From 722ed5d4cd8c8a9497a4a0b11028ac767d833a73 Mon Sep 17 00:00:00 2001 From: jkanche Date: Sat, 15 Jun 2024 06:24:22 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20BiocPy/c?= =?UTF-8?q?ellarr@0e145e7efdd9ed0357ecb81bf69a97ba4f6f9020=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .buildinfo | 2 +- _modules/cellarr/CellArrDataset.html | 437 +++ _modules/cellarr/build_cellarrdataset.html | 719 +++++ _modules/cellarr/utils_anndata.html | 476 ++++ _modules/cellarr/utils_tiledb_array.html | 479 ++++ ...{skeleton.html => utils_tiledb_frame.html} | 212 +- _modules/index.html | 17 +- _sources/api/cellarr.rst.txt | 46 +- _sources/index.md.txt | 18 +- _sources/tutorial.md.txt | 58 + _static/documentation_options.js | 2 +- ...69c37c29e427902b24a333a5f9fcb2f0b3ac41.css | 2342 +++++++++++++++++ api/cellarr.html | 509 +++- api/modules.html | 55 +- authors.html | 11 +- changelog.html | 11 +- contributing.html | 17 +- genindex.html | 156 +- index.html | 41 +- license.html | 11 +- objects.inv | Bin 400 -> 632 bytes py-modindex.html | 43 +- readme.html | 118 +- search.html | 11 +- searchindex.js | 2 +- tutorial.html | 396 +++ 26 files changed, 5844 insertions(+), 345 deletions(-) create mode 100644 _modules/cellarr/CellArrDataset.html create mode 100644 _modules/cellarr/build_cellarrdataset.html create mode 100644 _modules/cellarr/utils_anndata.html create mode 100644 _modules/cellarr/utils_tiledb_array.html rename _modules/cellarr/{skeleton.html => utils_tiledb_frame.html} (52%) create mode 100644 _sources/tutorial.md.txt create mode 100644 _static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css create mode 100644 tutorial.html diff --git a/.buildinfo b/.buildinfo index 4b302b5..c752f75 100644 --- a/.buildinfo +++ b/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: c2816a1d94bae1060883498f48c5259b +config: c8acf869f0515f592e67571142b76a30 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_modules/cellarr/CellArrDataset.html b/_modules/cellarr/CellArrDataset.html new file mode 100644 index 0000000..baf2c96 --- /dev/null +++ b/_modules/cellarr/CellArrDataset.html @@ -0,0 +1,437 @@ + + + + + + + + cellarr.CellArrDataset - cellarr 0.0.2 documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Source code for cellarr.CellArrDataset

+import os
+from typing import Union
+
+import tiledb
+
+__author__ = "Jayaram Kancherla"
+__copyright__ = "Jayaram Kancherla"
+__license__ = "MIT"
+
+
+

+[docs]
+class CellArrDataset:
+    """A class that represent a collection of cells in TileDB."""
+
+

+[docs]
+    def __init__(
+        self,
+        dataset_path: str,
+        counts_tdb_uri: str = "counts",
+        gene_metadata_uri: str = "gene_metadata",
+        cell_metadata_uri: str = "cell_metadata",
+    ):
+        """Initialize a ``CellArr`` dataset.
+
+        Args:
+            counts_tdb_uri:
+                Path to counts TileDB.
+
+            gene_metadata_uri:
+                Path to gene metadata TileDB.
+
+            cell_metadata_uri:
+                Path to cell metadata TileDB.
+        """
+
+        if not os.path.isdir(dataset_path):
+            raise ValueError("'dataset_path' is not a directory.")
+
+        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._cell_metadata_tdb = tiledb.open(
+            f"{dataset_path}/{cell_metadata_uri}", "r"
+        )

+
+
+

+[docs]
+    def __del__(self):
+        self._counts_tdb_tdb.close()
+        self._gene_metadata_tdb.close()
+        self._cell_metadata_tdb.close()

+
+
+    # TODO:
+    # Methods to implement
+    # search by gene
+    # search by cell metadata
+    # slice counts after search
+
+

+[docs]
+    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)
+
+        return columns

+
+
+

+[docs]
+    def get_cell_metadata_column(self, column_name: str):
+        return self._cell_metadata_tdb.query(attrs=[column_name]).df[:]

+
+
+

+[docs]
+    def get_cell_subset(
+        self, subset: Union[slice, tiledb.QueryCondition], columns=None
+    ):
+        if columns is None:
+            columns = self.get_cell_metadata_columns()
+
+        query = self._cell_metadata_tdb.query(cond=subset, attrs=columns)
+        data = query.df[:]
+        result = data.dropna()
+        return result

+
+
+

+[docs]
+    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)
+
+        return columns

+
+
+

+[docs]
+    def get_gene_metadata_column(self, column_name: str):
+        return self._gene_metadata_tdb.query(attrs=[column_name]).df[:]

+
+
+

+[docs]
+    def get_gene_subset(
+        self, subset: Union[slice, tiledb.QueryCondition], columns=None
+    ):
+        if columns is None:
+            columns = self.get_gene_metadata_columns()
+
+        query = self._gene_metadata_tdb.query(cond=subset, attrs=columns)
+        data = query.df[:]
+        result = data.dropna()
+        return result

+
+
+

+[docs]
+    def get_slice(
+        self,
+        cell_subset: Union[slice, tiledb.QueryCondition],
+        gene_subset: Union[slice, tiledb.QueryCondition],
+    ):
+        _csubset = self.get_cell_subset(cell_subset)
+        _cell_indices = _csubset.index.tolist()
+
+        _gsubset = self.get_gene_subset(gene_subset)
+        _gene_indices = _gsubset.index.tolist()
+
+        return self._counts_tdb_tdb.multi_index[_cell_indices, _gene_indices]

+

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, Jayaram Kancherla +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/cellarr/build_cellarrdataset.html b/_modules/cellarr/build_cellarrdataset.html new file mode 100644 index 0000000..b916b9e --- /dev/null +++ b/_modules/cellarr/build_cellarrdataset.html @@ -0,0 +1,719 @@ + + + + + + + + cellarr.build_cellarrdataset - cellarr 0.0.2 documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
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+
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+ + + + + Back to top + +
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+ +
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+

