Single cell memmap dataset
FileNames
Bases: str
, Enum
Names of files that are generated in SingleCellCollection.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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METADATA
Bases: str
, Enum
Stored metadata.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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Mode
Bases: str
, Enum
Valid modes for the single cell memory mapped dataset.
The write append mode is 'w+' while the read append mode is 'r+'.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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NeighborSamplingStrategy
Bases: str
, Enum
Valid sampling strategies for neighbor selection.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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SingleCellMemMapDataset
Bases: SingleCellRowDataset
Represents one or more AnnData matrices.
Data is stored in large, memory-mapped arrays that enables fast access of datasets larger than the available amount of RAM on a system. SCMMAP implements a consistent API defined in SingleCellRowDataset.
Attributes:
Name | Type | Description |
---|---|---|
data_path |
str
|
Location of np.memmap files to be loaded from or that will be |
mode |
Mode
|
Whether the dataset will be read in (r+) from np.memmap files or |
data |
Optional[ndarray]
|
A numpy array of the data |
row_index |
Optional[ndarray]
|
A numpy array of row pointers |
col_index |
Optional[ndarray]
|
A numpy array of column values |
metadata |
Dict[str, int]
|
Various metadata about the dataset. |
_feature_index |
RowFeatureIndex
|
The corresponding RowFeatureIndex where features are |
dtypes |
Dict[FileNames, str]
|
A dictionary containing the datatypes of the data, row_index, |
_version |
str
|
The version of the dataset |
load_neighbors |
bool
|
Whether to load and utilize neighbor information from the 'neighbor_key' in AnnData's .obsp. Defaults to False. |
neighbor_key |
str
|
The key in AnnData's .obsp containing the sparse adjacency matrix for neighbors. Defaults to 'next_cell_ids'. |
neighbor_sampling_strategy |
str
|
Strategy for sampling neighbors ('random'). Defaults to 'random'. |
fallback_to_identity |
bool
|
If a cell has no neighbors, whether to use the cell itself as its neighbor. Defaults to True. |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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__getitem__(idx)
Get the row values located and index idx.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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__init__(data_path, h5ad_path=None, num_elements=None, num_rows=None, mode=Mode.READ_APPEND, paginated_load_cutoff=10000, load_block_row_size=1000000, feature_index_name='feature_id', load_neighbors=False, neighbor_key='next_cell_ids', neighbor_sampling_strategy=NeighborSamplingStrategy.RANDOM, fallback_to_identity=True)
Instantiate the class.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
data_path
|
str
|
The location where the data np.memmap files are read from |
required |
h5ad_path
|
Optional[str]
|
Optional, the location of the h5_ad path. |
None
|
num_elements
|
Optional[int]
|
The total number of elements in the array. |
None
|
num_rows
|
Optional[int]
|
The number of rows in the data frame. |
None
|
mode
|
Mode
|
Whether to read or write from the data_path. |
READ_APPEND
|
paginated_load_cutoff
|
int
|
MB size on disk at which to load the h5ad structure with paginated load. |
10000
|
load_block_row_size
|
int
|
Number of rows to load into memory with paginated load |
1000000
|
feature_index_name
|
The name of the features if the features are only stored in features_df.index.values |
'feature_id'
|
|
load_neighbors
|
bool
|
Boolean to control to control whether to load and utilize neighbor information |
False
|
neighbor_key
|
str
|
The key in AnnData's .obsp containing neighbor information. |
'next_cell_ids'
|
neighbor_sampling_strategy
|
str
|
Sampling strategy for neighbors. |
RANDOM
|
fallback_to_identity
|
bool
|
If a cell has no neighbors, whether to use the cell itself as its neighbor. |
True
|
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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__init__obj()
Initializes the data path and writes the version.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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__len__()
Return the number of rows.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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concat(other_dataset, extend_copy_size=10 * 1024 * 1024, output_path=None, destroy_on_copy=False)
Concatenates one or a list of SingleCellMemMapDatasest to the existing one.
