From cbc01d11f00a3a682caf69631a263f35397d73c7 Mon Sep 17 00:00:00 2001 From: felixpetschko <48593591+felixpetschko@users.noreply.github.com> Date: Sun, 8 Sep 2024 21:08:02 +0200 Subject: [PATCH] Speed up clonotype distance calculation (#470) * compute_distances new version added * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * data type for csr max computation set to int16 to allow negative values * data type also for csr_min set to int16 to allow negative values * raise AssertainError instead of assert False * matrix shape changed in lookup function * array size changed for v-gene and column matching * naming conventions * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * use self._chain_count2 for asymmetric matrix for filter_chain_count * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * reverse table assignment adjusted in lookup function * changed AssertionError to NotImplementedError * removed unnecessary for loop * refactored lookup function * refactored lookup function and added docstring * adapted docsting of lookup function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * j_gene matching implemented * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fixes typo in j_gene parameter * bind loop variables in function match_gene_segment * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * set datatypes of csr matrix arrays explicitely * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * docstring and data type checks added for csr_min and csr_max * Changed data types of indices and indptr arrays for the csr matrices in _dist_for_clonotype and lookup to int64 instead of int32. * Changed data type checks to max value checks * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * documentation adaptions * update pre-commit config * reformat * deleted print statement * Implemented allowing graph partitioning method "fastgreedy" * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * test case for j_gene added * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * refactored filter_chain_count and added documentation * documentation and refactoring * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * documentation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Rerun tutorial * Update CHANGELOG * Attempt to fix conda CI * update conda CI * Fix python version conda ci * override python version --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Gregor Sturm --- .github/workflows/conda.yaml | 13 +- CHANGELOG.md | 10 +- docs/tutorials/tutorial_3k_tcr.ipynb | 108 +---- src/scirpy/ir_dist/_clonotype_neighbors.py | 369 +++++++++++++----- src/scirpy/ir_dist/_util.py | 101 +++-- .../j_gene_test_data.h5ad | Bin 0 -> 5172419 bytes src/scirpy/tests/test_clonotypes.py | 21 + src/scirpy/tl/_clonotypes.py | 17 +- 8 files changed, 402 insertions(+), 237 deletions(-) create mode 100644 src/scirpy/tests/data/clonotypes_test_data/j_gene_test_data.h5ad diff --git a/.github/workflows/conda.yaml b/.github/workflows/conda.yaml index fe1da71aa..d4461b28a 100644 --- a/.github/workflows/conda.yaml +++ b/.github/workflows/conda.yaml @@ -22,27 +22,26 @@ jobs: matrix: include: - os: ubuntu-latest - python: "3.12" + python: "3.11" env: OS: ${{ matrix.os }} PYTHON: ${{ matrix.python }} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Setup Miniconda - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v3 with: - miniforge-variant: Mambaforge - miniforge-version: latest + mamba-version: "*" channels: conda-forge,bioconda channel-priority: strict - python-version: ${{ matrix.python-version }} + python-version: ${{ matrix.python }} - name: install conda build run: | - mamba install -y boa conda-verify + mamba install -y boa conda-verify python=${{ matrix.python }} shell: bash - name: build and test package diff --git a/CHANGELOG.md b/CHANGELOG.md index c29438c55..cd305992a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,20 +8,22 @@ 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] +## v0.18.0 ### Additions - Isotypically included B cells are now labelled as `receptor_subtype="IGH+IGK/L"` instead of `ambiguous` in `tl.chain_qc` ([#537](https://github.com/scverse/scirpy/pull/537)). - Added