Source code for cellarr.build_cellarrdataset

+"""Build the `CellArrDatset`
+
+The `CellArrDataset` method is designed to store single-cell RNA-seq
+datasets but can be generalized to store any 2-dimensional experimental data.
+
+This method creates three TileDB files in the directory specified by `output_path`:
+- `gene_metadata`: A TileDB file containing gene metadata.
+- `cell_metadata`: A TileDB file containing cell metadata.
+- A matrix TileDB file named as specified by the `layer_matrix_name` parameter.
+
+The TileDB matrix file is stored in a cell X gene orientation. This orientation
+is chosen because the fastest-changing dimension as new files are added to the
+collection is usually the cells rather than genes.
+
+## Process
+
+1. **Scan the Collection**: Scan the entire collection of files to create
+a unique set of gene symbols. Store this gene set as the
+`gene_metadata` TileDB file.
+2. **Store Cell Metadata**: Store cell metadata as the
+`cell_metadata` TileDB file.
+3. **Remap and Orient Data**: For each dataset in the collection,
+remap and orient the gene dimension using the gene set from Step 1.
+This step ensures consistency in gene measurement and order, even if
+some genes are unmeasured or ordered differently in the original experiments.
+
+
+Example:
+
+    .. code-block:: python
+
+        import anndata
+        import numpy as np
+        import tempfile
+        from cellarr import build_cellarrdataset, CellArrDataset
+
+        # Create a temporary directory
+        tempdir = tempfile.mkdtemp()
+
+        # Read AnnData objects
+        adata1 = anndata.read_h5ad("path/to/object1.h5ad")
+        # or just provide the path
+        adata2 = "path/to/object2.h5ad"
+
+        # Build CellArrDataset
+        dataset = build_cellarrdataset(
+            output_path=tempdir,
+            h5ad_or_adata=[adata1, adata2],
+            matrix_dim_dtype=np.float32
+        )
+"""
+
+import os
+import warnings
+from typing import List, Union
+
+import anndata
+import numpy as np
+import pandas as pd
+
+from . import utils_anndata as uad
+from . import utils_tiledb_array as uta
+from . import utils_tiledb_frame as utf
+from .CellArrDataset import CellArrDataset
+
+__author__ = "Jayaram Kancherla"
+__copyright__ = "Jayaram Kancherla"
+__license__ = "MIT"
+
+
+

+[docs]
+def build_cellarrdataset(
+    files: List[Union[str, anndata.AnnData]],
+    output_path: str,
+    num_cells: int = None,
+    num_genes: int = None,
+    cell_metadata: Union[pd.DataFrame, str] = None,
+    gene_metadata: Union[List[str], dict, str, pd.DataFrame] = None,
+    var_gene_column: str = "index",
+    layer_matrix_name: str = "counts",
+    skip_gene_tiledb: bool = False,
+    skip_cell_tiledb: bool = False,
+    skip_matrix_tiledb: bool = False,
+    cell_dim_dtype: np.dtype = np.uint32,
+    gene_dim_dtype: np.dtype = np.uint32,
+    matrix_dim_dtype: np.dtype = np.uint32,
+    optimize_tiledb: bool = True,
+    num_threads: int = 1,
+):
+    """Generate the `CellArrDataset`.
+
+    All files are expected to be consistent and any modifications
+    to make them consistent is outside the scope of this function
+    and package.
+
+    There's a few assumptions this process makes:
+    - If object in ``files`` is an AnnData or H5AD object, these must
+    contain an assay matrix in layer names as ``layer_matrix_name``
+    parameter.
+    - Feature information must contain a column defined by
+    ``var_gene_column`` that contains gene symbols or a common entity
+    across all files.
+    -
+
+    Args:
+        files:
+            List of file paths to `H5AD` or ``AnnData`` objects.
+            Each object in this list must contain
+            - gene symbols as index or the column specified by
+            ``var_gene_column``.
+            - Must contain a layers with a matrix named as
+            ``layer_matrix_name``.
+
+        output_path:
+            Path to where the output tiledb files should be stored.
+
+        num_cells:
+            Number of cells across all files.
+
+            Defualts to None, in which case, automatically inferred by
+            scanning all objects in ``h5ad_or_adata``.
+
+        num_genes:
+            Number of genes across all cells.
+
+            Defualts to None, in which case, automatically inferred by
+            scanning all objects in ``h5ad_or_adata``.
+
+        cell_metadata:
+            Path to the file containing a concatenated cell metadata across
+            all cells. In this case, the first row is expected to contain the
+            column names.
+
+            Alternatively, may also provide a dataframe containing the cell
+            metadata across all objects.
+
+            Regardless of the input type, the number of rows in the file or
+            DataFrame must match the ``num_cells`` argument.
+
+            Defaults to None, then a simple range index is created using the
+            ``num_cells`` argument.
+
+        gene_metadata:
+            Path to the file containing a concatenated gene annotations across
+            all datasets. In this case, the first row is
+            expected to contain the column names and an index column
+            containing the gene symbols to remap the matrix.
+
+            Alternatively, may also provide a dataframe containing the gene
+            annotations across all objects.
+
+            Alternatively, a list or a dictionary of gene symbols.
+
+            Regardless of the input type, the number of rows in the file or
+            DataFrame must match the ``num_genes`` argument.
+
+            Defaults to None, then a gene set is generated by scanning all
+            objects in ``h5ad_or_adata``.
+
+        var_gene_column:
+            Column name from ``var`` slot that contains the gene symbols.
+            Must be consistent across all objects in ``h5ad_or_adata``.
+
+            Defaults to "index".
+
+        layer_matrix_name:
+            Matrix name from ``layers`` slot to add to tiledb.
+            Must be consistent across all objects in ``h5ad_or_adata``.
+
+            Defaults to "counts".
+
+        skip_gene_tiledb:
+            Whether to skip generating gene metadata tiledb.
+
+            Defaults to False.
+
+        skip_cell_tiledb:
+            Whether to skip generating cell metadata tiledb.
+
+            Defaults to False.
+
+        skip_matrix_tiledb:
+            Whether to skip generating matrix tiledb.
+
+            Defaults to False.
+
+        cell_dim_dtype:
+            NumPy dtype for the cell dimension.
+            Defaults to np.uint32.
+
+            Note: make sure the number of cells fit
+            within the range limits of unsigned-int32.
+
+        gene_dim_dtype:
+            NumPy dtype for the gene dimension.
+            Defaults to np.uint32.
+
+            Note: make sure the number of genes fit
+            within the range limits of unsigned-int32.
+
+        matrix_dim_dtype:
+            NumPy dtype for the values in the matrix.
+            Defaults to np.uint32.
+
+            Note: make sure the matrix values fit
+            within the range limits of unsigned-int32.
+
+        num_threads:
+            Number of threads.
+
+            Defaults to 1.
+    """
+    if not os.path.isdir(output_path):
+        raise ValueError("'output_path' must be a directory.")
+
+    if gene_metadata is None:
+        warnings.warn(
+            "Scanning all files for gene symbols, this may take long", UserWarning
+        )
+        gene_set = uad.scan_for_genes(
+            files, var_gene_column=var_gene_column, num_threads=num_threads
+        )
+
+        gene_set = sorted(gene_set)
+
+        gene_metadata = pd.DataFrame({"genes": gene_set}, index=gene_set)
+    elif isinstance(gene_metadata, list):
+        _gene_list = sorted(list(set(gene_metadata)))
+        gene_metadata = pd.DataFrame({"genes": _gene_list}, index=_gene_list)
+    elif isinstance(gene_metadata, dict):
+        _gene_list = sorted(list(gene_metadata.keys()))
+        gene_metadata = pd.DataFrame({"genes": _gene_list}, index=_gene_list)
+    elif isinstance(gene_metadata, str):
+        gene_metadata = pd.read_csv(gene_metadata, index=True, header=True)
+
+    gene_metadata["genes_index"] = gene_metadata.index.tolist()
+
+    if not isinstance(gene_metadata, pd.DataFrame):
+        raise TypeError("'gene_metadata' must be a pandas dataframe.")
+
+    if len(gene_metadata.index.unique()) != len(gene_metadata.index.tolist()):
+        raise ValueError("'gene_metadata' must contain a unique index.")
+
+    if num_genes is None:
+        num_genes = len(gene_metadata)
+
+    # Create the gene metadata tiledb
+    if not skip_gene_tiledb:
+        _col_types = {}
+        for col in gene_metadata.columns:
+            _col_types[col] = "ascii"
+
+        _gene_output_uri = f"{output_path}/gene_metadata"
+        generate_metadata_tiledb_frame(
+            _gene_output_uri, gene_metadata, column_types=_col_types
+        )
+
+        if optimize_tiledb:
+            uta.optimize_tiledb_array(_gene_output_uri)
+
+    if cell_metadata is None:
+        if num_cells is None:
+            warnings.warn(
+                "Scanning all files to compute cell counts, this may take long",
+                UserWarning,
+            )
+            cell_counts = uad.scan_for_cellcounts(files, num_threads=num_threads)
+            num_cells = sum(cell_counts)
+
+        cell_metadata = pd.DataFrame({"cell_index": [x for x in range(num_cells)]})
+
+    if isinstance(cell_metadata, str):
+        warnings.warn(
+            "Scanning 'cell_metadata' to count number of cells, this may take long",
+            UserWarning,
+        )
+        with open(cell_metadata) as fp:
+            count = 0
+            for _ in fp:
+                count += 1
+
+        num_cells = count - 1  # removing 1 for the header line
+    elif isinstance(cell_metadata, pd.DataFrame):
+        num_cells = len(cell_metadata)
+
+    if num_cells is None:
+        raise ValueError(
+            "Cannot determine 'num_cells', we recommend setting this parameter."
+        )
+
+    # Create the cell metadata tiledb
+    if not skip_cell_tiledb:
+        _cell_output_uri = f"{output_path}/cell_metadata"
+
+        if isinstance(cell_metadata, str):
+            _cell_metaframe = pd.read_csv(cell_metadata, chunksize=5, header=True)
+            generate_metadata_tiledb_csv(
+                _cell_output_uri, cell_metadata, _cell_metaframe.columns
+            )
+        elif isinstance(cell_metadata, pd.DataFrame):
+            _col_types = {}
+            for col in gene_metadata.columns:
+                _col_types[col] = "ascii"
+
+            _to_write = gene_metadata.astype(str)
+
+            generate_metadata_tiledb_frame(
+                _cell_output_uri, _to_write, column_types=_col_types
+            )
+
+        if optimize_tiledb:
+            uta.optimize_tiledb_array(_cell_output_uri)
+
+    # create the counts metadata
+    if not skip_matrix_tiledb:
+        gene_idx = gene_metadata.index.tolist()
+        gene_set = {}
+        for i, x in enumerate(gene_idx):
+            gene_set[x] = i
+
+        _counts_uri = f"{output_path}/{layer_matrix_name}"
+        uta.create_tiledb_array(
+            _counts_uri,
+            num_cells=num_cells,
+            num_genes=num_genes,
+            matrix_attr_name=layer_matrix_name,
+            x_dim_dtype=cell_dim_dtype,
+            y_dim_dtype=gene_dim_dtype,
+            matrix_dim_dtype=matrix_dim_dtype,
+        )
+
+        offset = 0
+        for fd in files:
+            mat = uad.remap_anndata(
+                fd,
+                gene_set,
+                var_gene_column=var_gene_column,
+                layer_matrix_name=layer_matrix_name,
+            )
+            uta.write_csr_matrix_to_tiledb(
+                _counts_uri, matrix=mat, row_offset=offset, value_dtype=matrix_dim_dtype
+            )
+            offset += int(mat.shape[0])
+
+        if optimize_tiledb:
+            uta.optimize_tiledb_array(_counts_uri)
+
+    return CellArrDataset(dataset_path=output_path, counts_tdb_uri=layer_matrix_name)

