diff --git a/api/python/cellxgene_census/pyproject.toml b/api/python/cellxgene_census/pyproject.toml index 2904089ff..f050bef75 100644 --- a/api/python/cellxgene_census/pyproject.toml +++ b/api/python/cellxgene_census/pyproject.toml @@ -48,6 +48,9 @@ experimental = [ "psutil~=5.0", "datasets~=2.0", "tdigest~=0.5", + # choose newest version of tiledb-vector-search that doesn't need a newer version of tiledb + # than tiledbsoma: https://github.com/TileDB-Inc/TileDB-Vector-Search/blob/0.2.2/pyproject.toml + "tiledb-vector-search~=0.2", # Not expressible in pyproject.toml: #"git+https://huggingface.co/ctheodoris/Geneformer", # instead, experimental/ml/geneformer_tokenizer.py catches ImportError to ask user to install that. diff --git a/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py b/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py index 5ba8b77fb..523603ea5 100644 --- a/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py +++ b/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py @@ -133,6 +133,7 @@ class CensusMirror(TypedDict): provider: Provider base_uri: str region: Optional[str] + embeddings_base_uri: str CensusMirrors = Dict[CensusMirrorName, Union[CensusMirrorName, CensusMirror]] diff --git a/api/python/cellxgene_census/src/cellxgene_census/experimental/__init__.py b/api/python/cellxgene_census/src/cellxgene_census/experimental/__init__.py index 4a65cb3ab..eabcc74c9 100644 --- a/api/python/cellxgene_census/src/cellxgene_census/experimental/__init__.py +++ b/api/python/cellxgene_census/src/cellxgene_census/experimental/__init__.py @@ -7,6 +7,7 @@ get_embedding_metadata, get_embedding_metadata_by_name, ) +from ._embedding_search import NeighborObs, find_nearest_obs, predict_obs_metadata __all__ = [ "get_embedding", @@ -14,4 +15,7 @@ "get_embedding_metadata_by_name", "get_all_available_embeddings", "get_all_census_versions_with_embedding", + "find_nearest_obs", + "NeighborObs", + "predict_obs_metadata", ] diff --git a/api/python/cellxgene_census/src/cellxgene_census/experimental/_embedding_search.py b/api/python/cellxgene_census/src/cellxgene_census/experimental/_embedding_search.py new file mode 100644 index 000000000..6290f418b --- /dev/null +++ b/api/python/cellxgene_census/src/cellxgene_census/experimental/_embedding_search.py @@ -0,0 +1,167 @@ +"""Nearest-neighbor search based on vector index of Census embeddings.""" + +from contextlib import ExitStack +from typing import Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, cast + +import anndata as ad +import numpy as np +import numpy.typing as npt +import pandas as pd +import tiledb.vector_search as vs +import tiledbsoma as soma + +from .._experiment import _get_experiment_name +from .._open import DEFAULT_TILEDB_CONFIGURATION, open_soma +from .._release_directory import CensusMirror, _get_census_mirrors +from .._util import _uri_join +from ._embedding import get_embedding_metadata_by_name + + +class NeighborObs(NamedTuple): + """Results of nearest-neighbor search for Census obs embeddings.""" + + distances: npt.NDArray[np.float32] + """ + Distances to the nearest neighbors for each query obs embedding (q by k, where q is the number + of query embeddings and k is the desired number of neighbors). The distance metric is + implementation-dependent. + """ + + neighbor_ids: npt.NDArray[np.int64] + """ + obs soma_joinid's of the nearest neighbors for each query embedding (q by k). + """ + + +def find_nearest_obs( + embedding_name: str, + organism: str, + census_version: str, + query: ad.AnnData, + *, + k: int = 10, + nprobe: int = 100, + memory_GiB: int = 4, + mirror: Optional[str] = None, + embedding_metadata: Optional[Dict[str, Any]] = None, + **kwargs: Dict[str, Any], +) -> NeighborObs: + """Search Census for similar obs (cells) based on nearest neighbors in embedding space. + + Args: + embedding_name, organism, census_version: + Identify the embedding to search, as in :func:`get_embedding_metadata_by_name`. + query: + AnnData object with an obsm layer embedding the query cells. The obsm layer name + matches ``embedding_metadata["embedding_name"]`` (e.g. scvi, geneformer). The layer + shape matches the number of query cells and the number of features in the embedding. + k: + Number of nearest neighbors to return for each query obs. + nprobe: + Sensitivity parameter; defaults to 100 (roughly N^0.25 where N is the number of Census + cells) for a thorough search. Decrease for faster but less accurate search. + memory_GiB: + Memory budget for the search index, in gibibytes; defaults to 4 GiB. + mirror: + Name of the Census mirror to use for the search. + embedding_metadata: + The result of `get_embedding_metadata_by_name(embedding_name, organism, census_version)`. + Supplying this saves a network request for repeated searches. + """ + if embedding_metadata is None: + embedding_metadata = get_embedding_metadata_by_name(embedding_name, organism, census_version) + assert embedding_metadata["embedding_name"] == embedding_name + n_features = embedding_metadata["n_features"] + + # validate query (expected obsm layer exists with the expected dimensionality) + if embedding_name not in query.obsm: + raise ValueError(f"Query does not have the expected layer {embedding_name}") + if query.obsm[embedding_name].shape[1] != n_features: + raise ValueError( + f"Query embedding {embedding_name} has {query.obsm[embedding_name].shape[1]} features, expected {n_features}" + ) + + # formulate index URI and run query + resolved_index = _resolve_embedding_index(embedding_metadata, mirror=mirror) + if not resolved_index: + raise ValueError("No suitable embedding index found for " + embedding_name) + index_uri, index_region = resolved_index + config = {k: str(v) for k, v in DEFAULT_TILEDB_CONFIGURATION.items()} + config["vfs.s3.region"] = index_region + memory_vectors = memory_GiB * (2**30) // (4 * n_features) # number of float32 vectors + index = vs.ivf_flat_index.IVFFlatIndex(uri=index_uri, config=config, memory_budget=memory_vectors) + distances, neighbor_ids = index.query(query.obsm[embedding_name], k=k, nprobe=nprobe, **kwargs) + + return NeighborObs(distances=distances, neighbor_ids=neighbor_ids) + + +def _resolve_embedding_index( + embedding_metadata: Dict[str, Any], + mirror: Optional[str] = None, +) -> Optional[Tuple[str, str]]: + index_metadata = embedding_metadata.get("indexes", None) + if not index_metadata: + return None + # TODO (future): support multiple index [types] + assert index_metadata[0]["type"] == "IVFFlat", "Only IVFFlat index is supported (update cellxgene_census)" + mirrors = _get_census_mirrors() + mirror = mirror or cast(str, mirrors["default"]) + mirror_info = cast(CensusMirror, mirrors[mirror]) + uri = _uri_join(mirror_info["embeddings_base_uri"], index_metadata[0]["relative_uri"]) + return uri, cast(str, mirror_info["region"]) + + +def predict_obs_metadata( + organism: str, + census_version: str, + neighbors: NeighborObs, + column_names: Sequence[str], + experiment: Optional[soma.Experiment] = None, +) -> pd.DataFrame: + """Predict obs metadata attributes for the query cells based on the embedding nearest neighbors. + + Args: + organism, census_version: + Embedding information as supplied to :func:`find_nearest_obs`. + neighbors: + Results of a :func:`find_nearest_obs` search. + column_names: + Desired obs metadata column names. The current implementation is suitable for + categorical attributes (e.g. cell_type, tissue_general). + experiment: + Open handle for the relevant SOMAExperiment, if available (otherwise, will be opened + internally). e.g. ``census["census_data"]["homo_sapiens"]`` with the relevant Census + version. + + Returns: + Pandas DataFrame with the desired column predictions. Additionally, for each predicted + column ``col``, an additional column ``col_confidence`` with a confidence score between 0 + and 1. + """ + with ExitStack() as cleanup: + if experiment is None: + # open Census transiently + census = cleanup.enter_context(open_soma(census_version=census_version)) + experiment = census["census_data"][_get_experiment_name(organism)] + + # fetch the desired obs metadata for all of the found neighbors + neighbor_obs = ( + experiment.obs.read( + coords=(neighbors.neighbor_ids.flatten(),), column_names=(["soma_joinid"] + list(column_names)) + ) + .concat() + .to_pandas() + ).set_index("soma_joinid") + + # step through query cells to generate prediction for each column as the plurality value + # found among its neighbors, with a confidence score based on the simple fraction (for now) + # TODO: something more intelligent for numeric columns! also use distances, etc. + out: Dict[str, List[Any]] = {} + for i in range(neighbors.neighbor_ids.shape[0]): + neighbors_i = neighbor_obs.loc[neighbors.neighbor_ids[i]] + for col in column_names: + col_value_counts = neighbors_i[col].value_counts(normalize=True) + out.setdefault(col, []).append(col_value_counts.idxmax()) + out.setdefault(col + "_confidence", []).append(col_value_counts.max()) + + return pd.DataFrame.from_dict(out) diff --git a/api/python/cellxgene_census/tests/experimental/test_embeddings_search.py b/api/python/cellxgene_census/tests/experimental/test_embeddings_search.py new file mode 100644 index 000000000..b3c0f6f77 --- /dev/null +++ b/api/python/cellxgene_census/tests/experimental/test_embeddings_search.py @@ -0,0 +1,240 @@ +import json +from typing import Any, Dict, List + +import anndata as ad +import numpy as np +import pytest + +import cellxgene_census +from cellxgene_census.experimental import ( + NeighborObs, + find_nearest_obs, + predict_obs_metadata, +) + + +@pytest.mark.experimental +@pytest.mark.live_corpus +def test_embeddings_search(true_neighbors: Dict[str, Any], query_result: NeighborObs) -> None: + # check result shapes + rslt = query_result + assert isinstance(rslt.neighbor_ids, np.ndarray) + assert rslt.neighbor_ids.dtype == np.uint64 + assert rslt.neighbor_ids.shape == (len(true_neighbors), TRUE_NEAREST_NEIGHBORS_K) + assert isinstance(rslt.distances, np.ndarray) + assert rslt.distances.dtype == np.float32 + assert rslt.distances.shape == (len(true_neighbors), TRUE_NEAREST_NEIGHBORS_K) + + # compute Jaccard index for the true neighbors & those returned by the query + true_ids = set() + for ns in true_neighbors.values(): + true_ids |= {n["cell_id"] for n in ns} + rslt_ids = set(rslt.neighbor_ids.flatten()) + jaccard = len(true_ids & rslt_ids) / len(true_ids | rslt_ids) + + # Jaccard threshold: the search is approximate, and we set nprobe to a low value to speed up + # the test + assert jaccard >= 0.92 + + return + + +@pytest.mark.experimental +@pytest.mark.live_corpus +def test_embeddings_search_errors(query_anndata: ad.AnnData) -> None: + # bogus embedding name + with pytest.raises(ValueError, match="No embeddings found"): + find_nearest_obs( + "bogus123", TRUE_NEAREST_NEIGHBORS_ORGANISM, TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION, query_anndata + ) + # query anndata missing the embedding layer + bogus_ad = query_anndata.copy() + bogus_ad.obsm.pop(TRUE_NEAREST_NEIGHBORS_EMBEDDING_NAME) + with pytest.raises(ValueError, match="Query does not