The data is stored in the same place as for the original data set or at output_path if it is set. Then, at output_path or at self.data_path, there would be a saved SingleCellMemmpDataset, which can be read in with SingleCellMemmpDataset(output_path).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
other_dataset
|
Union[list[SingleCellMemMapDataset], SingleCellMemMapDataset]
|
A SingleCellMemMapDataset or a list of |
required |
extend_copy_size
|
int
|
how much to copy in memory at once |
10 * 1024 * 1024
|
output_path
|
str | None
|
location to store new dataset |
None
|
destroy_on_copy
|
bool
|
Whether to remove the current data_path |
False
|
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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features()
Return the corresponding RowFeatureIndex.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_neighbor_indices_for_cell(cell_index)
Returns the array of neighbor indices for a given cell.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
cell_index
|
int
|
Index of the cell to get neighbors for |
required |
Returns:
Type | Description |
---|---|
ndarray
|
np.ndarray: Array of neighbor indices, empty if no neighbors or neighbor data unavailable |
Raises:
Type | Description |
---|---|
IndexError
|
If cell_index is out of bounds |
ValueError
|
If neighbor functionality was explicitly enabled but data is unavailable |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_neighbor_stats()
Returns statistics about the neighbors in the dataset.
Returns:
Name | Type | Description |
---|---|---|
dict |
dict
|
Dictionary with neighbor statistics: - has_neighbors: Whether dataset has neighbor data - total_connections: Total number of neighbor relationships - min_neighbors_per_cell: Minimum number of neighbors any cell has - max_neighbors_per_cell: Maximum number of neighbors any cell has - avg_neighbors_per_cell: Average number of neighbors per cell - cells_with_no_neighbors: Count of cells that have no neighbors |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_neighbor_weights_for_cell(cell_index)
Returns the array of neighbor weights (e.g., pseudotime differences) for a given cell.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
cell_index
|
int
|
Index of the cell to get neighbor weights for |
required |
Returns:
Type | Description |
---|---|
ndarray
|
np.ndarray: Array of weights corresponding to neighbors, empty if no neighbors |
Raises:
Type | Description |
---|---|
IndexError
|
If cell_index is out of bounds |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_row(index, return_features=False, feature_vars=None)
Returns a given row in the dataset along with optional features.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
index
|
int
|
The row to be returned. This is in the range of [0, num_rows) |
required |
return_features
|
bool
|
boolean that indicates whether to return features |
False
|
feature_vars
|
Optional[List[str]]
|
Optional, feature variables to extract |
None
|
Return: [Tuple[np.ndarray, np.ndarray]: data values and column pointes List[np.ndarray]: optional, corresponding features.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_row_column(index, column, impute_missing_zeros=True)
Returns the value at a given index and the corresponding column.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
index
|
int
|
The index to be returned |
required |
column
|
int
|
The column to be returned |
required |
impute_missing_zeros
|
bool
|
boolean that indicates whether to set missing |
True
|
Return: A float that is the value in the array or None.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_row_padded(index, return_features=False, feature_vars=None)
Returns a padded version of a row in the dataset.
A padded version is one where the a sparse array representation is converted to a conventional represenentation. Optionally, features are returned.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
index
|
int
|
The row to be returned |
required |
return_features
|
bool
|
boolean that indicates whether to return features |
False
|
feature_vars
|
Optional[List[str]]
|
Optional, feature variables to extract |
None
|
Return: np.ndarray: conventional row representation List[np.ndarray]: optional, corresponding features.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_row_padded_with_neighbor(index, return_features=False, feature_vars=None)
Returns a padded version of a row with optional neighbor data.
