mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-15 20:57:56 +08:00
b90447c387
* first flatbuffer schema * do not lint auto-generated files * add flatbuffers package * add flatbuffer module * wire up /data/X/T route * use flatbuffers for matrix data fetc * clarity and comments * add flatbuffer layout route * clean up obsolete code * fix tests * move flake8 config to setup.cfg * add comments * lint * rework layout routes for fbs * add more type support to fbs * lint * add flatbuffer support for annotations * function name improvements * fix botched merge with master * remove unused import * route cleanup for flatbuffers * rename function for clarity * add missing globals to Jest tests * fix client JS tests * fix routes for Python tests * comments for clarity * non-finite floating point hardening * more non-finite number handling * lint * fix tests for summarizeAnnotations * harden diffexp calculation against FP errors * cleanup unused code * lint * add encoding tests for flatbuffers * application type specified as strings * fix spelling error * improve variable names * add note about documentation gap * rename FBS DataFrame to Matrix
570 lines
23 KiB
Python
570 lines
23 KiB
Python
import warnings
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import numpy as np
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from pandas import DataFrame
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from pandas.core.dtypes.dtypes import CategoricalDtype
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import scanpy.api as sc
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from scipy import sparse
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from server.app.driver.driver import CXGDriver
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from server.app.util.constants import Axis, DEFAULT_TOP_N
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from server.app.util.errors import (
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FilterError,
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InteractiveError,
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JSONEncodingValueError,
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PrepareError,
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ScanpyFileError,
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)
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from server.app.util.utils import jsonify_scanpy
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from server.app.scanpy_engine.diffexp import diffexp_ttest
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from server.app.util.fbs.matrix import encode_matrix_fbs
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"""
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Sort order for methods
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1. Initialize
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2. Helper
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3. Filter
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4. Data & Metadata
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5. Computation
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"""
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class ScanpyEngine(CXGDriver):
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def __init__(self, data, args):
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super().__init__(data, args)
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self._alias_annotation_names(Axis.OBS, args["obs_names"])
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self._alias_annotation_names(Axis.VAR, args["var_names"])
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self._validate_data_types()
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self._validate_data_calculations()
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self.cell_count = self.data.shape[0]
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self.gene_count = self.data.shape[1]
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self.layout_options = ["umap", "tsne"]
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self.diffexp_options = ["ttest"]
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self._create_schema()
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# TODO: temporary work-arounds
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if args["nan_to_num"]:
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self._IEEE754_special_values_workaround()
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def _alias_annotation_names(self, axis, name):
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"""
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Do all user-specified annotation aliasing.
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As a *critical* side-effect, ensure the indices are simple number ranges
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(accomplished by calling pandas.DataFrame.reset_index())
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"""
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if name == "name":
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# a noop, so skip it
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return
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ax_name = str(axis)
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df_axis = getattr(self.data, ax_name)
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if name is None:
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# reset index to simple range; alias "name" to point at the
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# previously specified index.
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df_axis.reset_index(inplace=True)
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df_axis.rename(inplace=True, columns={"index": "name"})
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elif name in df_axis.columns:
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if name not in df_axis.columns:
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raise KeyError(
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f"Annotation name {name}, specified in --{ax_name}-name does not exist."
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)
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if not df_axis[name].is_unique:
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raise KeyError(
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f"Values in -{ax_name}-name must be unique. "
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"Please prepare data to contain unique values."
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)
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# reset index to simple range; alias user-specified annotation to "name"
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df_axis.reset_index(drop=True, inplace=True)
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df_axis.rename(inplace=True, columns={name: "name"})
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else:
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raise KeyError(
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f"Annotation name {name}, specified in --{ax_name}_name does not exist."
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)
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@staticmethod
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def _can_cast_to_float32(ann):
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if ann.dtype.kind == "f":
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if not np.can_cast(ann.dtype, np.float32):
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warnings.warn(
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f"Annotation {ann.name} will be converted to 32 bit float and may lose precision."