+
+
+
+

+[docs]
+def generate_metadata_tiledb_frame(
+    output_uri: str, input: pd.DataFrame, column_types: dict = None
+):
+    """Generate metadata tiledb from a :pu:class:`~pandas.DataFrame`.
+
+    Args:
+        output_uri:
+            TileDB URI or path to save the file.
+
+        input:
+            Input dataframe.
+
+        column_types:
+            You can specify type of each column name to cast into.
+            "ascii" or str works best for most scenarios.
+
+            Defaults to None.
+    """
+    _to_write = input.astype(str)
+    utf.create_tiledb_frame_from_dataframe(
+        output_uri, _to_write, column_types=column_types
+    )

+
+
+
+

+[docs]
+def generate_metadata_tiledb_csv(
+    output_uri: str,
+    input: str,
+    column_dtype=str,
+    chunksize=1000,
+):
+    """Generate a metadata tiledb from csv.
+
+    The difference between this and ``generate_metadata_tiledb_frame``
+    is when the csv is super large and it won't fit into memory.
+
+    Args:
+        output_uri:
+            TileDB URI or path to save the file.
+
+        input:
+            Path to the csv file. The first row is expected to
+            contain the column names.
+
+        column_dtype:
+            Dtype of the columns.
+            Defaults to str.
+
+        chunksize:
+            Chunk size to read the dataframe.
+            Defaults to 1000.
+    """
+    chunksize = 1000
+    initfile = True
+    offset = 0
+
+    for chunk in pd.read_csv(input, chunksize=chunksize, header=True):
+        if initfile:
+            utf.create_tiledb_frame_from_column_names(
+                output_uri, chunk.columns, column_dtype
+            )
+            initfile = False
+
+        _to_write = chunk.astype(str)
+        utf.append_to_tiledb_frame(output_uri, _to_write, offset)
+        offset += len(chunk)