A padded version converts sparse representation to a dense array where missing values are filled with zeros.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
index
|
int
|
The row to be returned |
required |
return_features
|
bool
|
Boolean that indicates whether to return features |
False
|
feature_vars
|
Optional[List[str]]
|
Optional, feature variables to extract |
None
|
Returns:
Type | Description |
---|---|
Dict[str, Union[ndarray, int, List[ndarray]]]
|
Dict with keys: |
Dict[str, Union[ndarray, int, List[ndarray]]]
|
|
Dict[str, Union[ndarray, int, List[ndarray]]]
|
|
Dict[str, Union[ndarray, int, List[ndarray]]]
|
|
Dict[str, Union[ndarray, int, List[ndarray]]]
|
|
Dict[str, Union[ndarray, int, List[ndarray]]]
|
|
Raises:
Type | Description |
---|---|
ValueError
|
If neighbor functionality is disabled or no neighbor data is available |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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get_row_with_neighbor(index, return_features=False, feature_vars=None)
Returns a given row in the dataset along with optional features and neighbor data.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
index
|
int
|
The row to be returned. This is in the range of [0, num_rows) |
required |
return_features
|
bool
|
Boolean that indicates whether to return features |
False
|
feature_vars
|
Optional[List[str]]
|
Optional, feature variables to extract |
None
|
Returns:
Type | Description |
---|---|
Dict[str, Union[Tuple[ndarray, ndarray], int, Optional[List[ndarray]]]]
|
Dict with keys: |
Dict[str, Union[Tuple[ndarray, ndarray], int, Optional[List[ndarray]]]]
|
|
Dict[str, Union[Tuple[ndarray, ndarray], int, Optional[List[ndarray]]]]
|
|
Dict[str, Union[Tuple[ndarray, ndarray], int, Optional[List[ndarray]]]]
|
|
Dict[str, Union[Tuple[ndarray, ndarray], int, Optional[List[ndarray]]]]
|
|
Dict[str, Union[Tuple[ndarray, ndarray], int, Optional[List[ndarray]]]]
|
|
Raises:
Type | Description |
---|---|
ValueError
|
If neighbor functionality is disabled or no neighbor data is available |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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load(stored_path)
Loads the data at store_path that is an np.memmap format.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
stored_path
|
str
|
directory with np.memmap files |
required |
Raises: FileNotFoundError if the corresponding directory or files are not found, or if the metadata file is not present.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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load_h5ad(anndata_path)
Loads an existing AnnData archive from disk.
This creates a new backing data structure which is saved. Note: the storage utilized will roughly double. Currently, the data must be in a scipy.sparse.spmatrix format.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
anndata_path
|
str
|
location of data to load |
required |
Raises: FileNotFoundError if the data path does not exist. NotImplementedError if the data is not in scipy.sparse.spmatrix format ValueError it there is not count data
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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number_nonzero_values()
Number of non zero entries in the dataset.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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number_of_rows()
The number of rows in the dataset.
Returns:
Type | Description |
---|---|
int
|
The number of rows in the dataset |
Raises: ValueError if the length of the number of rows in the feature index does not correspond to the number of stored rows.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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number_of_values()
Get the total number of values in the array.
For each index, the length of the corresponding np.ndarray of features is counted.
Returns:
Type | Description |
---|---|
int
|
The sum of lengths of the features in every row |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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number_of_variables()
Get the number of features in every entry in the dataset.
Returns:
Type | Description |
---|---|
List[int]
|
A list containing the lengths of the features in every row |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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paginated_load_h5ad(anndata_path)
Method for block loading a larger h5ad file and converting it to the SCDL format.
This should be used in the case when the entire anndata file cannot be loaded into memory. The anndata is loaded into memory load_block_row_size number of rows at a time. Each chunk is converted into numpy memory maps which are then concatenated together.
Returns:
Name | Type | Description |
---|---|---|
DataFrame
|
pd.DataFrame: var variables for features |
|
int |
int
|
number of rows in the dataframe. |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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regular_load_h5ad(anndata_path)
Method for loading an h5ad file into memorySu and converting it to the SCDL format.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
anndata_path
|
str
|
location of data to load |
required |
Raises: NotImplementedError if the data is not in scipy.sparse.spmatrix format ValueError it there is not count data Returns: pd.DataFrame: var variables for features int: number of rows in the dataframe.
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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sample_neighbor_index(cell_index)
Samples a neighbor index for the given cell based on the configured sampling strategy.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
cell_index
|
int
|
Index of the cell to sample a neighbor for |
required |
Returns:
Name | Type | Description |
---|---|---|
int |
int
|
Index of the sampled neighbor If no neighbors exist and fallback_to_identity is True, returns cell_index |
Raises:
Type | Description |
---|---|
ValueError
|
If an unsupported sampling strategy is specified |
IndexError
|
If cell_index is out of bounds |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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save(output_path=None)
Saves the class to a given output path.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
output_path
|
Optional[str]
|
The location to save - not yet implemented and should |
None
|
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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shape()
Get the shape of the dataset.
This is the number of entries by the the length of the feature index corresponding to that variable.
Returns:
Type | Description |
---|---|
int
|
The number of elements in the dataset |
List[int]
|
A list containing the number of variables for each row. |
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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version()
Returns a version number.
(following
Source code in bionemo/scdl/io/single_cell_memmap_dataset.py
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