Add fastalignment metric

CHANGELOG.md

@@ -8,6 +8,16 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## Unreleased
+
+## New features
+
+-   Speed up alignment distances by pre-filtering. There are two filtering strategies: A (lossless) length-based filter
+    and a heuristic based on the expected penalty per mismatch. This is implemented in the `FastAlignmentDistanceCalculator`
+    class which supersedes the `AlignmentDistanceCalculator` class, which is now deprecated. Using the `"alignment"` metric
+    in `pp.ir_dist` now uses the `FastAlignmentDistanceCalculator` with only the lenght-based filter activated.
+    Using the `"fastalignment"` activates the heuristic, which is significantly faster, but results in some false-negatives.
+
 ## v0.14.0
 
 ### Breaking changes

docs/api.rst

@@ -301,3 +301,4 @@ distance metrics
    ir_dist.metrics.LevenshteinDistanceCalculator
    ir_dist.metrics.HammingDistanceCalculator
    ir_dist.metrics.AlignmentDistanceCalculator
+   ir_dist.metrics.FastAlignmentDistanceCalculator

src/scirpy/ir_dist/__init__.py

@@ -34,7 +34,7 @@ def IrNeighbors(*args, **kwargs):
 
 
 MetricType = Union[
-    Literal["alignment", "identity", "levenshtein", "hamming"],
+    Literal["alignment", "fastalignment", "identity", "levenshtein", "hamming"],
     metrics.DistanceCalculator,
 ]
 
@@ -50,7 +50,11 @@ def IrNeighbors(*args, **kwargs):
         See :class:`~scirpy.ir_dist.metrics.HammingDistanceCalculator`.
       * `alignment` -- Distance based on pairwise sequence alignments using the
         BLOSUM62 matrix. This option is incompatible with nucleotide sequences.
-        See :class:`~scirpy.ir_dist.metrics.AlignmentDistanceCalculator`.
+        See :class:`~scirpy.ir_dist.metrics.FastAlignmentDistanceCalculator`.
+        * `fastalignment` -- Distance based on pairwise sequence alignments using the
+        BLOSUM62 matrix. Faster implementation of `alignment` with some loss.
+        This option is incompatible with nucleotide sequences.
+        See :class:`~scirpy.ir_dist.metrics.FastAlignmentDistanceCalculator`.
       * any instance of :class:`~scirpy.ir_dist.metrics.DistanceCalculator`.
 """
 
@@ -59,7 +63,7 @@ def IrNeighbors(*args, **kwargs):
     All distances `> cutoff` will be replaced by `0` and eliminated from the sparse
     matrix. A sensible cutoff depends on the distance metric, you can find
     information in the corresponding docs. If set to `None`, the cutoff
-    will be `10` for the `alignment` metric, and `2` for `levenshtein` and `hamming`.
+    will be `10` for the `alignment` and `fastalignment` metric, and `2` for `levenshtein` and `hamming`.
     For the identity metric, the cutoff is ignored and always set to `0`.
 """
 
@@ -81,7 +85,9 @@ def _get_distance_calculator(metric: MetricType, cutoff: Union[int, None], *, n_
     if isinstance(metric, metrics.DistanceCalculator):
         dist_calc = metric
     elif metric == "alignment":
-        dist_calc = metrics.AlignmentDistanceCalculator(cutoff=cutoff, n_jobs=n_jobs, **kwargs)
+        dist_calc = metrics.FastAlignmentDistanceCalculator(cutoff=cutoff, n_jobs=n_jobs, estimated_penalty=0, **kwargs)
+    elif metric == "fastalignment":
+        dist_calc = metrics.FastAlignmentDistanceCalculator(cutoff=cutoff, n_jobs=n_jobs, **kwargs)
     elif metric == "identity":
         dist_calc = metrics.IdentityDistanceCalculator(cutoff=cutoff, **kwargs)
     elif metric == "levenshtein":

src/scirpy/ir_dist/metrics.py

@@ -12,7 +12,7 @@
 from scipy.sparse import coo_matrix, csr_matrix
 from tqdm.contrib.concurrent import process_map
 
-from scirpy.util import _doc_params, tqdm
+from scirpy.util import _doc_params, deprecated, tqdm
 
 _doc_params_parallel_distance_calculator = """\
 n_jobs
@@ -412,6 +412,12 @@ class AlignmentDistanceCalculator(ParallelDistanceCalculator):
         Gap extend penatly
     """
 