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, Jayaram Kancherla +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/cellarr/utils_anndata.html b/_modules/cellarr/utils_anndata.html new file mode 100644 index 0000000..e60e385 --- /dev/null +++ b/_modules/cellarr/utils_anndata.html @@ -0,0 +1,476 @@ + + + + + + + + cellarr.utils_anndata - cellarr 0.0.2 documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
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+ +
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+
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 +
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+
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+ +
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+

Source code for cellarr.utils_anndata

+import itertools
+from multiprocessing import Pool
+from typing import List, Union
+
+import anndata
+import numpy as np
+from scipy.sparse import coo_matrix, csr_array, csr_matrix
+
+__author__ = "Jayaram Kancherla, Tony Kuo"
+__copyright__ = "Jayaram Kancherla"
+__license__ = "MIT"
+
+
+

+[docs]
+def remap_anndata(
+    h5ad_or_adata: Union[str, anndata.AnnData],
+    gene_set_order: dict,
+    var_gene_column: str = "index",
+    layer_matrix_name: str = "counts",
+) -> csr_matrix:
+    """Extract and remap the count matrix to the provided gene set order from the :py:class:`~anndata.AnnData` object.
+
+    Args:
+        adata:
+            Input ``AnnData`` object.
+
+            Alternatively, may also provide a path to the H5ad file.
+
+            The index of the `var` slot must contain the gene symbols
+            for the columns in the matrix.
+
+        gene_set_order:
+            A dictionary with the symbols as keys and their index as value.
+            The gene symbols from the ``AnnData`` object are remapped to the
+            gene order from this dictionary.
+
+        var_gene_column:
+            Column in ``var`` containing the symbols.
+            Defaults to the index of the ``var`` slot.
+
+        layer_matrix_name:
+            Layer containing the matrix to add to tiledb.
+            Defaults to "counts".
+
+    Returns:
+        A ``csr_matrix`` representation of the assay matrix.
+    """
+
+    if isinstance(h5ad_or_adata, str):
+        adata = anndata.read_h5ad(h5ad_or_adata)
+    else:
+        if not isinstance(h5ad_or_adata, anndata.AnnData):
+            raise TypeError("Input is not an 'AnnData' object.")
+
+        adata = h5ad_or_adata
+
+    mat = adata.layers[layer_matrix_name]
+
+    if var_gene_column == "index":
+        symbols = adata.var.index.tolist()
+    else:
+        symbols = adata.var[var_gene_column].tolist()
+
+    indices_to_keep = [i for i, x in enumerate(symbols) if x in gene_set_order]
+    symbols_to_keep = [symbols[i] for i in indices_to_keep]
+
+    mat = mat[:, indices_to_keep].copy()
+
+    indices_to_map = []
+    for x in symbols_to_keep:
+        indices_to_map.append(gene_set_order[x])
+
+    if isinstance(mat, np.ndarray):
+        mat_coo = coo_matrix(mat)
+    elif isinstance(mat, (csr_array, csr_matrix)):
+        mat_coo = mat.tocoo()
+    else:
+        raise TypeError(f"Unknown matrix type: {type(mat)}.")
+
+    new_col = np.array([indices_to_map[i] for i in mat_coo.col])
+
+    return coo_matrix(
+        (mat_coo.data, (mat_coo.row, new_col)),
+        shape=(adata.shape[0], len(gene_set_order)),
+    ).tocsr()

+
+
+
+def _get_genes_index(
+    h5ad_or_adata: Union[str, anndata.AnnData],
+    var_gene_column: str = "index",
+) -> List[str]:
+    if isinstance(h5ad_or_adata, str):
+        adata = anndata.read_h5ad(h5ad_or_adata, backed=True)
+    else:
+        if not isinstance(h5ad_or_adata, anndata.AnnData):
+            raise TypeError("Input is not an 'AnnData' object.")
+
+        adata = h5ad_or_adata
+
+    if var_gene_column == "index":
+        symbols = adata.var.index.tolist()
+    else:
+        symbols = adata.var[var_gene_column].tolist()
+
+    return symbols
+
+
+def _wrapper_get_genes(args):
+    file, gcol = args
+    return _get_genes_index(file, gcol)
+
+
+

+[docs]
+def scan_for_genes(
+    h5ad_or_adata: List[Union[str, anndata.AnnData]],
+    var_gene_column: str = "index",
+    num_threads: int = 1,
+) -> List[str]:
+    """Extract and generate the list of unique genes identifiers across files.
+
+    Args:
+        h5ad_or_adata:
+            List of anndata objects or path to h5ad files.
+
+        var_gene_column:
+            Column containing the gene symbols.
+            Defaults to "index".
+
+        num_threads:
+            Number of threads to use.
+            Defaults to 1.
+
+    Returns:
+        List of all unique gene symbols across all files.
+    """
+    with Pool(num_threads) as p:
+        _args = [(file_info, var_gene_column) for file_info in h5ad_or_adata]
+        all_symbols = p.map(_wrapper_get_genes, _args)
+        return list(set(itertools.chain.from_iterable(all_symbols)))

+
+
+
+def _get_cellcounts(h5ad_or_adata: Union[str, anndata.AnnData]) -> int:
+    if isinstance(h5ad_or_adata, str):
+        adata = anndata.read_h5ad(h5ad_or_adata, backed=True)
+    else:
+        if not isinstance(h5ad_or_adata, anndata.AnnData):
+            raise TypeError("Input is not an 'AnnData' object.")
+
+        adata = h5ad_or_adata
+
+    return adata.shape[0]
+
+
+

+[docs]
+def scan_for_cellcounts(
+    h5ad_or_adata: List[Union[str, anndata.AnnData]],
+    num_threads: int = 1,
+) -> List[int]:
+    """Extract cell counts across files.
+
+    Args:
+        h5ad_or_adata:
+            List of anndata objects or path to h5ad files.
+
+        num_threads:
+            Number of threads to use.
+            Defaults to 1.
+
+    Returns:
+        List of cell counts across files.
+    """
+    with Pool(num_threads) as p:
+        all_counts = p.map(_get_cellcounts, h5ad_or_adata)
+        return all_counts

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, Jayaram Kancherla +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/cellarr/utils_tiledb_array.html b/_modules/cellarr/utils_tiledb_array.html new file mode 100644 index 0000000..67cc07a --- /dev/null +++ b/_modules/cellarr/utils_tiledb_array.html @@ -0,0 +1,479 @@ + + + + + + + + cellarr.utils_tiledb_array - cellarr 0.0.2 documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+ +
+
+
cellarr 0.0.2 documentation
 +
+
+
+ +
+ +
+
+ +
+
+
+ + + + + Back to top + +
+
+ +
+ +
+
+