the `normalized_hamming` metric to `pp.ir_dist` that accounts for differences in CDR3 sequence length ([#512](https://github.com/scverse/scirpy/pull/512)). +- `tl.define_clonotype_clusters` now has an option to require J genes to match (`same_j_gene=True`) in addition to `same_v_gene`. ([#470](https://github.com/scverse/scirpy/pull/470)). ### Performance improvements -- The hamming distance was reimplemented with numba, achieving a significant speedup ([#512](https://github.com/scverse/scirpy/pull/512)). +- The hamming distance has been reimplemented with numba, achieving a significant speedup ([#512](https://github.com/scverse/scirpy/pull/512)). +- Clonotype clustering has been accelerated leveraging sparse matrix operations ([#470](https://github.com/scverse/scirpy/pull/470)). ### Fixes -- Fix that pl.clonotype_network couldn't use non-standard obsm key ([#545](https://github.com/scverse/scirpy/pull/545)). +- Fix that `pl.clonotype_network` couldn't use non-standard obsm key ([#545](https://github.com/scverse/scirpy/pull/545)). ### Other changes @@ -54,7 +56,7 @@ and this project adheres to [Semantic Versioning][]. ### Fixes -- Fix issue with detecting the number of available CPUs on MacOD ([#518](https://github.com/scverse/scirpy/pull/502)) +- Fix issue with detecting the number of available CPUs on MacOS ([#518](https://github.com/scverse/scirpy/pull/502)) ## v0.16.1 diff --git a/docs/tutorials/tutorial_3k_tcr.ipynb b/docs/tutorials/tutorial_3k_tcr.ipynb index 84c94e9ac..f15285067 100644 --- a/docs/tutorials/tutorial_3k_tcr.ipynb +++ b/docs/tutorials/tutorial_3k_tcr.ipynb @@ -880,30 +880,10 @@ "Computing sequence x sequence distance matrix for VJ sequences.\n", "Computing sequence x sequence distance matrix for VDJ sequences.\n", "Initializing lookup tables. \n", - "Computing clonotype x clonotype distances.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1526/1526 [00:00<00:00, 1526.57it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Computing clonotype x clonotype distances.\n", "Stored result in `mdata.obs[\"airr:clone_id\"]`.\n", "Stored result in `mdata.obs[\"airr:clone_id_size\"]`.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -1069,30 +1049,10 @@ "output_type": "stream", "text": [ "Initializing lookup tables. \n", - "Computing clonotype x clonotype distances.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1549/1549 [00:02<00:00, 570.15it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Computing clonotype x clonotype distances.\n", "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist\"]`.\n", "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist_size\"]`.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -1332,30 +1292,10 @@ "output_type": "stream", "text": [ "Initializing lookup tables. \n", - "Computing clonotype x clonotype distances.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1549/1549 [00:03<00:00, 508.19it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Computing clonotype x clonotype distances.\n", "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist_same_v\"]`.\n", "Stored result in `mdata.obs[\"airr:cc_aa_tcrdist_same_v_size\"]`.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -2697,7 +2637,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1490/1490 [00:00<00:00, 79003.75it/s]" + "100%|██████████| 1490/1490 [00:00<00:00, 84735.71it/s]" ] }, { @@ -2712,7 +2652,7 @@ "output_type": "stream", "text": [ "\n", - "100%|██████████| 1000/1000 [00:00<00:00, 2022.26it/s]\n" + "100%|██████████| 1000/1000 [00:00<00:00, 2187.13it/s]\n" ] }, { @@ -2823,7 +2763,7 @@ }, { "data": { - "image/png": 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" ] @@ -2926,7 +2866,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "ranking genes\n", + "ranking genes\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " finished (0:00:00)\n" ] }, @@ -2990,9 +2936,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:273: RuntimeWarning: divide by zero encountered in log2\n", + "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:272: RuntimeWarning: divide by zero encountered in log2\n", " logfoldchange = np.log2((case_mean_freq + global_minimum) / (control_mean_freq + global_minimum))\n", - "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:273: RuntimeWarning: divide by zero encountered in scalar divide\n", + "/home/sturm/projects/2020/scirpy/src/scirpy/tl/_clonotype_imbalance.py:272: RuntimeWarning: divide by zero encountered in scalar divide\n", " logfoldchange = np.log2((case_mean_freq + global_minimum) / (control_mean_freq + global_minimum))\n" ] } @@ -3254,29 +3200,9 @@ "output_type": "stream", "text": [ "Initializing lookup tables. \n", - "Computing clonotype x clonotype distances.