mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-15 20:57:56 +08:00
3dc45d6330
* enforce column name uniqueness for obs and var * parameterize the column name containing obs and var user-readable names * use the new annotation index value from schema * update f/e unit tests * PR review suggestions * lint
462 lines
18 KiB
Python
462 lines
18 KiB
Python
import warnings
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import numpy as np
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import pandas
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from pandas.core.dtypes.dtypes import CategoricalDtype
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import anndata
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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, MAX_LAYOUTS
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from server.app.util.errors import (
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FilterError,
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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, requires_data
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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=None, args={}):
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super().__init__(data, args)
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if self.data:
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self._validate_and_initialize()
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def update(self, data=None, args={}):
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super().__init__(data, args)
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if self.data:
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self._validate_and_initialize()
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@staticmethod
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def _get_default_config():
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return {
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"layout": [],
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"diffexp": "ttest",
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"max_category_items": 100,
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"obs_names": None,
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"var_names": None,
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"diffexp_lfc_cutoff": 0.01,
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}
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@staticmethod
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def _create_unique_column_name(df, col_name_prefix):
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""" given the columns of a dataframe, and a name prefix, return a column name which
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does not exist in the dataframe, AND which is prefixed by `prefix`
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The approach is to append a numeric suffix, starting at zero and increasing by
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one, until an unused name is found (eg, prefix_0, prefix_1, ...).
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"""
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suffix = 0
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while f"{col_name_prefix}{suffix}" in df:
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suffix += 1
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return f"{col_name_prefix}{suffix}"
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def _alias_annotation_names(self):
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"""
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The front-end relies on the existance of a unique, human-readable
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index for obs & var (eg, var is typically gene name, obs the cell name).
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The user can specify these via the --obs-names and --var-names config.
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If they are not specified, use the existing index to create them, giving
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the resulting column a unique name (eg, "name").
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In both cases, enforce that the result is unique, and communicate the
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index column name to the front-end via the obs_names and var_names config
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(which is incorporated into the schema).
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"""
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for (ax_name, config_name) in ((Axis.OBS, "obs_names"), (Axis.VAR, "var_names")):
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name = self.config[config_name]
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df_axis = getattr(self.data, str(ax_name))
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if name is None:
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# Default: create unique names from index
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if not df_axis.index.is_unique:
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raise KeyError(
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f"Values in {ax_name}.index must be unique. "
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"Please prepare data to contain unique index values, or specify an "
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"alternative with --{ax_name}-name."
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)
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name = self._create_unique_column_name(df_axis.columns, "name_")
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self.config[config_name] = name
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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.rename_axis(name, inplace=True)
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df_axis.reset_index(inplace=True)
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elif name in df_axis.columns:
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# User has specified alternative column for unique names, and it exists
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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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df_axis.reset_index(drop=True, inplace=True)
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else:
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# user specified a non-existent column name
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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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@requires_data
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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": {
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"obs": {
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"index": self.config["obs_names"],
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"columns": []
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},
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"var": {
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"index": self.config["var_names"],
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"columns": []
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}
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},
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"layout": {"obs": []}
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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]["columns"].append(ann_schema)
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for layout in self.config['layout']:
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layout_schema = {
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"name": layout,
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"type": "float32",
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"dims": [f"{layout}_0", f"{layout}_1"]
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}
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self.schema["layout"]["obs"].append(layout_schema)
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def _load_data(self, data):
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# as of AnnData 0.6.19, backed mode performs initial load fast, but at the
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# cost of significantly slower access to X data.
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try:
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self.data = anndata.read_h5ad(data)
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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 MemoryError:
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raise ScanpyFileError("Error while loading file: out of memory, file is too large"
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" for memory available")
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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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@requires_data
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def _validate_and_initialize(self):
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# var and obs column names must be unique
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if not self.data.obs.columns.is_unique or not self.data.var.columns.is_unique:
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raise KeyError(f"All annotation column names must be unique.")
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self._alias_annotation_names()
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self._validate_data_types()
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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._default_and_validate_layouts()
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self._create_schema()
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@requires_data
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def _default_and_validate_layouts(self):
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""" function:
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a) generate list of default layouts, if not already user specified
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b) validate layouts are legal. remove/warn on any that are not
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c) cap total list of layouts at global const MAX_LAYOUTS
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"""
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layouts = self.config['layout']
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# handle default
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if layouts is None or len(layouts) == 0:
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# load default layouts from the data.
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layouts = [key[2:] for key in self.data.obsm_keys() if type(key) == str and key.startswith("X_")]
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if len(layouts) == 0:
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raise PrepareError(f"Unable to find any precomputed layouts within the dataset.")