Source code for cellarr.utils_tiledb_array

+import os
+import shutil
+from typing import Union
+
+import numpy as np
+import tiledb
+from scipy.sparse import csr_array, csr_matrix
+
+__author__ = "Jayaram Kancherla"
+__copyright__ = "Jayaram Kancherla"
+__license__ = "MIT"
+
+
+

+[docs]
+def create_tiledb_array(
+    tiledb_uri_path: str,
+    num_cells: int,
+    num_genes: int,
+    x_dim_name: str = "cell_index",
+    y_dim_name: str = "gene_index",
+    matrix_attr_name: str = "counts",
+    x_dim_dtype: np.dtype = np.uint32,
+    y_dim_dtype: np.dtype = np.uint32,
+    matrix_dim_dtype: np.dtype = np.uint32,
+    is_sparse: bool = True,
+):
+    """Create a tiledb file with the provided attributes to persistent storage.
+
+    This will materialize the array directory and all
+    related schema files.
+
+    Args:
+        tiledb_uri_path:
+            Path to create the array tiledb file.
+
+        num_cells:
+            Number of cells (x/fastest-changing dimension).
+
+        num_genes:
+            Number fo genes (y dimension).
+
+        x_dim_name:
+            Name for the x-dimension.
+            Defaults to "cell_index".
+
+        y_dim_name:
+            Name for the y-dimension.
+            Defaults to "gene_index".
+
+        matrix_attr_name:
+            Name for the attribute in the array.
+            Defaults to "counts".
+
+        x_dim_dtype:
+            NumPy dtype for the x-dimension.
+            Defaults to np.uint32.
+
+        y_dim_dtype:
+            NumPy dtype for the y-dimension.
+            Defaults to np.uint32.
+
+        matrix_dim_dtype:
+            NumPy dtype for the values in the matrix.
+            Defaults to np.uint32.
+
+        is_sparse:
+            Whether the matrix is sparse.
+            Defaults to True.
+    """
+
+    xdim = tiledb.Dim(name=x_dim_name, domain=(0, num_cells - 1), dtype=x_dim_dtype)
+    ydim = tiledb.Dim(name=y_dim_name, domain=(0, num_genes - 1), dtype=y_dim_dtype)
+
+    dom = tiledb.Domain(xdim, ydim)
+
+    # expecting counts
+    tdb_attr = tiledb.Attr(
+        name=matrix_attr_name,
+        dtype=matrix_dim_dtype,
+        filters=tiledb.FilterList([tiledb.GzipFilter()]),
+    )
+
+    schema = tiledb.ArraySchema(domain=dom, sparse=is_sparse, attrs=[tdb_attr])
+
+    if os.path.exists(tiledb_uri_path):
+        shutil.rmtree(tiledb_uri_path)
+
+    tiledb.Array.create(tiledb_uri_path, schema)
+
+    tdbfile = tiledb.open(tiledb_uri_path, "w")
+    tdbfile.close()

+
+
+
+

+[docs]
+def write_csr_matrix_to_tiledb(
+    tiledb_array_uri: Union[str, tiledb.SparseArray],
+    matrix: csr_matrix,
+    value_dtype: np.dtype = np.uint32,
+    row_offset: int = 0,
+    batch_size: int = 25000,
+):
+    """Append and save a :py:class:`~scipy.sparse.csr_matrix` to tiledb.
+
+    Args:
+        tiledb_array_uri:
+            Tiledb array object or path to a tiledb object.
+
+        matrix:
+            Input matrix to write to tiledb, must be a
+            :py:class:`~scipy.sparse.csr_matrix` matrix.
+
+        value_dtype:
+            NumPy dtype to reformat the matrix values.
+            Defaults to ``uint32``.
+
+        row_offset:
+            Offset row number to append to matrix.
+            Defaults to 0.
+
+        batch_size:
+            Batch size.
+            Defaults to 25000.
+    """
+    tiledb_fp = tiledb_array_uri
+    if isinstance(tiledb_array_uri, str):
+        tiledb_fp = tiledb.open(tiledb_array_uri, "w")
+
+    if not isinstance(matrix, (csr_array, csr_matrix)):
+        raise TypeError("sparse matrix must be in csr format.")
+
+    indptrs = matrix.indptr
+    indices = matrix.indices
+    data = matrix.data.astype(value_dtype)
+
+    x = []
+    y = []
+    vals = []
+    for i, indptr in enumerate(indptrs):
+        if i != 0 and (i % batch_size == 0 or i == len(indptrs) - 1):
+            tiledb_fp[x, y] = vals
+            x = []
+            y = []
+            vals = []
+
+        stop = None
+        if i != len(indptrs) - 1:
+            stop = indptrs[i + 1]
+
+        val_slice = data[slice(indptr, stop)]
+        ind_slice = indices[slice(indptr, stop)]
+
+        x.extend([row_offset + i] * len(ind_slice))
+        y.extend(ind_slice)
+        vals.extend(val_slice)
+
+    tiledb_fp[x, y] = vals

+
+
+
+

+[docs]
+def optimize_tiledb_array(tiledb_array_uri: str, verbose: bool = True):
+    """Consolidate tiledb fragments."""
+    if verbose:
+        print(f"Optimizing {tiledb_array_uri}")
+
+    frags = tiledb.array_fragments(tiledb_array_uri)
+    if verbose:
+        print("Fragments before consolidation: {}".format(len(frags)))
+
+    cfg = tiledb.Config()
+    cfg["sm.consolidation.step_min_frags"] = 1
+    cfg["sm.consolidation.step_max_frags"] = 200
+    tiledb.consolidate(tiledb_array_uri, config=cfg)
+    tiledb.vacuum(tiledb_array_uri)
+
+    frags = tiledb.array_fragments(tiledb_array_uri)
+    if verbose:
+        print("Fragments after consolidation: {}".format(len(frags)))

+
+
+
+
+
+ +
+ + +
+
+
+
+ Copyright © 2024, Jayaram Kancherla +
+ Made with Sphinx and @pradyunsg's + + Furo + +
+
+ +
+
+ +
+
+ +
+
+ + + + + \ No newline at end of file diff --git a/_modules/cellarr/skeleton.html b/_modules/cellarr/utils_tiledb_frame.html similarity index 52% rename from _modules/cellarr/skeleton.html rename to _modules/cellarr/utils_tiledb_frame.html index ae48649..0c94b48 100644 --- a/_modules/cellarr/skeleton.html +++ b/_modules/cellarr/utils_tiledb_frame.html @@ -5,9 +5,10 @@ - cellarr.skeleton - cellarr 0.0.1 documentation + cellarr.utils_tiledb_frame - cellarr 0.0.2 documentation + @@ -178,7 +179,7 @@
-
cellarr 0.0.1 documentation
 +
cellarr 0.0.2 documentation
@@ -202,7 +203,7 @@
- cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
-