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 1549/1549 [00:01<00:00, 839.84it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Computing clonotype x clonotype distances.\n", "Stored IR distance matrix in `adata.uns[\"ir_query_VDJDB_aa_identity\"]`.\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] } ], "source": [ @@ -3412,7 +3338,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 324/324 [00:00<00:00, 8583.86it/s]" + "100%|██████████| 324/324 [00:00<00:00, 8135.75it/s]" ] }, { diff --git a/src/scirpy/ir_dist/_clonotype_neighbors.py b/src/scirpy/ir_dist/_clonotype_neighbors.py index e2171b755..c948c617e 100644 --- a/src/scirpy/ir_dist/_clonotype_neighbors.py +++ b/src/scirpy/ir_dist/_clonotype_neighbors.py @@ -6,13 +6,12 @@ import pandas as pd import scipy.sparse as sp from scanpy import logging -from tqdm.contrib.concurrent import process_map from scirpy.get import _has_ir from scirpy.get import airr as get_airr -from scirpy.util import DataHandler, _get_usable_cpus, tqdm +from scirpy.util import DataHandler -from ._util import DoubleLookupNeighborFinder, merge_coo_matrices, reduce_and, reduce_or +from ._util import DoubleLookupNeighborFinder class ClonotypeNeighbors: @@ -24,6 +23,7 @@ def __init__( receptor_arms: Literal["VJ", "VDJ", "all", "any"], dual_ir: Literal["primary_only", "all", "any"], same_v_gene: bool = False, + same_j_gene: bool = False, match_columns: None | Sequence[str] = None, distance_key: str, sequence_key: str, @@ -34,6 +34,7 @@ def __init__( receptor configuration and calls clonotypes from this distance matrix """ self.same_v_gene = same_v_gene + self.same_j_gene = same_j_gene self.match_columns = match_columns self.receptor_arms = receptor_arms self.dual_ir = dual_ir @@ -67,8 +68,13 @@ def _make_clonotype_table(self, params: DataHandler) -> tuple[Mapping, pd.DataFr raise ValueError("Obs names need to be unique!") airr_variables = [self.sequence_key] + if self.same_v_gene: airr_variables.append("v_call") + + if self.same_j_gene: + airr_variables.append("j_call") + chains = [f"{arm}_{chain}" for arm, chain in itertools.product(self._receptor_arm_cols, self._dual_ir_cols)] obs = get_airr(params, airr_variables, chains) @@ -157,13 +163,28 @@ def _add_distance_matrices(self) -> None: self.clonotypes2, [x for x in self.clonotypes.columns if "v_call" in x], ) - self.neighbor_finder.add_distance_matrix( "v_gene", sp.identity(len(v_genes), dtype=bool, format="csr"), v_genes, # type: ignore ) + if self.same_j_gene: + # J gene distance matrix (identity mat) + j_genes = self._unique_values_in_multiple_columns( + self.clonotypes, [x for x in self.clonotypes.columns if "j_call" in x] + ) + if self.clonotypes2 is not None: + j_genes |= self._unique_values_in_multiple_columns( + self.clonotypes2, + [x for x in self.clonotypes.columns if "j_call" in x], + ) + self.neighbor_finder.add_distance_matrix( + "j_gene", + sp.identity(len(j_genes), dtype=bool, format="csr"), + j_genes, # type: ignore + ) + if self.match_columns is not None: match_columns_values = set(self.clonotypes["match_columns"].values) if self.clonotypes2 is not None: @@ -190,6 +211,13 @@ def _add_lookup_tables(self) -> None: "v_gene", dist_type="boolean", ) + if self.same_j_gene: + self.neighbor_finder.add_lookup_table( + f"{arm}_{i}_j_call", + f"{arm}_{i}_j_call", + "j_gene", + dist_type="boolean", + ) if self.match_columns is not None: self.neighbor_finder.add_lookup_table( @@ -208,34 +236,17 @@ def compute_distances(self) -> sp.csr_matrix: """ start = logging.info("Computing clonotype x clonotype distances.") # type: ignore n_clonotypes = self.clonotypes.shape[0] + clonotype_ids = np.arange(n_clonotypes) - # only use multiprocessing for sufficiently large datasets - # for small datasets the overhead is too large for a benefit - if self.n_jobs == 1 or n_clonotypes <= 2 * self.chunksize: - dist_rows = tqdm( - (self._dist_for_clonotype(i) for i in range(n_clonotypes)), - total=n_clonotypes, - ) - else: - logging.info( - "NB: Computation happens in chunks. The progressbar only advances " "when a chunk has finished. " - ) # type: ignore - - dist_rows = process_map( - self._dist_for_clonotype, - range(n_clonotypes), - max_workers=_get_usable_cpus(self.n_jobs), - chunksize=2000, - tqdm_class=tqdm, - ) + dist = self._dist_for_clonotype(clonotype_ids) - dist = sp.vstack(list(dist_rows)) dist.eliminate_zeros() logging.hint("Done computing clonotype x clonotype distances. ", time=start) return dist # type: ignore - def _dist_for_clonotype(self, ct_id: int) -> sp.csr_matrix: - """Compute neighboring clonotypes for a given clonotype. + def _dist_for_clonotype(self, ct_ids: np.ndarray[int]) -> sp.csr_matrix: + """Compute neighboring clonotypes for the given clonotypes. + Returns a clonotype x clonotype2 sparse distance matrix. Or operations use the min dist of two matching entries. And operations use the max dist of two matching entries. @@ -245,83 +256,249 @@ def _dist_for_clonotype(self, ct_id: int) -> sp.csr_matrix: has a sequence dist < threshold. If we require both receptors to match ("and"), the higher one should count. """ - # Lookup distances for current row - tmp_clonotypes = self.clonotypes2 if self.clonotypes2 is not None else self.clonotypes - lookup = {} # CDR3 distances - lookup_v = {} # V-gene distances - for tmp_arm in self._receptor_arm_cols: - chain_ids = [(1, 1)] if self.dual_ir == "primary_only" else [(1, 1), (2, 2), (1, 2), (2, 1)] + lookup = {} + chain_ids = [(1, 1)] if self.dual_ir == "primary_only" else [(1, 1), (2, 2), (1, 2), (2, 1)] + for receptor_arm in self._receptor_arm_cols: for c1, c2 in chain_ids: - lookup[(tmp_arm, c1, c2)] = self.neighbor_finder.lookup( - ct_id, - f"{tmp_arm}_{c1}", - f"{tmp_arm}_{c2}", + lookup[(receptor_arm, c1, c2)] = self.neighbor_finder.lookup( + ct_ids, + f"{receptor_arm}_{c1}", + f"{receptor_arm}_{c2}", ) - if self.same_v_gene: - lookup_v[(tmp_arm, c1, c2)] = self.neighbor_finder.lookup( - ct_id, - f"{tmp_arm}_{c1}_v_call", - f"{tmp_arm}_{c2}_v_call", - ) - - # need to loop through all coordinates that have at least one distance. - has_distance = merge_coo_matrices(lookup.values()).tocsr() # type: ignore - # convert to csr matrices to iterate over indices - lookup = {k: v.tocsr() for k, v in lookup.items()} - - def _lookup_dist_for_chains(tmp_arm: Literal["VJ", "VDJ"], c1: Literal[1, 2], c2: Literal[1, 2]): - """Lookup the distance between two chains of a given receptor - arm. Only considers those columns in the current row that - have an entry in `has_distance`. Returns a dense - array with dimensions (1, n) where n equals the number - of entries in `has_distance`. + id_len = len(ct_ids) + + first_value = next(iter(lookup.values())) + has_distance_table = sp.csr_matrix((id_len, first_value.shape[1])) + for value in lookup.values(): + has_distance_table += value + + has_distance_mask = has_distance_table + has_distance_mask.data = np.ones_like(has_distance_mask.data) + + def OR_min(a: sp.csr_matrix, b: sp.csr_matrix) -> sp.csr_matrix: """ - ct_col2 = tmp_clonotypes[f"{tmp_arm}_{c2}_{self.sequence_key}"].values - tmp_array = lookup[(tmp_arm, c1, c2)][0, has_distance.indices].todense().A1.astype(np.float16) - tmp_array[ct_col2[has_distance.indices] == "nan"] = np.nan - if self.same_v_gene: - mask_v_gene = lookup_v[(tmp_arm, c1, c2)][0, has_distance.indices] - tmp_array = np.multiply(tmp_array, mask_v_gene) - return tmp_array + Computes the element-wise minimum between 2 CSR matrices while ignoring 0 values. If 2 values + are compared and at least one of them is a 0, the maximum of the 2 values is taken instead of the minimum. + + To be able to use built-in functions, we shift the data arrays by the overall maximum value such that we get negative values. + Then we can use the built-in "<" function to compare the CSR matrices while ignoring 0 