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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,111 +1,226 @@ | ||
| import os | ||
| from typing import Union | ||
| from typing import List, Union, Sequence | ||
|
|
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| import pandas as pd | ||
| import tiledb | ||
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| from . import queryutils_tiledb_frame as qtd | ||
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| __author__ = "Jayaram Kancherla" | ||
| __copyright__ = "Jayaram Kancherla" | ||
| __license__ = "MIT" | ||
|
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| class CellArrDataset: | ||
| """A class that represent a collection of cells in TileDB.""" | ||
| """A class that represent a collection of cells and their associated metadata in a TileDB backed store.""" | ||
|
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||
| def __init__( | ||
| self, | ||
| dataset_path: str, | ||
| counts_tdb_uri: str = "counts", | ||
| gene_metadata_uri: str = "gene_metadata", | ||
| matrix_tdb_uri: str = "counts", | ||
| gene_annotation_uri: str = "gene_annotation", | ||
| cell_metadata_uri: str = "cell_metadata", | ||
| sample_metadata_uri: str = "sample_metadata", | ||
| ): | ||
| """Initialize a ``CellArr`` dataset. | ||
| """Initialize a ``CellArrDataset``. | ||
| Args: | ||
| dataset_path: | ||
| Path to the directory containing the tiledb stores. | ||
| Usually the ``output_path`` from the | ||
| :py:func:`~cellarr.build_cellarrdataset.build_cellarrdataset`. | ||
| counts_tdb_uri: | ||
| Path to counts TileDB. | ||
| Relative path to matrix store. | ||
| gene_metadata_uri: | ||
| Path to gene metadata TileDB. | ||
| gene_annotation_uri: | ||
| Relative path to gene annotation store. | ||
| cell_metadata_uri: | ||
| Path to cell metadata TileDB. | ||
| Relative path to cell metadata store. | ||
| sample_metadata_uri: | ||
| Relative path to sample metadata store. | ||
| """ | ||
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| if not os.path.isdir(dataset_path): | ||
| raise ValueError("'dataset_path' is not a directory.") | ||
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| self._dataset_path = dataset_path | ||
| # TODO: Maybe switch to on-demand loading of these objects | ||
| self._counts_tdb_tdb = tiledb.open(f"{dataset_path}/{counts_tdb_uri}", "r") | ||
| self._gene_metadata_tdb = tiledb.open( | ||
| f"{dataset_path}/{gene_metadata_uri}", "r" | ||
| self._matrix_tdb_tdb = tiledb.open(f"{dataset_path}/{matrix_tdb_uri}", "r") | ||
| self._gene_annotation_tdb = tiledb.open( | ||
| f"{dataset_path}/{gene_annotation_uri}", "r" | ||
| ) | ||
| self._cell_metadata_tdb = tiledb.open( | ||
| f"{dataset_path}/{cell_metadata_uri}", "r" | ||
| ) | ||
|
|
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| def __del__(self): | ||
| self._counts_tdb_tdb.close() | ||
| self._gene_metadata_tdb.close() | ||
| self._matrix_tdb_tdb.close() | ||
| self._gene_annotation_tdb.close() | ||
| self._cell_metadata_tdb.close() | ||
|
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||
| # TODO: | ||
| # Methods to implement | ||
| # search by gene | ||
| # search by cell metadata | ||
| # slice counts after search | ||
| def get_cell_metadata_columns(self) -> List[str]: | ||
| """Get column names from ``cell_metadata`` store. | ||
| def get_cell_metadata_columns(self): | ||
| columns = [] | ||
| for i in range(self._cell_metadata_tdb.schema.nattr): | ||
| columns.append(self._cell_metadata_tdb.schema.attr(i).name) | ||
| Returns: | ||
| List of available metadata columns. | ||
| """ | ||
| return qtd.get_schema_names_frame(self._cell_metadata_tdb) | ||
|
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| return columns | ||
| def get_cell_metadata_column(self, column_name: str) -> list: | ||
| """Access a column from the ``cell_metadata`` store. | ||
| def get_cell_metadata_column(self, column_name: str): | ||
| return self._cell_metadata_tdb.query(attrs=[column_name]).df[:] | ||
| Args: | ||
| column_name: | ||
| Name of the column or attribute. Usually one of the column names | ||
| from of :py:meth:`~get_cell_metadata_columns`. | ||
| Returns: | ||
| A list of values for this column. | ||
| """ | ||
| return qtd.get_a_column(self._cell_metadata_tdb, column_name=column_name) | ||
|
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| def get_cell_subset( | ||
| self, subset: Union[slice, tiledb.QueryCondition], columns=None | ||
| ): | ||
| if columns is None: | ||
| columns = self.get_cell_metadata_columns() | ||
| ) -> pd.DataFrame: | ||
| """Slice the ``cell_metadata`` store. | ||
| Args: | ||
| subset: | ||
| A list of integer indices to subset the ``cell_metadata`` | ||
| store. | ||
| Alternatively, may also provide a | ||
| :py:class:`tiledb.QueryCondition` to query the store. | ||
| query = self._cell_metadata_tdb.query(cond=subset, attrs=columns) | ||
| data = query.df[:] | ||
| result = data.dropna() | ||
| return result | ||
| columns: | ||
| List of specific column names to access. | ||
| def get_gene_metadata_columns(self): | ||
| columns = [] | ||
| for i in range(self._gene_metadata_tdb.schema.nattr): | ||
| columns.append(self._gene_metadata_tdb.schema.attr(i).name) | ||