Source code for cellarr.skeleton

-"""
-This is a skeleton file that can serve as a starting point for a Python
-console script. To run this script uncomment the following lines in the
-``[options.entry_points]`` section in ``setup.cfg``::
+          
Source code for cellarr.utils_tiledb_frame
+import os
+import shutil
+from typing import List
 
-    console_scripts =
-         fibonacci = cellarr.skeleton:run
+import pandas as pd
+import tiledb
 
-Then run ``pip install .`` (or ``pip install -e .`` for editable mode)
-which will install the command ``fibonacci`` inside your current environment.
-
-Besides console scripts, the header (i.e. until ``_logger``...) of this file can
-also be used as template for Python modules.
-
-Note:
-    This file can be renamed depending on your needs or safely removed if not needed.
-
-References:
-    - https://setuptools.pypa.io/en/latest/userguide/entry_point.html
-    - https://pip.pypa.io/en/stable/reference/pip_install
-"""
-
-import argparse
-import logging
-import sys
-
-from cellarr import __version__
-
-__author__ = "Jayaram Kancherla"
+__author__ = "Jayaram Kancherla, Tony Kuo"
 __copyright__ = "Jayaram Kancherla"
 __license__ = "MIT"
 
-_logger = logging.getLogger(__name__)
 
+

+[docs]
+def create_tiledb_frame_from_column_names(
+    tiledb_uri_path: str, column_names: List[str], column_dtype=str
+):
+    """Create a tiledb file with the provided attributes to persistent storage.
 
-# ---- Python API ----
-# The functions defined in this section can be imported by users in their
-# Python scripts/interactive interpreter, e.g. via
-# `from cellarr.skeleton import fib`,
-# when using this Python module as a library.
-
-
-

-[docs]
-def fib(n):
-    """Fibonacci example function
+    This will materialize the array directory and all
+    related schema files.
 
     Args:
-      n (int): integer
+        tiledb_uri_path:
+            Path to create the metadata tiledb file.
+
+        column_names:
+            Column names of the data frame.
 
-    Returns:
-      int: n-th Fibonacci number
+        column_dtype:
+            Type for the columns, usually str.
+            Defaults to string.
     """
-    assert n > 0
-    a, b = 1, 1
-    for _i in range(n - 1):
-        a, b = b, a + b
-    return a

+    if os.path.exists(tiledb_uri_path):
+        shutil.rmtree(tiledb_uri_path)
 
+    df = pd.DataFrame(columns=list(column_names), dtype=column_dtype)
+    for c in df.columns:
+        df.loc[0, c] = "None"
 
+    tiledb.from_pandas(tiledb_uri_path, df, mode="schema_only", full_domain=True)

 
-# ---- CLI ----
-# The functions defined in this section are wrappers around the main Python
-# API allowing them to be called directly from the terminal as a CLI
-# executable/script.
 
 
-

-[docs]
-def parse_args(args):
-    """Parse command line parameters
+

+[docs]
+def create_tiledb_frame_from_dataframe(
+    tiledb_uri_path: str, frame: List[str], column_types=dict
+):
+    """Create a tiledb file with the provided attributes to persistent storage.
+
+    This will materialize the array directory and all
+    related schema files.
 
     Args:
-      args (List[str]): command line parameters as list of strings
-          (for example  ``["--help"]``).
+        tiledb_uri_path:
+            Path to create the metadata tiledb file.
 
-    Returns:
-      :obj:`argparse.Namespace`: command line parameters namespace
-    """
-    parser = argparse.ArgumentParser(description="Just a Fibonacci demonstration")
-    parser.add_argument(
-        "--version",
-        action="version",
-        version=f"cellarr {__version__}",
-    )
-    parser.add_argument(dest="n", help="n-th Fibonacci number", type=int, metavar="INT")
-    parser.add_argument(
-        "-v",
-        "--verbose",
-        dest="loglevel",
-        help="set loglevel to INFO",
-        action="store_const",
-        const=logging.INFO,
-    )
-    parser.add_argument(
-        "-vv",
-        "--very-verbose",
-        dest="loglevel",
-        help="set loglevel to DEBUG",
-        action="store_const",
-        const=logging.DEBUG,
-    )
-    return parser.parse_args(args)

-
-
-
-

-[docs]
-def setup_logging(loglevel):
-    """Setup basic logging
+        column_names:
+            Column names of the data frame.
 
-    Args:
-      loglevel (int): minimum loglevel for emitting messages
+        column_types:
+            Dictionary specifying the column types for each
+            column in the frame.
     """
-    logformat = "[%(asctime)s] %(levelname)s:%(name)s:%(message)s"
-    logging.basicConfig(
-        level=loglevel, stream=sys.stdout, format=logformat, datefmt="%Y-%m-%d %H:%M:%S"
-    )

-
+    if os.path.exists(tiledb_uri_path):
+        shutil.rmtree(tiledb_uri_path)
 
+    tiledb.from_pandas(tiledb_uri_path, dataframe=frame, column_types=column_types)

 
-

-[docs]
-def main(args):
-    """Wrapper allowing :func:`fib` to be called with string arguments in a CLI fashion
 
-    Instead of returning the value from :func:`fib`, it prints the result to the
-    ``stdout`` in a nicely formatted message.
 
-    Args:
-      args (List[str]): command line parameters as list of strings
-          (for example  ``["--verbose", "42"]``).
-    """
-    args = parse_args(args)
-    setup_logging(args.loglevel)
-    _logger.debug("Starting crazy calculations...")
-    print(f"The {args.n}-th Fibonacci number is {fib(args.n)}")
-    _logger.info("Script ends here")

+

+[docs]
+def append_to_tiledb_frame(
+    tiledb_uri_path: str, frame: pd.DataFrame, row_offset: int = 0
+):
+    """Create a tiledb file with the provided attributes to persistent storage.
 
+    This will materialize the array directory and all
+    related schema files.
 
+    Args:
+        tiledb_uri_path:
+            Path to create the metadata tiledb file.
 
-

-[docs]
-def run():
-    """Calls :func:`main` passing the CLI arguments extracted from :obj:`sys.argv`
+        frame:
+            Pandas Dataframe to append to tiledb.
 