values. + """ + max_value_a = np.max(a.data, initial=0) + max_value_b = np.max(b.data, initial=0) + + if max_value_a > np.iinfo(np.uint8).max or max_value_b > np.iinfo(np.uint8).max: + raise ValueError("CSR matrix data values exceed maximum value for datatype uint8 (255).") + + max_value = np.int16(np.max([max_value_a, max_value_b]) + 1) + min_mat_a = sp.csr_matrix((a.data.astype(np.int16), a.indices, a.indptr), shape=a.shape) + min_mat_a.data -= max_value + min_mat_b = sp.csr_matrix((b.data.astype(np.int16), b.indices, b.indptr), shape=b.shape) + min_mat_b.data -= max_value + a_smaller_b = min_mat_a < min_mat_b + min_result = b + (a - b).multiply(a_smaller_b) + return min_result + + def AND_max(a, b): + """ + Computes the element-wise maximum between 2 CSR matrices while handling 0 values differently. If 2 values + are compared and at least one of them is a 0, the minimum (=0) of the 2 values is taken instead of the maximum. + + To be able to use built-in functions, we shift the data arrays by the overall maximum value such that we get negative values. + Then we can use the built-in ">" function to compare the CSR matrices while handling the special case for 0 values at + the same time. + """ + max_value_a = np.max(a.data, initial=0) + max_value_b = np.max(b.data, initial=0) + + if max_value_a > np.iinfo(np.uint8).max or max_value_b > np.iinfo(np.uint8).max: + raise ValueError("CSR matrix data values exceed maximum value for datatype uint8 (255).") + + max_value = np.int16(np.max([max_value_a, max_value_b]) + 1) + max_mat_a = sp.csr_matrix((a.data.astype(np.int16), a.indices, a.indptr), shape=a.shape) + max_mat_a.data -= max_value + max_mat_b = sp.csr_matrix((b.data.astype(np.int16), b.indices, b.indptr), shape=b.shape) + max_mat_b.data -= max_value + a_greater_b = max_mat_a > max_mat_b + max_result = b + (a - b).multiply(a_greater_b) + return max_result + + if self.match_columns is not None: + # Create a mask to filter clonotype pairs based on having similar entries in given columns + distance_matrix_name, forward, _ = self.neighbor_finder.lookups["match_columns"] + distance_matrix_name_reverse, _, reverse = self.neighbor_finder.lookups["match_columns"] + if distance_matrix_name != distance_matrix_name_reverse: + raise ValueError("Forward and reverse lookup tablese must be defined " "on the same distance matrices.") + reverse_lookup_values = np.vstack(list(reverse.lookup.values())) + reverse_lookup_keys = np.zeros(reverse.size, dtype=np.int64) + reverse_lookup_keys[list(reverse.lookup.keys())] = np.arange(len(list(reverse.lookup.keys()))) + match_column_mask = sp.csr_matrix( + (np.empty(len(has_distance_mask.indices)), has_distance_mask.indices, has_distance_mask.indptr), + shape=has_distance_mask.shape, + ) + has_distance_mask_coo = match_column_mask.tocoo() + indices_in_dist_mat = forward[has_distance_mask_coo.row] + match_column_mask.data = reverse_lookup_values[ + reverse_lookup_keys[indices_in_dist_mat], has_distance_mask_coo.col + ] + + receptor_arm_res = {} + dist_mats_chains = {} + + def filter_chain_count_data( + dist_mat_coo, + chain_counts_a, + chain_counts_b, + ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Helper function for filter_chain_count. Computes the data + arrays for the csr matrices that we want to filter by chain count. + """ + filtered_data_stacked = np.array( + [np.zeros_like(dist_mat_coo.data), np.zeros_like(dist_mat_coo.data), np.zeros_like(dist_mat_coo.data)] + ) + + ct_pair_ids_a = dist_mat_coo.row + ct_pair_ids_b = dist_mat_coo.col + + data_array_indices = np.arange(len(dist_mat_coo.data)) + chain_counts_pair_a = chain_counts_a[ct_pair_ids_a] + chain_counts_pair_b = chain_counts_b[ct_pair_ids_b] + chain_counts_equal = chain_counts_pair_a == chain_counts_pair_b + + filtered_data_stacked[chain_counts_pair_a[chain_counts_equal], data_array_indices[chain_counts_equal]] = ( + dist_mat_coo.data[chain_counts_equal] + ) + + return ( + filtered_data_stacked[0], + filtered_data_stacked[1], + filtered_data_stacked[2], + ) + + def filter_chain_count( + tmp_dist_mat: sp.csr_matrix, col: str + ) -> tuple[sp.csr_matrix, sp.csr_matrix, sp.csr_matrix]: + """ + Filters a temporary clonotype distance