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)
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return True
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return False
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@staticmethod
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def _can_cast_to_int32(ann):
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if ann.dtype.kind in ["i", "u"]:
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if np.can_cast(ann.dtype, np.int32):
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return True
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ii32 = np.iinfo(np.int32)
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if ann.min() >= ii32.min and ann.max() <= ii32.max:
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return True
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return False
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def _create_schema(self):
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self.schema = {
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"dataframe": {
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"nObs": self.cell_count,
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"nVar": self.gene_count,
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"type": str(self.data.X.dtype),
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},
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"annotations": {"obs": [], "var": []},
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}
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for ax in Axis:
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curr_axis = getattr(self.data, str(ax))
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for ann in curr_axis:
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ann_schema = {"name": ann}
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dtype = curr_axis[ann].dtype
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data_kind = dtype.kind
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if self._can_cast_to_float32(curr_axis[ann]):
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ann_schema["type"] = "float32"
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elif self._can_cast_to_int32(curr_axis[ann]):
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ann_schema["type"] = "int32"
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elif dtype == np.bool_:
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ann_schema["type"] = "boolean"
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elif data_kind == "O" and dtype == "object":
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ann_schema["type"] = "string"
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elif data_kind == "O" and dtype == "category":
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ann_schema["type"] = "categorical"
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ann_schema["categories"] = curr_axis[ann].dtype.categories.tolist()
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else:
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raise TypeError(
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f"Annotations of type {curr_axis[ann].dtype} are unsupported by cellxgene."
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)
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self.schema["annotations"][ax].append(ann_schema)
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@staticmethod
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def _load_data(data):
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# Based on benchmarking, cache=True has no impact on perf.
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# Note: as of current scanpy/anndata release, setting backed='r' will
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# result in an error. https://github.com/theislab/anndata/issues/79
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try:
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result = sc.read(data, cache=True)
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except ValueError:
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raise ScanpyFileError(
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"File must be in the .h5ad format. Please read "
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"https://github.com/theislab/scanpy_usage/blob/master/170505_seurat/info_h5ad.md to "
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"learn more about this format. You may be able to convert your file into this format "
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"using `cellxgene prepare`, please run `cellxgene prepare --help` for more "
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"information."
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)
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except Exception as e:
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raise ScanpyFileError(
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f"Error while loading file: {e}, File must be in the .h5ad format, please check "
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f"that your input and try again."
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)
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return result
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def _validate_data_types(self):
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if self.data.X.dtype != "float32":
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warnings.warn(
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f"Scanpy data matrix is in {self.data.X.dtype} format not float32. "
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f"Precision may be truncated."
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)
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for ax in Axis:
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curr_axis = getattr(self.data, str(ax))
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for ann in curr_axis:
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datatype = curr_axis[ann].dtype
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downcast_map = {
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"int64": "int32",
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"uint32": "int32",
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"uint64": "int32",
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"float64": "float32",
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}
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if datatype in downcast_map:
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warnings.warn(
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f"Scanpy annotation {ax}:{ann} is in unsupported format: {datatype}. "
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f"Data will be downcast to {downcast_map[datatype]}."
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)
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if isinstance(datatype, CategoricalDtype):
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category_num = len(curr_axis[ann].dtype.categories)
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if category_num > 500 and category_num > self.max_category_items:
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warnings.warn(
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f"{str(ax).title()} annotation '{ann}' has {category_num} categories, this may be "
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f"cumbersome or slow to display. We recommend setting the "
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f"--max-category-items option to 500, this will hide categorical "
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f"annotations with more than 500 categories in the UI"
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)
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def _validate_data_calculations(self):
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layout_key = f"X_{self.layout_method}"
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try:
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assert layout_key in self.data.obsm_keys()
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except AssertionError:
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raise PrepareError(
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f"Cannot find a field with coordinates for the {self.layout_method} layout requested. A different"
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f" layout may have been computed. The requested layout must be pre-calculated and saved "
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f"back in the h5ad file. You can run "
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f"`cellxgene prepare --layout {self.layout_method} <datafile>` "
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f"to solve this problem. "
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)
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def _IEEE754_special_values_workaround(self):
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"""
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TODO: temporary workaround
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Because all floating point data is serialized to JSON, and JSON has no means of representing
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non-finite, floating point special values (NaN, +/-Infinity, etc), we include this temporary
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work-around.
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This will likely be removed in the future, contingent upon improved marshalling.
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Where non-finite floating point is present in obs, var or X:
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* issue a warning to the user that these values will be convert to finite numbers.
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* set NaN to zero, and Infinities to min/max of the element.
