum(self.genotypes == tskit.MISSING_DATA)
for i, allele in enumerate(self.alleles[:-1]):
counts[allele] = np.sum(self.genotypes == i)
else:
bincounts = np.bincount(self.genotypes, minlength=self.num_alleles)
for i, allele in enumerate(self.alleles):
counts[allele] = bincounts[i]
return counts
[docs] def frequencies(self, remove_missing=None) -> dict[str, float]:
"""
Return a dictionary mapping each possible :attr:`allele <Variant.alleles>`
at this site to the frequency of that allele: i.e. the number of samples
with that allele divided by the total number of samples, among the set of
samples specified when creating this Variant (by default, this is all the
sample nodes in the tree sequence). Note, therefore, that if a restricted set
of samples was specified on creation, the allele frequencies returned here
will *not* be the global allele frequencies in the whole tree sequence.
:param bool remove_missing: If True, only samples with non-missing data will
be counted in the total number of samples used to calculate the frequency,
and no information on the frequency of missing data is returned. Otherwise
(default), samples with missing data are included when calculating
frequencies.
:return: A dictionary mapping allelic states to the frequency of each allele
among the samples
"""
if remove_missing is None:
remove_missing = False
total = len(self.samples)
if remove_missing:
total -= self.num_missing
if total == 0:
logging.warning(
"No non-missing samples at this site, frequencies undefined"
)
return {
allele: count / total if total > 0 else np.nan
for allele, count in self.counts().items()
if not (allele is None and remove_missing)
}
def __str__(self) -> str:
"""
Return a plain text summary of the contents of a variant.
"""
try:
site_id = self.site.id
site_position = self.site.position
counts = self.counts()
freqs = self.frequencies()
rows = (
[
["Site id", str(site_id)],
["Site position", str(site_position)],
["Number of samples", str(len(self.samples))],
["Number of alleles", str(self.num_alleles)],
]
+ [
[
f"""Samples with allele {'missing' if k is None
else "'" + k + "'"}""",
f"{counts[k]} ({freqs[k] * 100:.2g}%)",
]
for k in self.alleles
]
+ [
["Has missing data", str(self.has_missing_data)],
["Isolated as missing", str(bool(self.isolated_as_missing))],
]
)
except ValueError as err:
rows = [[str(err), ""]]
return util.unicode_table(rows, title="Variant")
def _repr_html_(self) -> str:
"""
Return an html summary of a variant. Called by Jupyter notebooks
to render a Variant.
"""
return util.variant_html(self)
def __repr__(self):
d = {
"site": self.site,
"samples": self.samples,
"alleles": self.alleles,
"genotypes": self.genotypes,
"has_missing_data": self.has_missing_data,
"isolated_as_missing": self.isolated_as_missing,
}
return f"Variant({repr(d)})"
#
# Miscellaneous auxiliary methods.
#
def allele_remap(alleles_from, alleles_to):
# Returns an index map from the elements in one list (alleles_from)
# to the elements of another list (alleles_to).
#
# If some elements in alleles_from are not in alleles_to,
# then indices outside of alleles_to are used.
alleles_to = np.array(alleles_to, dtype="U")
alleles_from = np.array(alleles_from, dtype="U")
allele_map = np.empty_like(alleles_from, dtype="uint32")
overflow = len(alleles_to)
for i, allele in enumerate(alleles_from):
try:
# Use the index of the first matching element.
allele_map[i] = np.where(alleles_to == allele)[0][0]
except IndexError:
allele_map[i] = overflow
overflow += 1
return allele_map