matrix based on the count of receptor chains. + We want to keep clonotype pairs that both have the same number of chains which can be either 0, 1, or 2 chains. + Return 3 matrices: pairs with both 0 chains, pairs with both 1 chain, pairs with both 2 chains; + """ + chain_counts_a = self._chain_count[col] + + if self._chain_count2 is None: + chain_counts_b = chain_counts_a + else: + chain_counts_b = self._chain_count2[col] + + dist_mat_coo = tmp_dist_mat.tocoo() + + filtered_chain_count0, filtered_chain_count1, filtered_chain_count2 = ( + tmp_dist_mat.copy(), + tmp_dist_mat.copy(), + tmp_dist_mat.copy(), + ) + filtered_chain_count0.data, filtered_chain_count1.data, filtered_chain_count2.data = ( + filter_chain_count_data( + dist_mat_coo, + chain_counts_a, + chain_counts_b, + ) + ) + return filtered_chain_count0, filtered_chain_count1, filtered_chain_count2 + + def match_gene_segment( + tmp_dist_mat: sp.csr_matrix, tmp_arm: str, c1: int, c2: int, segment_suffix: Literal["v_call", "j_call"] + ) -> sp.csr_matrix: + """ + Filters a temporary clonotype distance matrix based on gene segement similarity (e.g. v_gene or j_gene). + We want to keep clonotype pairs with the same gene segment. + """ + distance_matrix_name, forward, _ = self.neighbor_finder.lookups[f"{tmp_arm}_{c1}_{segment_suffix}"] + distance_matrix_name_reverse, _, reverse = self.neighbor_finder.lookups[f"{tmp_arm}_{c2}_{segment_suffix}"] + if distance_matrix_name != distance_matrix_name_reverse: + raise ValueError("Forward and reverse lookup tablese must be defined " "on the same distance matrices.") + empty_row = np.array([np.zeros(reverse.size, dtype=bool)]) + reverse_lookup_values = np.vstack((*reverse.lookup.values(), empty_row)) + reverse_lookup_keys = np.full(id_len, -1, dtype=np.int32) + keys_array = np.fromiter(reverse.lookup.keys(), dtype=int, count=len(reverse.lookup)) + reverse_lookup_keys[keys_array] = np.arange(len(keys_array)) + gene_segment_mask = sp.csr_matrix( + (np.empty(len(has_distance_mask.indices)), has_distance_mask.indices, has_distance_mask.indptr), + shape=has_distance_mask.shape, + ) + has_distance_mask_coo = gene_segment_mask.tocoo() + indices_in_dist_mat = forward[has_distance_mask_coo.row] + gene_segment_mask.data = reverse_lookup_values[ + reverse_lookup_keys[indices_in_dist_mat], has_distance_mask_coo.col + ] + return tmp_dist_mat.multiply(gene_segment_mask) + + # Now we merge the distances of chains. + # Can first be filtered based on v_gene and j_gene similarity and other column similarity. + # We also need to filter based on chain count similarity. + # Then we reduce the temporary clonotype distance matrices of the receptor chains with an AND/OR logic. + for receptor_arm in self._receptor_arm_cols: + for c1, c2 in chain_ids: + tmp_dist_mat = lookup[(receptor_arm, c1, c2)][ct_ids] + + if not (self.same_v_gene or self.same_j_gene or self.match_columns): + tmp_dist_mat = tmp_dist_mat.multiply(has_distance_mask) + + if self.same_v_gene: + tmp_dist_mat = match_gene_segment(tmp_dist_mat, receptor_arm, c1, c2, segment_suffix="v_call") + + if self.same_j_gene: + tmp_dist_mat = match_gene_segment(tmp_dist_mat, receptor_arm, c1, c2, segment_suffix="j_call") + + if self.match_columns is not None: + tmp_dist_mat = tmp_dist_mat.multiply(match_column_mask) + + if self.dual_ir == "all": + filtered0, filtered1, filtered2 = filter_chain_count(tmp_dist_mat, receptor_arm) + dist_mats_chains[(receptor_arm, c1, c2, 0)] = filtered0 + dist_mats_chains[(receptor_arm, c1, c2, 1)] = filtered1 + dist_mats_chains[(receptor_arm, c1, c2, 2)] = filtered2 + else: + dist_mats_chains[(receptor_arm, c1, c2)] = tmp_dist_mat - # Merge the distances of chains - res = [] - for tmp_arm in self._receptor_arm_cols: if self.dual_ir == "primary_only": - tmp_res = _lookup_dist_for_chains(tmp_arm, 1, 1) - elif self.dual_ir == "all": - tmp_res = reduce_or( - reduce_and( - _lookup_dist_for_chains(tmp_arm, 1, 1), - _lookup_dist_for_chains(tmp_arm, 2, 2), - chain_count=self._chain_count[tmp_arm][ct_id], - ), - reduce_and( - _lookup_dist_for_chains(tmp_arm, 1, 2), - _lookup_dist_for_chains(tmp_arm, 2, 1), - chain_count=self._chain_count[tmp_arm][ct_id], - ), + receptor_arm_res[receptor_arm] = dist_mats_chains[(receptor_arm, 