-    This function can be used as entry point to create console scripts with setuptools.
+        row_offset:
+            Row offset to append new rows to.
+            Defaults to 0.
     """
-    main(sys.argv[1:])

-
-
-
-if __name__ == "__main__":
-    # ^  This is a guard statement that will prevent the following code from
-    #    being executed in the case someone imports this file instead of
-    #    executing it as a script.
-    #    https://docs.python.org/3/library/__main__.html
+    tiledb.from_pandas(
+        tiledb_uri_path, dataframe=frame, mode="append", row_start_idx=row_offset
+    )

 
-    # After installing your project with pip, users can also run your Python
-    # modules as scripts via the ``-m`` flag, as defined in PEP 338::
-    #
-    #     python -m cellarr.skeleton 42
-    #
-    run()
@@ -451,7 +385,7 @@

Source code for cellarr.skeleton
-
+
diff --git a/_modules/index.html b/_modules/index.html index abfed26..be225ea 100644 --- a/_modules/index.html +++ b/_modules/index.html @@ -5,9 +5,10 @@ - Overview: module code - cellarr 0.0.1 documentation + Overview: module code - cellarr 0.0.2 documentation + @@ -178,7 +179,7 @@
-
cellarr 0.0.1 documentation
 +
cellarr 0.0.2 documentation
@@ -202,7 +203,7 @@
- cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
@@ -288,7 +293,7 @@