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"""
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# annotations
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for ax in Axis:
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curr_axis = getattr(self.data, str(ax))
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for ann in curr_axis:
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dtype = curr_axis[ann].dtype
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if dtype.kind == "f":
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finite_idx = np.isfinite(curr_axis[ann])
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if not finite_idx.all():
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curr_axis.loc[np.isnan(curr_axis[ann]), ann] = 0
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curr_axis.loc[np.isneginf(curr_axis[ann]), ann] = curr_axis[
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ann
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][finite_idx].min()
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curr_axis.loc[np.isposinf(curr_axis[ann]), ann] = curr_axis[
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ann
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][finite_idx].max()
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warnings.warn(
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f"{str(ax).title()} annotation '{ann}' contains floating point NaN or Infinities. "
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f"These will be converted to finite values."
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)
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# X
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non_finite_X_found = False
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if sparse.issparse(self.data._X):
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coo = self.data._X.tocoo()
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finite_idx = np.isfinite(coo.data)
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if not finite_idx.all():
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non_finite_X_found = True
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coo.data[np.isnan(coo.data)] = 0
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coo.data[np.isneginf(coo.data)] = np.min(coo.data[finite_idx])
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coo.data[np.isposinf(coo.data)] = np.max(coo.data[finite_idx])
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coo.eliminate_zeros()
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_X = coo.asformat(self.data._X.getformat())
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self.data._X = _X
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else:
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_X = self.data._X
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finite_idx = np.isfinite(_X.flat)
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if not finite_idx.all():
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non_finite_X_found = True
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min_X = _X.flat[finite_idx].min()
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max_X = _X.flat[finite_idx].max()
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_X[np.isnan(_X)] = 0
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_X[np.isneginf(_X)] = min_X
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_X[np.isposinf(_X)] = max_X
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if non_finite_X_found:
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warnings.warn(
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"Dataframe X contains floating point NaN or Infinities. "
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"These will be converted to finite values."
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)
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def filter_dataframe(self, filter):
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"""
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Filter cells from data and return a subset of the data. They can operate on both obs and var dimension with
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indexing and filtering by annotation value. Filters are combined with the and operator.
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See REST specs for info on filter format:
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# TODO update this link to swagger when it's done
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https://docs.google.com/document/d/1Fxjp1SKtCk7l8QP9-7KAjGXL0eldi_qEnNT0NmlGzXI/edit#heading=h.8qc9q57amldx
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:param filter: dictionary with filter params
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:return: View into scanpy object with cells/genes filtered
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"""
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if not filter:
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return self.data
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obs_selector, var_selector = self._filter_to_mask(filter, use_slices=False)
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data = self._slice(self.data, obs_selector, var_selector)
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return data
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@staticmethod
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def _annotation_filter_to_mask(filter, d_axis, count):
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mask = np.ones((count,), dtype=bool)
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for v in filter:
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if d_axis[v["name"]].dtype.name in ["boolean", "category", "object"]:
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key_idx = np.in1d(getattr(d_axis, v["name"]), v["values"])
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mask = np.logical_and(mask, key_idx)
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else:
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min_ = v.get("min", None)
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max_ = v.get("max", None)
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if min_ is not None:
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key_idx = (getattr(d_axis, v["name"]) >= min_).ravel()
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mask = np.logical_and(mask, key_idx)
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if max_ is not None:
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key_idx = (getattr(d_axis, v["name"]) <= max_).ravel()
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mask = np.logical_and(mask, key_idx)
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return mask
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@staticmethod
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def _index_filter_to_mask(filter, count):
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mask = np.zeros((count,), dtype=bool)
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for i in filter:
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if type(i) == list:
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mask[i[0] : i[1]] = True
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else:
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mask[i] = True
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return mask
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@staticmethod
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def _axis_filter_to_mask(filter, d_axis, count):
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mask = np.ones((count,), dtype=bool)
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if "index" in filter:
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mask = np.logical_and(
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mask, ScanpyEngine._index_filter_to_mask(filter["index"], count)
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)
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if "annotation_value" in filter:
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mask = np.logical_and(
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mask,
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ScanpyEngine._annotation_filter_to_mask(
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filter["annotation_value"], d_axis, count
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),
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)
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return mask
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def _filter_to_mask(self, filter, use_slices=True):
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if use_slices:
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obs_selector = slice(0, self.data.n_obs)
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var_selector = slice(0, self.data.n_vars)
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else:
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obs_selector = None
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var_selector = None
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if filter is not None:
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if Axis.OBS in filter:
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obs_selector = self._axis_filter_to_mask(
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filter["obs"], self.data.obs, self.data.n_obs
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)
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if Axis.VAR in filter:
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var_selector = self._axis_filter_to_mask(
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filter["var"], self.data.var, self.data.n_vars
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)
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return obs_selector, var_selector
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@staticmethod
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def _slice(data, obs_selector=None, vars_selector=None):
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"""
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Slice date using any selector that the AnnData object
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supprots for slicing. If selector is None, will not slice
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on that axis.