1, 1)] + elif self.dual_ir == "any": + receptor_arm_res[receptor_arm] = OR_min( + OR_min(dist_mats_chains[(receptor_arm, 1, 1)], dist_mats_chains[(receptor_arm, 1, 2)]), + OR_min(dist_mats_chains[(receptor_arm, 2, 1)], dist_mats_chains[(receptor_arm, 2, 2)]), ) - else: # "any" - tmp_res = reduce_or( - _lookup_dist_for_chains(tmp_arm, 1, 1), - _lookup_dist_for_chains(tmp_arm, 1, 2), - _lookup_dist_for_chains(tmp_arm, 2, 2), - _lookup_dist_for_chains(tmp_arm, 2, 1), + elif self.dual_ir == "all": + receptor_arm_res[receptor_arm] = OR_min( + AND_max(dist_mats_chains[(receptor_arm, 1, 1, 2)], dist_mats_chains[(receptor_arm, 2, 2, 2)]), + AND_max(dist_mats_chains[(receptor_arm, 2, 1, 2)], dist_mats_chains[(receptor_arm, 1, 2, 2)]), ) - res.append(tmp_res) - - # Merge the distances of arms. - reduce_fun = reduce_and if self.receptor_arms == "all" else reduce_or - # checking only the chain=1 columns here is enough, as there must not - # be a secondary chain if there is no first one. - res = reduce_fun(np.vstack(res), chain_count=self._chain_count["arms"][ct_id]) + receptor_arm_res[receptor_arm] += ( + dist_mats_chains[(receptor_arm, 1, 1, 1)] + dist_mats_chains[(receptor_arm, 1, 1, 0)] + ) + else: + raise NotImplementedError(f"self.dual_ir method {self.dual_ir} is not implemented") - if self.match_columns is not None: - match_columns_mask = self.neighbor_finder.lookup(ct_id, "match_columns", "match_columns") - res = np.multiply(res, match_columns_mask[0, has_distance.indices]) + if len(receptor_arm_res) == 1: + ct_dist_mat = receptor_arm_res[self._receptor_arm_cols[0]] + else: + if self.receptor_arms == "all": + arm_res_filtered = {} + arm_res_filtered[("VJ", 0)], arm_res_filtered[("VJ", 1)], arm_res_filtered[("VJ", 2)] = ( + filter_chain_count(receptor_arm_res["VJ"], "arms") + ) + arm_res_filtered[("VDJ", 0)], arm_res_filtered[("VDJ", 1)], arm_res_filtered[("VDJ", 2)] = ( + filter_chain_count(receptor_arm_res["VDJ"], "arms") + ) + ct_dist_mat = AND_max(arm_res_filtered[("VJ", 2)], arm_res_filtered[("VDJ", 2)]) + ct_dist_mat += ( + arm_res_filtered[("VJ", 0)] + + arm_res_filtered[("VJ", 1)] + + arm_res_filtered[("VDJ", 0)] + + arm_res_filtered[("VDJ", 1)] + ) - final_res = has_distance.copy() - final_res.data = res.astype(np.uint8) - return final_res + else: + ct_dist_mat = OR_min(receptor_arm_res["VJ"], receptor_arm_res["VDJ"]) + return ct_dist_mat diff --git a/src/scirpy/ir_dist/_util.py b/src/scirpy/ir_dist/_util.py index b0c3bbeb2..08ffa9228 100644 --- a/src/scirpy/ir_dist/_util.py +++ b/src/scirpy/ir_dist/_util.py @@ -233,61 +233,90 @@ def n_cols(self): def lookup( self, - object_id: int, - forward_lookup_table: str, - reverse_lookup_table: str | None = None, - ) -> coo_matrix | np.ndarray: - """Get ids of neighboring objects from a lookup table. - - Performs the following lookup: + object_ids: np.ndarray[int], + forward_lookup_table_name: str, + reverse_lookup_table_name: str | None = None, + ) -> sp.csr_matrix: + """ + Creates a distance matrix between objects with the given ids based on a feature distance matrix. - object_id -> dist_mat -> neighboring features -> neighboring objects. + To get the distance between two objects we need to look up the features of the two objects. + The distance between those two features is then the distance between the two objects. - where an object is a clonotype in our case (but could be used for something else) + To do so, we first use the `object_ids` together with the `forward_lookup_table` to look up + the indices of the objects in the feature `distance_matrix`. Afterwards we pick the according row for each object + out of the `distance_matrix` and construct a `rows` matrix (n_object_ids x n_features). - "nan"s are not looked up via the distance matrix, they return a row of zeros + "nan"s (index = -1) are not looked up in the feature `distance_matrix`, they return a row of zeros instead. + Then we use the entries of the `reverse_lookup_table` to construct a `reverse_lookup_matrix` (n_features x n_object_ids). + By multiplying the `rows` matrix with the `reverse_lookup_matrix` we get the final `object_distance_matrix` that