| Defaults to None, in which case all columns are extracted. | ||
| return columns | ||
| Returns: | ||
| A pandas Dataframe of the subset. | ||
| """ | ||
| return qtd.subset_frame(self._cell_metadata_tdb, subset=subset, columns=columns) | ||
|
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| def get_gene_metadata_columns(self) -> List[str]: | ||
| """Get annotation column names from ``gene_metadata`` store. | ||
| Returns: | ||
| List of available annotations. | ||
| """ | ||
| return qtd.get_schema_names_frame(self._gene_annotation_tdb) | ||
|
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| def get_gene_metadata_column(self, column_name: str): | ||
| return self._gene_metadata_tdb.query(attrs=[column_name]).df[:] | ||
| """Access a column from the ``gene_metadata`` store. | ||
| Args: | ||
| column_name: | ||
| Name of the column or attribute. Usually one of the column names | ||
| from of :py:meth:`~get_gene_metadata_columns`. | ||
| Returns: | ||
| A list of values for this column. | ||
| """ | ||
| return qtd.get_a_column(self._gene_annotation_tdb, column_name=column_name) | ||
|
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| def get_gene_metadata_index(self): | ||
| """Get index of the ``gene_metadata`` store. This typically should store all unique gene symbols. | ||
| Returns: | ||
| List of unique symbols. | ||
| """ | ||
| return qtd.get_index(self._gene_annotation_tdb) | ||
|
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| def _get_indices_for_gene_list(self, query: list) -> List[int]: | ||
| _gene_index = self.get_gene_metadata_index() | ||
| return qtd._match_to_list(_gene_index, query=query) | ||
|
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| def get_gene_subset( | ||
| self, subset: Union[slice, tiledb.QueryCondition], columns=None | ||
| self, subset: Union[slice, List[str], tiledb.QueryCondition], columns=None | ||
| ): | ||
| if columns is None: | ||
| columns = self.get_gene_metadata_columns() | ||
| """Slice the ``gene_metadata`` store. | ||
| Args: | ||
| subset: | ||
| A list of integer indices to subset the ``gene_metadata`` | ||
| store. | ||
| Alternatively, may provide a | ||
| :py:class:`tiledb.QueryCondition` to query the store. | ||
| Alternatively, may provide a list of strings to match with | ||
| the index of ``gene_metadata`` store. | ||
| columns: | ||
| List of specific column names to access. | ||
| Defaults to None, in which case all columns are extracted. | ||
| Returns: | ||
| A pandas Dataframe of the subset. | ||
| """ | ||
|
|
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| if qtd._is_list_strings(subset): | ||
| subset = self._get_indices_for_gene_list(subset) | ||
|
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| query = self._gene_metadata_tdb.query(cond=subset, attrs=columns) | ||
| data = query.df[:] | ||
| result = data.dropna() | ||
| return result | ||
| return qtd.subset_frame( | ||
| self._gene_annotation_tdb, subset=subset, columns=columns | ||
| ) | ||
|
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||
| def get_slice( | ||
| self, | ||
| cell_subset: Union[slice, tiledb.QueryCondition], | ||
| gene_subset: Union[slice, tiledb.QueryCondition], | ||
| gene_subset: Union[slice, List[str], tiledb.QueryCondition], | ||
| ): | ||
| _csubset = self.get_cell_subset(cell_subset) | ||
| _cell_indices = _csubset.index.tolist() | ||
|
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| _gsubset = self.get_gene_subset(gene_subset) | ||
| _gene_indices = _gsubset.index.tolist() | ||
|
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| return self._counts_tdb_tdb.multi_index[_cell_indices, _gene_indices] | ||
| return self._matrix_tdb_tdb.multi_index[_cell_indices, _gene_indices] | ||
|
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| def __getitem__( | ||
| self, | ||
| args: Union[int, str, Sequence, tuple], | ||
| ): | ||
| """Subset a ``CellArrDataset``. | ||
| Args: | ||
| args: | ||
| Integer indices, a boolean filter, or (if the current object is | ||
| named) names specifying the ranges to be extracted. | ||
| Alternatively a tuple of length 1. The first entry specifies | ||
| the rows to retain based on their names or indices. | ||
| Alternatively a tuple of length 2. The first entry specifies | ||
| the rows to retain, while the second entry specifies the | ||
| columns to retain, based on their names or indices. | ||
| Raises: | ||
| ValueError: | ||
| If too many or too few slices provided. | ||
| """ | ||
| if isinstance(args, (str, int)): | ||
| return self.get_slice(args, slice(None)) | ||
|
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| if isinstance(args, tuple): | ||
| if len(args) == 0: | ||
| raise ValueError("At least one slicing argument must be provided.") | ||
|
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| if len(args) == 1: | ||
| return self.get_slice(args[0], slice(None)) | ||
| elif len(args) == 2: | ||
| return self.get_slice(args[0], args[1]) | ||
| else: | ||
| raise ValueError( | ||
| f"`{type(self).__name__}` only supports 2-dimensional slicing." | ||
| ) | ||
|
|
||
| raise TypeError( | ||
| "args must be a sequence or a scalar integer or string or a tuple of atmost 2 values." | ||
| ) |
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