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This method exists to optimize filtering/slicing sparse data that has
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access patterns which impact slicing performance.
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https://docs.scipy.org/doc/scipy/reference/sparse.html
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"""
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prefer_row_access = (
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sparse.isspmatrix_csr(data._X)
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or sparse.isspmatrix_lil(data._X)
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or sparse.isspmatrix_bsr(data._X)
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)
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if prefer_row_access:
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# Row-major slicing
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if obs_selector is not None:
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data = data[obs_selector, :]
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if vars_selector is not None:
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data = data[:, vars_selector]
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else:
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# Col-major slicing
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if vars_selector is not None:
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data = data[:, vars_selector]
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if obs_selector is not None:
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data = data[obs_selector, :]
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return data
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def annotation(self, filter, axis, fields=None):
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"""
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Gets annotation value for each observation
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:param filter: filter: dictionary with filter params
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:param axis: string obs or var
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:param fields: list of keys for annotation to return, returns all annotation values if not set.
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:return: dict: names - list of fields in order, data - list of lists or metadata
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[observation ids, val1, val2...]
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"""
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try:
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obs_selector, var_selector = self._filter_to_mask(filter)
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except (KeyError, IndexError) as e:
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raise FilterError(f"Error parsing filter: {e}") from e
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if axis == Axis.OBS:
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obs = self.data.obs[obs_selector]
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if not fields:
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fields = obs.columns.tolist()
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result = {
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"names": fields,
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"data": DataFrame(obs[fields]).to_records(index=True).tolist(),
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}
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else:
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var = self.data.var[var_selector]
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if not fields:
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fields = var.columns.tolist()
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result = {
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"names": fields,
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"data": DataFrame(var[fields]).to_records(index=True).tolist(),
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}
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try:
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return jsonify_scanpy(result)
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except ValueError:
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raise JSONEncodingValueError("Error encoding annotations to JSON")
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def annotation_to_fbs_matrix(self, axis, fields=None):
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if axis == Axis.OBS:
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df = self.data.obs
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else:
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df = self.data.var
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if fields is not None and len(fields) > 0:
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df = df[fields]
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return encode_matrix_fbs(df, col_idx=df.columns)
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def data_frame(self, filter, axis):
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"""
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Retrieves data for each variable for observations in data frame
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:param filter: filter: dictionary with filter params
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:param axis: string obs or var
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:return: {
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"var": list of variable ids,
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"obs": [cellid, var1 expression, var2 expression, ...],
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}
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"""
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try:
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obs_selector, var_selector = self._filter_to_mask(filter)
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except (KeyError, IndexError) as e:
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raise FilterError(f"Error parsing filter: {e}") from e
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_X = self.data._X[obs_selector, var_selector]
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if sparse.issparse(_X):
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_X = _X.toarray()
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var_index_sliced = self.data.var.index[var_selector]
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obs_index_sliced = self.data.obs.index[obs_selector]
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if axis == Axis.OBS:
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result = {
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"var": var_index_sliced.tolist(),
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"obs": DataFrame(_X, index=obs_index_sliced)
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.to_records(index=True)
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.tolist(),
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}
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else:
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result = {
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"obs": obs_index_sliced.tolist(),
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"var": DataFrame(_X.T, index=var_index_sliced)
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.to_records(index=True)
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.tolist(),
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}
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try:
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return jsonify_scanpy(result)
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except ValueError:
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raise JSONEncodingValueError("Error encoding dataframe to JSON")
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def data_frame_to_fbs_matrix(self, filter, axis):
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"""
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Retrieves data 'X' and returns in a flatbuffer Matrix.