shows + the distances between the objects with the given `object_ids` regarding a certain feature column. + + It might not be obvious at the first sight that the matrix multiplication between `rows` and `reverse_lookup_matrix` gives + us the desired result. But this trick allows us to use the built-in sparse matrix multiplication of `scipy.sparse` + for enhanced performance. + Parameters ---------- - object_id - The row index of the feature_table. - forward_lookup_table + object_ids + The row indices of the feature_table. + forward_lookup_table_name The unique identifier of a lookup table previously added via `add_lookup_table`. - reverse_lookup_table + reverse_lookup_table_name The unique identifier of the lookup table used for the reverse lookup. If not provided will use the same lookup table for forward and reverse lookup. This is useful to calculate distances across features from different columns of the feature table (e.g. primary and secondary VJ chains). + + Returns + ------- + object_distance_matrix + A CSR matrix containing the pairwise distances between objects with the + given `object_ids` regarding a certain feature column. """ - distance_matrix_name, forward, reverse = self.lookups[forward_lookup_table] + distance_matrix_name, forward_lookup_table, reverse_lookup_table = self.lookups[forward_lookup_table_name] - if reverse_lookup_table is not None: - distance_matrix_name_reverse, _, reverse = self.lookups[reverse_lookup_table] + if reverse_lookup_table_name is not None: + distance_matrix_name_reverse, _, reverse_lookup_table = self.lookups[reverse_lookup_table_name] if distance_matrix_name != distance_matrix_name_reverse: raise ValueError("Forward and reverse lookup tablese must be defined " "on the same distance matrices.") distance_matrix = self.distance_matrices[distance_matrix_name] - idx_in_dist_mat = forward[object_id] - if idx_in_dist_mat == -1: # nan - return reverse.empty() - else: - # get distances from the distance matrix... - row = distance_matrix[idx_in_dist_mat, :] - - if reverse.is_boolean: - assert ( - len(row.indices) == 1 # type: ignore - ), "Boolean reverse lookup only works for identity distance matrices." - return reverse[row.indices[0]] # type: ignore - else: - # ... and get column indices directly from sparse row - # sum concatenates coo matrices - return merge_coo_matrices( - (reverse[i] * multiplier for i, multiplier in zip(row.indices, row.data, strict=False)), # type: ignore - shape=(1, reverse.size), - ) + + if np.max(distance_matrix.data) > np.iinfo(np.uint8).max: + raise OverflowError( + "The data values in the distance scipy.sparse.csr_matrix exceed the maximum value for uint8 (255)" + ) + + indices_in_dist_mat = forward_lookup_table[object_ids] + indptr = np.empty(distance_matrix.indptr.shape[0] + 1, dtype=np.int64) + indptr[:-1] = distance_matrix.indptr + indptr[-1] = indptr[-2] + distance_matrix_extended = sp.csr_matrix( + (distance_matrix.data.astype(np.uint8), distance_matrix.indices, indptr), + shape=(distance_matrix.shape[0] + 1, distance_matrix.shape[1]), + ) + rows = distance_matrix_extended[indices_in_dist_mat, :] + + reverse_matrix_data = [np.array([], dtype=np.uint8)] * rows.shape[1] + reverse_matrix_col = [np.array([], dtype=np.int64)] * rows.shape[1] + nnz_array = np.zeros(rows.shape[1], dtype=np.int64) + + for key, value in reverse_lookup_table.lookup.items(): + reverse_matrix_data[key] = value.data + reverse_matrix_col[key] = value.col + nnz_array[key] = value.nnz + + data = np.concatenate(reverse_matrix_data) + col = np.concatenate(reverse_matrix_col) + indptr = np.concatenate([np.array([0], dtype=np.int64), np.cumsum(nnz_array)]) + + reverse_matrix = sp.csr_matrix((data, col, indptr), shape=(rows.shape[1], reverse_lookup_table.size)) + object_distance_matrix = rows * reverse_matrix + return object_distance_matrix def add_distance_matrix( self, diff --git a/src/scirpy/tests/data/clonotypes_test_data/j_gene_test_data.h5ad b/src/scirpy/tests/data/clonotypes_test_data/j_gene_test_data.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..43d5888662a032ed976fc061d1e416e29b89e7a8 GIT binary patch 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