+    @deprecated(
+        """\
+        FastAlignmentDistanceCalculator achieves (depending on the settings) identical results
+        at a higher speed.
+        """
+    )
     def __init__(
         self,
         cutoff: Union[None, int] = None,
@@ -478,3 +484,209 @@ def _self_alignment_scores(self, seqs: Sequence) -> dict:
             dtype=int,
             count=len(seqs),
         )
+
+
+@_doc_params(params=_doc_params_parallel_distance_calculator)
+class FastAlignmentDistanceCalculator(ParallelDistanceCalculator):
+    """\
+    Calculates distance between sequences based on pairwise sequence alignment.
+
+    The distance between two sequences is defined as :math:`S_{{1,2}}^{{max}} - S_{{1,2}}`,
+    where :math:`S_{{1,2}}` is the alignment score of sequences 1 and 2 and
+    :math:`S_{{1,2}}^{{max}}` is the max. achievable alignment score of sequences 1 and 2.
+    :math:`S_{{1,2}}^{{max}}` is defined as :math:`\\min(S_{{1,1}}, S_{{2,2}})`.
+
+    The use of alignment-based distances is heavily inspired by :cite:`TCRdist`.
+
+    High-performance sequence alignments are calculated leveraging
+    the `parasail library <https://github.com/jeffdaily/parasail-python>`_ (:cite:`Daily2016`).
+
+    To speed up the computation, we pre-filter sequence pairs based on
+        a) differences in sequence length
+        b) the number of different characters, based on an estimate of the mismatch penalty (`estimated_penalty`).
+
+    The filtering based on `estimated_penalty` is a *heuristic* and *may lead to false negatives*, i.e. sequence
+    pairs that are actually below the distance cutoff, but are removed during pre-filtering. Sensible values for
+    `estimated_penalty` are depending on the substitution matrix, but higher values lead to a higher false negative rate.
+
+    We provide default values for BLOSUM and PAM matrices. The provided default values were obtained by testing different
+    alues on the Wu dataset (:cite:`Wu2020`) and selecting those that provided a reasonable balance between
+    speedup and loss. Loss stayed well under 10% in all our test cases with the default values, and speedup
+    increases with the number of cells.
+
+    While the length-based filter is always active, the filter based on different characters can be disabled by
+    setting the estimated penalty to zero. Using length-based filtering only, there won't be any false negatives
+    *unless with a substitution matrix in which a substitution results in a higher score than the corresponding match.*
+    Using the length-based filter only results in a substancially reduced speedup compared to combining it with
+    the `estimated_penalty` heuristic.
+
+    Choosing a cutoff:
+        Alignment distances need to be viewed in the light of the substitution matrix.
+        The alignment distance is the difference between the actual alignment
+        score and the max. achievable alignment score. For instance, a mutation
+        from *Leucine* (`L`) to *Isoleucine* (`I`) results in a BLOSUM62 score of `2`.
+        An `L` aligned with `L` achieves a score of `4`. The distance is, therefore, `2`.
+
+        On the other hand, a single *Tryptophane* (`W`) mutating into, e.g.
+        *Proline* (`P`) already results in a distance of `15`.
+
+        We are still lacking empirical data up to which distance a CDR3 sequence still
+        is likely to recognize the same antigen, but reasonable cutoffs are `<15`.
+
+    Choosing an expected penalty:
+        The choice of an expected penalty is likely influenced by similar considerations as the
+        other parameters. Essentially, this can be thought of as a superficial (dis)similarity
+        measure. A higher value more strongly penalizes mismatching characters and is more in line
+        with looking for closely related sequence pairs, while a lower value is more forgiving
+        and better suited when looking for more distantly related sequence pairs.
+
+    Parameters
+    ----------
+    cutoff
+        Will eleminate distances > cutoff to make efficient
+        use of sparse matrices. The default cutoff is `10`.
+    {params}
+    subst_mat
+        Name of parasail substitution matrix
+    gap_open
+        Gap open penalty
+    gap_extend
+        Gap extend penatly
+    estimated_penalty
+        Estimate of the average mismatch penalty
+    """
+
+    def __init__(
+        self,
+        cutoff: Union[None, int] = None,
+        *,