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# remove invalid layouts
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valid_layouts = []
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obsm_keys = self.data.obsm_keys()
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for layout in layouts:
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layout_name = f"X_{layout}"
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if layout_name not in obsm_keys:
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warnings.warn(f"Ignoring unknown layout name: {layout}.")
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elif not self._is_valid_layout(self.data.obsm[layout_name]):
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warnings.warn(f"Ignoring layout due to malformed shape or data type: {layout}")
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else:
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valid_layouts.append(layout)
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if len(valid_layouts) == 0:
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raise PrepareError(f"No valid layout data.")
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# cap layouts to MAX_LAYOUTS
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self.config['layout'] = valid_layouts[0:MAX_LAYOUTS]
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@requires_data
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def _is_valid_layout(self, arr):
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""" return True if this layout data is a valid array for front-end presentation:
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* ndarray, with shape (n_obs, >= 2), dtype float/int/uint
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* contains only finite values
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"""
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is_valid = type(arr) == np.ndarray and arr.dtype.kind in "fiu"
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is_valid = is_valid and arr.shape[0] == self.data.n_obs and arr.shape[1] >= 2
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is_valid = is_valid and np.all(np.isfinite(arr))
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return is_valid
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@requires_data
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def _validate_data_types(self):
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if sparse.isspmatrix(self.data.X) and not sparse.isspmatrix_csc(self.data.X):
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warnings.warn(
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f"Scanpy data matrix is sparse, but not a CSC (columnar) matrix. "
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f"Performance may be improved by using CSC."
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)
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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.config['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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@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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@requires_data
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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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@requires_data
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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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@staticmethod
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def slice_columns(X, var_mask):
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"""
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Slice columns from the matrix X, as specified by the mask
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Semantically equivalent to X[:, var_mask], but handles sparse
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matrices in a more performant manner.
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"""
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if var_mask is None: # noop
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return X
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if sparse.issparse(X): # use tuned getcol/hstack for performance
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indices = np.nonzero(var_mask)[0]
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cols = [X.getcol(i) for i in indices]
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return sparse.hstack(cols, format="csc")
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else: # else, just use standard slicing, which is fine for dense arrays
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return X[:, var_mask]
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@requires_data
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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.
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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: flatbuffer Matrix
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Caveats:
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* currently only supports access on VAR axis
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* currently only supports filtering on VAR axis
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"""
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if axis != Axis.VAR:
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raise ValueError("Only VAR dimension access is supported")
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try:
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obs_selector, var_selector = self._filter_to_mask(filter, use_slices=False)
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except (KeyError, IndexError, TypeError) as e:
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raise FilterError(f"Error parsing filter: {e}") from e
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if obs_selector is not None:
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raise FilterError("filtering on obs unsupported")
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# Currently only handles VAR dimension
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X = self.slice_columns(self.data._X, var_selector)
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return encode_matrix_fbs(X, col_idx=np.nonzero(var_selector)[0], row_idx=None)
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@requires_data
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def diffexp_topN(self, obsFilterA, obsFilterB, top_n=None, interactive_limit=None):
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if Axis.VAR in obsFilterA or Axis.VAR in obsFilterB:
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raise FilterError("Observation filters may not contain vaiable conditions")
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try:
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obs_mask_A = self._axis_filter_to_mask(
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obsFilterA["obs"], self.data.obs, self.data.n_obs
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)
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obs_mask_B = self._axis_filter_to_mask(
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obsFilterB["obs"], self.data.obs, self.data.n_obs
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)
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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 top_n is None:
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top_n = DEFAULT_TOP_N
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result = diffexp_ttest(
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self.data, obs_mask_A, obs_mask_B, top_n, self.config['diffexp_lfc_cutoff']
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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(
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"Error encoding differential expression to JSON"
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)
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@requires_data
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def layout_to_fbs_matrix(self):
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"""
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Return the default 2-D layout for cells as a FBS Matrix.
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Caveats:
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* does not support filtering
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* only returns Matrix in columnar layout
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"""
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try:
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layout_data = []
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for layout in self.config["layout"]:
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full_embedding = self.data.obsm[f"X_{layout}"]
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embedding = full_embedding[:, :2]
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normalized_layout = (embedding - embedding.min()) / (embedding.max() - embedding.min())
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normalized_layout = normalized_layout.astype(dtype=np.float32)
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layout_data.append(pandas.DataFrame(normalized_layout, columns=[f"{layout}_0", f"{layout}_1"]))
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except ValueError as e:
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raise PrepareError(
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f"Layout has not been calculated using {self.config['layout']}, "
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f"please prepare your datafile and relaunch cellxgene") from e
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df = pandas.concat(layout_data, axis=1, copy=False)
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return encode_matrix_fbs(df, col_idx=df.columns, row_idx=None)
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