have"): + find_nearest_obs( + TRUE_NEAREST_NEIGHBORS_EMBEDDING_NAME, + TRUE_NEAREST_NEIGHBORS_ORGANISM, + TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION, + bogus_ad, + ) + # embedding layer has wrong number of features + bogus_ad = query_anndata.copy() + bogus_ad.obsm[TRUE_NEAREST_NEIGHBORS_EMBEDDING_NAME] = np.zeros((len(bogus_ad), 42)) + with pytest.raises(ValueError, match="features, expected"): + find_nearest_obs( + TRUE_NEAREST_NEIGHBORS_EMBEDDING_NAME, + TRUE_NEAREST_NEIGHBORS_ORGANISM, + TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION, + bogus_ad, + ) + return + + +@pytest.mark.experimental +@pytest.mark.live_corpus +def test_predict_obs_metadata(query_anndata: ad.AnnData, query_result: NeighborObs) -> None: + columns = ["cell_type", "tissue_general"] + + with cellxgene_census.open_soma(census_version=TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION) as census: + truth_df = ( + census["census_data"][TRUE_NEAREST_NEIGHBORS_ORGANISM] + .obs.read(coords=(query_anndata.obs["soma_joinid"].values,), column_names=columns) + .concat() + .to_pandas() + ) + + pred_df = predict_obs_metadata( + TRUE_NEAREST_NEIGHBORS_ORGANISM, TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION, query_result, columns + ) + assert len(pred_df) == len(query_anndata.obs) + + for col in columns: + assert col in pred_df.columns + assert f"{col}_confidence" in pred_df.columns + accuracy = (pred_df[col] == truth_df[col]).mean() + assert accuracy > 0.75, f"Accuracy for {col} is {accuracy}" + + +@pytest.fixture(scope="module") +def true_neighbors() -> Dict[int, List[Dict[str, Any]]]: + ans = {} + for line in TRUE_NEAREST_NEIGHBORS_JSON.strip().split("\n"): + example = json.loads(line) + ans[example["cell_id"]] = example["neighbors"][:TRUE_NEAREST_NEIGHBORS_K] + return ans + + +@pytest.fixture(scope="module") +def query_anndata(true_neighbors: Dict[str, Any]) -> ad.AnnData: + with cellxgene_census.open_soma(census_version=TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION) as census: + return cellxgene_census.get_anndata( + census, + TRUE_NEAREST_NEIGHBORS_ORGANISM, + obs_coords=sorted(true_neighbors.keys()), + obs_embeddings=[TRUE_NEAREST_NEIGHBORS_EMBEDDING_NAME], + ) + + +@pytest.fixture(scope="module") +def query_result(query_anndata: ad.AnnData) -> NeighborObs: + return find_nearest_obs( + TRUE_NEAREST_NEIGHBORS_EMBEDDING_NAME, + TRUE_NEAREST_NEIGHBORS_ORGANISM, + TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION, + query_anndata, + k=TRUE_NEAREST_NEIGHBORS_K, + nprobe=25, + ) + + +""" +The following "truth" data were generated by randomly sampling 100 Census primary cells and their +scVI embeddings (CxG-czi-5). Then, we made an exhaustive pass through the embeddings array to find +their 25 nearest neighbors by Euclidean distance. +""" +TRUE_NEAREST_NEIGHBORS_CENSUS_VERSION = "2023-12-15" +TRUE_NEAREST_NEIGHBORS_EMBEDDING_NAME = "scvi" +TRUE_NEAREST_NEIGHBORS_ORGANISM = "homo_sapiens" +TRUE_NEAREST_NEIGHBORS_K = 10 +TRUE_NEAREST_NEIGHBORS_JSON = """ +{"cell_id": 48884266, "neighbors": [{"distance": 0.0, "cell_id": 48884266}, {"distance": 0.36870312719526344, "cell_id": 48882582}, {"distance": 0.45194134685206555, "cell_id": 48904243}, {"distance": 0.4689120465432001, "cell_id": 48883973}, {"distance": 0.4701634704715092, "cell_id": 48883894}, {"distance": 0.4772844749267185, "cell_id": 48881548}, {"distance": 0.47747182817763884, "cell_id": 48883215}, {"distance": 0.5171140271570219, "cell_id": 48882290}, {"distance": 0.5343526701049458, "cell_id": 48905593}, {"distance": 0.5393365808038064, "cell_id": 48896713}, {"distance": 0.5409478030410546, "cell_id": 48906104}, {"distance": 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