+        n_jobs: Union[int, None] = None,
+        block_size: int = 50,
+        subst_mat: str = "blosum62",
+        gap_open: int = 11,
+        gap_extend: int = 11,
+        estimated_penalty: float = None,
+    ):
+        if cutoff is None:
+            cutoff = 10
+        super().__init__(cutoff, n_jobs=n_jobs, block_size=block_size)
+        self.subst_mat = subst_mat
+        self.gap_open = gap_open
+        self.gap_extend = gap_extend
+
+        penalty_dict = {
+            "blosum30": 4.0,
+            "blosum35": 4.0,
+            "blosum40": 4.0,
+            "blosum45": 4.0,
+            "blosum50": 4.0,
+            "blosum55": 4.0,
+            "blosum60": 4.0,
+            "blosum62": 4.0,
+            "blosum65": 4.0,
+            "blosum70": 4.0,
+            "blosum75": 4.0,
+            "blosum80": 4.0,
+            "blosum85": 4.0,
+            "blosum90": 4.0,
+            "pam10": 8.0,
+            "pam20": 8.0,
+            "pam30": 8.0,
+            "pam40": 8.0,
+            "pam50": 8.0,
+            "pam60": 4.0,
+            "pam70": 4.0,
+            "pam80": 4.0,
+            "pam90": 4.0,
+            "pam100": 4.0,
+            "pam110": 2.0,
+            "pam120": 2.0,
+            "pam130": 2.0,
+            "pam140": 2.0,
+            "pam150": 2.0,
+            "pam160": 2.0,
+            "pam170": 2.0,
+            "pam180": 2.0,
+            "pam190": 2.0,
+            "pam200": 2.0,
+        }
+
+        if subst_mat not in penalty_dict.keys():
+            raise Exception("Invalid substitution matrix.")
+
+        self.estimated_penalty = estimated_penalty if estimated_penalty is not None else penalty_dict[subst_mat]
+
+    def _compute_block(self, seqs1, seqs2, origin):
+        import parasail
+
+        subst_mat = parasail.Matrix(self.subst_mat)
+        origin_row, origin_col = origin
+
+        square_matrix = seqs2 is None
+        if square_matrix:
+            seqs2 = seqs1
+
+        self_alignment_scores1 = self._self_alignment_scores(seqs1)
+        if square_matrix:
+            self_alignment_scores2 = self_alignment_scores1
+        else:
+            self_alignment_scores2 = self._self_alignment_scores(seqs2)
+
+        max_len_diff = ((self.cutoff - self.gap_open) / self.gap_extend) + 1
+
+        result = []
+        for row, s1 in enumerate(seqs1):
+            col_start = row if square_matrix else 0
+            profile = parasail.profile_create_16(s1, subst_mat)
+            len1 = len(s1)
+
+            for col, s2 in enumerate(seqs2[col_start:], start=col_start):
+                len_diff = abs(len1 - len(s2))
+                # No need to calculate diagonal values
+                if s1 == s2:
+                    result.append((1, origin_row + row, origin_col + col))
+                # Dismiss sequences based on length
+                elif len_diff <= max_len_diff:
+                    # Dismiss sequences that are too different
+                    if (
+                        self._num_different_characters(s1, s2, len_diff) * self.estimated_penalty
+                        + len_diff * self.gap_extend
+                        <= self.cutoff
+                    ):
+                        r = parasail.nw_scan_profile_16(profile, s2, self.gap_open, self.gap_extend)
+                        max_score = np.min([self_alignment_scores1[row], self_alignment_scores2[col]])
+                        d = max_score - r.score
+
+                        if d <= self.cutoff:
+                            result.append((d + 1, origin_row + row, origin_col + col))
+
+        return result
+
+    def _self_alignment_scores(self, seqs: Sequence) -> dict:
+        """Calculate self-alignments. We need them as reference values
+        to turn scores into dists
+        """
+        import parasail
+
+        return np.fromiter(
+            (
+                parasail.nw_scan_16(
+                    s,
+                    s,
+                    self.gap_open,
+                    self.gap_extend,
+                    parasail.Matrix(self.subst_mat),
+                ).score
+                for s in seqs
+            ),
+            dtype=int,
+            count=len(seqs),
+        )
+
+    def _num_different_characters(self, s1, s2, len_diff):
+        longer, shorter = (s1, s2) if len(s1) >= len(s2) else (s2, s1)
+
+        for c in shorter:
+            if c in longer:
+                longer = longer.replace(c, "", 1)
+        return len(longer) - len_diff