|
||
:param filter: filter: dictionary with filter params
|
||
:param axis: string obs or var
|
||
:return: flatbuffer Matrix
|
||
|
||
Caveats:
|
||
* currently only supports access on VAR axis
|
||
* currently only supports filtering on VAR axis
|
||
"""
|
||
if axis != Axis.VAR:
|
||
raise ValueError("Only VAR dimension access is supported")
|
||
try:
|
||
obs_selector, var_selector = self._filter_to_mask(filter, use_slices=False)
|
||
except (KeyError, IndexError) as e:
|
||
raise FilterError(f"Error parsing filter: {e}") from e
|
||
if obs_selector is not None:
|
||
raise FilterError("filtering on obs unsupported")
|
||
|
||
# Currently only handles VAR dimension
|
||
X = self.data._X[:, var_selector]
|
||
return encode_matrix_fbs(X, col_idx=np.nonzero(var_selector)[0], row_idx=None)
|
||
|
||
def diffexp_topN(self, obsFilterA, obsFilterB, top_n=None, interactive_limit=None):
|
||
if Axis.VAR in obsFilterA or Axis.VAR in obsFilterB:
|
||
raise FilterError("Observation filters may not contain vaiable conditions")
|
||
try:
|
||
obs_mask_A = self._axis_filter_to_mask(
|
||
obsFilterA["obs"], self.data.obs, self.data.n_obs
|
||
)
|
||
obs_mask_B = self._axis_filter_to_mask(
|
||
obsFilterB["obs"], self.data.obs, self.data.n_obs
|
||
)
|
||
except (KeyError, IndexError) as e:
|
||
raise FilterError(f"Error parsing filter: {e}") from e
|
||
if top_n is None:
|
||
top_n = DEFAULT_TOP_N
|
||
result = diffexp_ttest(
|
||
self.data, obs_mask_A, obs_mask_B, top_n, self.diffexp_lfc_cutoff
|
||
)
|
||
try:
|
||
return jsonify_scanpy(result)
|
||
except ValueError:
|
||
raise JSONEncodingValueError(
|
||
"Error encoding differential expression to JSON"
|
||
)
|
||
|
||
def layout(self, filter, interactive_limit=None):
|
||
"""
|
||
Computes a n-d layout for cells through dimensionality reduction.
|
||
:param filter: filter: dictionary with filter params
|
||
:param interactive_limit: -- don't compute if total # genes in dataframes are larger than this
|
||
:return: [cellid, x, y, ...]
|
||
"""
|
||
try:
|
||
df = self.filter_dataframe(filter)
|
||
except (KeyError, IndexError) as e:
|
||
raise FilterError(f"Error parsing filter: {e}") from e
|
||
if interactive_limit and len(df.obs.index) > interactive_limit:
|
||
raise InteractiveError("Size data is too large for interactive computation")
|
||
# TODO Filtering cells is fine, but filtering genes does nothing because the neighbors are
|
||
# calculated using the original vars (geneset) and this doesn’t get updated when you use less.
|
||
# Need to recalculate neighbors (long) if user requests new layout filtered by var
|
||
# TODO for MVP we are pushing computation of layout to preprocessing and not allowing re-layout
|
||
# this will probably change after user feedback
|
||
# getattr(sc.tl, self.layout_method)(df, random_state=123)
|
||
try:
|
||
df_layout = df.obsm[f"X_{self.layout_method}"]
|
||
except ValueError as e:
|
||
raise PrepareError(
|
||
f"Layout has not been calculated using {self.layout_method}, "
|
||
f"please prepare your datafile and relaunch cellxgene"
|
||
) from e
|
||
normalized_layout = DataFrame(
|
||
(df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()),
|
||
index=df.obs.index,
|
||
)
|
||
try:
|
||
return jsonify_scanpy(
|
||
{
|
||
"layout": {
|
||
"ndims": normalized_layout.shape[1],
|
||
"coordinates": normalized_layout.to_records(
|
||
index=True
|
||
).tolist(),
|
||
}
|
||
}
|
||
)
|
||
except ValueError:
|
||
raise JSONEncodingValueError("Error encoding layout to JSON")
|
||
|
||
def layout_to_fbs_matrix(self):
|
||
"""
|
||
Return the default 2-D layout for cells as a FBS Matrix.
|
||
|
||
Caveats:
|
||
* does not support filtering
|
||
* only returns Matrix in columnar layout
|
||
"""
|
||
try:
|
||
df_layout = self.data.obsm[f"X_{self.layout_method}"]
|
||
except ValueError as e:
|
||
raise PrepareError(
|
||
f"Layout has not been calculated using {self.layout_method}, "
|
||
f"please prepare your datafile and relaunch cellxgene") from e
|
||
normalized_layout = (df_layout - df_layout.min()) / (df_layout.max() - df_layout.min())
|
||
return encode_matrix_fbs(normalized_layout.astype(dtype=np.float32), col_idx=None, row_idx=None)
|