All modules for which code is available

-
+
diff --git a/_sources/api/cellarr.rst.txt b/_sources/api/cellarr.rst.txt index 548d860..6cc3e43 100644 --- a/_sources/api/cellarr.rst.txt +++ b/_sources/api/cellarr.rst.txt @@ -4,10 +4,50 @@ cellarr package Submodules ---------- -cellarr.skeleton module ------------------------ +cellarr.CellArrDataset module +----------------------------- -.. automodule:: cellarr.skeleton +.. automodule:: cellarr.CellArrDataset + :members: + :undoc-members: + :show-inheritance: + +cellarr.build\_cellarrdataset module +------------------------------------ + +.. automodule:: cellarr.build_cellarrdataset + :members: + :undoc-members: + :show-inheritance: + +cellarr.dataloader module +------------------------- + +.. automodule:: cellarr.dataloader + :members: + :undoc-members: + :show-inheritance: + +cellarr.utils\_anndata module +----------------------------- + +.. automodule:: cellarr.utils_anndata + :members: + :undoc-members: + :show-inheritance: + +cellarr.utils\_tiledb\_array module +----------------------------------- + +.. automodule:: cellarr.utils_tiledb_array + :members: + :undoc-members: + :show-inheritance: + +cellarr.utils\_tiledb\_frame module +----------------------------------- + +.. automodule:: cellarr.utils_tiledb_frame :members: :undoc-members: :show-inheritance: diff --git a/_sources/index.md.txt b/_sources/index.md.txt index 458985e..7283ccf 100644 --- a/_sources/index.md.txt +++ b/_sources/index.md.txt @@ -1,25 +1,21 @@ # cellarr -Add a short description here! +Cell Arrays is a Python package that provides a TileDB-backed store for large collections of genomic experimental data, such as millions of cells across multiple single-cell experiment objects. +## Install -## Note - -> This is the main page of your project's [Sphinx] documentation. It is -> formatted in [Markdown]. Add additional pages by creating md-files in -> `docs` or rst-files (formatted in [reStructuredText]) and adding links to -> them in the `Contents` section below. -> -> Please check [Sphinx] and [MyST] for more information -> about how to document your project and how to configure your preferences. +To get started, install the package from [PyPI](https://pypi.org/project/cellarr/) +```bash +pip install cellarr +``` ## Contents ```{toctree} :maxdepth: 2 -Overview +Overview Contributions & Help License Authors diff --git a/_sources/tutorial.md.txt b/_sources/tutorial.md.txt new file mode 100644 index 0000000..4778202 --- /dev/null +++ b/_sources/tutorial.md.txt @@ -0,0 +1,58 @@ +--- +file_format: mystnb +kernelspec: + name: python +--- + +# Cell Arrays + +Cell Arrays is a Python package that provides a TileDB-backed store for large collections of genomic experimental data, such as millions of cells across multiple single-cell experiment objects. + +## Usage + +### Create the `CellArrDataset` + +Creating a CellArrDataset generates three TileDB files in the specified output directory: + +- `gene_metadata`: Contains feature annotations. +- `cell_metadata`: Contains cell or sample metadata. +- `matrix`: A TileDB-backed sparse array containing expression vectors. + +***Note: Currently only supports either paths to H5AD or `AnnData` objects*** + +To build a `CellArrDataset` from a collection of `H5AD` or `AnnData` objects: + +```python +import anndata +import numpy as np +import tempfile +from cellarr import build_cellarrdataset, CellArrDataset + +# Create a temporary directory +tempdir = tempfile.mkdtemp() + +# Read AnnData objects +adata1 = anndata.read_h5ad("path/to/object1.h5ad") +# or just provide the path +adata2 = "path/to/object2.h5ad" + +# Build CellArrDataset +dataset = build_cellarrdataset( + output_path=tempdir, + h5ad_or_adata=[adata1, adata2], + matrix_dim_dtype=np.float32 +) +``` +---- + +#### TODO: This following section does not work yet. + +Users have the option to reuse the `dataset` object retuned when building the dataset or by creating a `CellArrDataset` object by initializng it to the path where the files were created. + +```python +# Create a CellArrDataset object from the existing dataset +dataset = CellArrDataset(dataset_path=tempdir) + +# Query data from the dataset +expression_data = dataset[10, ["gene1", "gene10", "gene500"]] +``` diff --git a/_static/documentation_options.js b/_static/documentation_options.js index d1f2291..b15f8b4 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,5 +1,5 @@ const DOCUMENTATION_OPTIONS = { - VERSION: '0.0.1', + VERSION: '0.0.2', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css b/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css new file mode 100644 index 0000000..3356631 --- /dev/null +++ b/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css @@ -0,0 +1,2342 @@ +/* Variables */ +:root { + --mystnb-source-bg-color: #f7f7f7; 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+} + +div.highlight .-Color[class*=-C71] { + color: #5FAF5F +} + +div.highlight .-Color[class*=-BGC71] { + background-color: #5FAF5F +} + +div.highlight .-Color[class*=-C72] { + color: #5FAF87 +} + +div.highlight .-Color[class*=-BGC72] { + background-color: #5FAF87 +} + +div.highlight .-Color[class*=-C73] { + color: #5FAFAF +} + +div.highlight .-Color[class*=-BGC73] { + background-color: #5FAFAF +} + +div.highlight .-Color[class*=-C74] { + color: #5FAFD7 +} + +div.highlight .-Color[class*=-BGC74] { + background-color: #5FAFD7 +} + +div.highlight .-Color[class*=-C75] { + color: #5FAFFF +} + +div.highlight .-Color[class*=-BGC75] { + background-color: #5FAFFF +} + +div.highlight .-Color[class*=-C76] { + color: #5FD700 +} + +div.highlight .-Color[class*=-BGC76] { + background-color: #5FD700 +} + +div.highlight .-Color[class*=-C77] { + color: #5FD75F +} + +div.highlight .-Color[class*=-BGC77] { + background-color: #5FD75F +} + +div.highlight .-Color[class*=-C78] { + color: #5FD787 +} + +div.highlight .-Color[class*=-BGC78] { + background-color: #5FD787 +} + +div.highlight .-Color[class*=-C79] { + color: #5FD7AF +} + +div.highlight .-Color[class*=-BGC79] { + background-color: #5FD7AF +} + +div.highlight .-Color[class*=-C80] { + color: #5FD7D7 +} + +div.highlight .-Color[class*=-BGC80] { + background-color: #5FD7D7 +} + +div.highlight .-Color[class*=-C81] { + color: #5FD7FF +} + +div.highlight .-Color[class*=-BGC81] { + background-color: #5FD7FF +} + +div.highlight .-Color[class*=-C82] { + color: #5FFF00 +} + +div.highlight .-Color[class*=-BGC82] { + background-color: #5FFF00 +} + +div.highlight .-Color[class*=-C83] { + color: #5FFF5F +} + +div.highlight .-Color[class*=-BGC83] { + background-color: #5FFF5F +} + +div.highlight .-Color[class*=-C84] { + color: #5FFF87 +} + +div.highlight .-Color[class*=-BGC84] { + background-color: #5FFF87 +} + +div.highlight .-Color[class*=-C85] { + color: #5FFFAF +} + +div.highlight .-Color[class*=-BGC85] { + background-color: #5FFFAF +} + +div.highlight .-Color[class*=-C86] { + color: #5FFFD7 +} + +div.highlight .-Color[class*=-BGC86] { + background-color: #5FFFD7 +} + +div.highlight .-Color[class*=-C87] { + color: #5FFFFF +} + +div.highlight .-Color[class*=-BGC87] { + background-color: #5FFFFF +} + +div.highlight .-Color[class*=-C88] { + color: #870000 +} + +div.highlight .-Color[class*=-BGC88] { + background-color: #870000 +} + +div.highlight .-Color[class*=-C89] { + color: #87005F +} + +div.highlight .-Color[class*=-BGC89] { + background-color: #87005F +} + +div.highlight .-Color[class*=-C90] { + color: #870087 +} + +div.highlight .-Color[class*=-BGC90] { + background-color: #870087 +} + +div.highlight .-Color[class*=-C91] { + color: #8700AF +} + +div.highlight .-Color[class*=-BGC91] { + background-color: #8700AF +} + +div.highlight .-Color[class*=-C92] { + color: #8700D7 +} + +div.highlight .-Color[class*=-BGC92] { + background-color: #8700D7 +} + +div.highlight .-Color[class*=-C93] { + color: #8700FF +} + +div.highlight .-Color[class*=-BGC93] { + background-color: #8700FF +} + +div.highlight .-Color[class*=-C94] { + color: #875F00 +} + +div.highlight .-Color[class*=-BGC94] { + background-color: #875F00 +} + +div.highlight .-Color[class*=-C95] { + color: #875F5F +} + +div.highlight .-Color[class*=-BGC95] { + background-color: #875F5F +} + +div.highlight .-Color[class*=-C96] { + color: #875F87 +} + +div.highlight .-Color[class*=-BGC96] { + background-color: #875F87 +} + +div.highlight .-Color[class*=-C97] { + color: #875FAF +} + +div.highlight .-Color[class*=-BGC97] { + background-color: #875FAF +} + +div.highlight .-Color[class*=-C98] { + color: #875FD7 +} + +div.highlight .-Color[class*=-BGC98] { + background-color: #875FD7 +} + +div.highlight .-Color[class*=-C99] { + color: #875FFF +} + +div.highlight .-Color[class*=-BGC99] { + background-color: #875FFF +} + +div.highlight .-Color[class*=-C100] { + color: #878700 +} + +div.highlight .-Color[class*=-BGC100] { + background-color: #878700 +} + +div.highlight .-Color[class*=-C101] { + color: #87875F +} + +div.highlight .-Color[class*=-BGC101] { + background-color: #87875F +} + +div.highlight .-Color[class*=-C102] { + color: #878787 +} + +div.highlight .-Color[class*=-BGC102] { + background-color: #878787 +} + +div.highlight .-Color[class*=-C103] { + color: #8787AF +} + +div.highlight .-Color[class*=-BGC103] { + background-color: #8787AF +} + +div.highlight .-Color[class*=-C104] { + color: #8787D7 +} + +div.highlight .-Color[class*=-BGC104] { + background-color: #8787D7 +} + +div.highlight .-Color[class*=-C105] { + color: #8787FF +} + +div.highlight .-Color[class*=-BGC105] { + background-color: #8787FF +} + +div.highlight .-Color[class*=-C106] { + color: #87AF00 +} + +div.highlight .-Color[class*=-BGC106] { + background-color: #87AF00 +} + +div.highlight .-Color[class*=-C107] { + color: #87AF5F +} + +div.highlight .-Color[class*=-BGC107] { + background-color: #87AF5F +} + +div.highlight .-Color[class*=-C108] { + color: #87AF87 +} + +div.highlight .-Color[class*=-BGC108] { + background-color: #87AF87 +} + +div.highlight .-Color[class*=-C109] { + color: #87AFAF +} + +div.highlight .-Color[class*=-BGC109] { + background-color: #87AFAF +} + +div.highlight .-Color[class*=-C110] { + color: #87AFD7 +} + +div.highlight .-Color[class*=-BGC110] { + background-color: #87AFD7 +} + +div.highlight .-Color[class*=-C111] { + color: #87AFFF +} + +div.highlight .-Color[class*=-BGC111] { + background-color: #87AFFF +} + +div.highlight .-Color[class*=-C112] { + color: #87D700 +} + +div.highlight .-Color[class*=-BGC112] { + background-color: #87D700 +} + +div.highlight .-Color[class*=-C113] { + color: #87D75F +} + +div.highlight .-Color[class*=-BGC113] { + background-color: #87D75F +} + +div.highlight .-Color[class*=-C114] { + color: #87D787 +} + +div.highlight .-Color[class*=-BGC114] { + background-color: #87D787 +} + +div.highlight .-Color[class*=-C115] { + color: #87D7AF +} + +div.highlight .-Color[class*=-BGC115] { + background-color: #87D7AF +} + +div.highlight .-Color[class*=-C116] { + color: #87D7D7 +} + +div.highlight .-Color[class*=-BGC116] { + background-color: #87D7D7 +} + +div.highlight .-Color[class*=-C117] { + color: #87D7FF +} + +div.highlight .-Color[class*=-BGC117] { + background-color: #87D7FF +} + +div.highlight .-Color[class*=-C118] { + color: #87FF00 +} + +div.highlight .-Color[class*=-BGC118] { + background-color: #87FF00 +} + +div.highlight .-Color[class*=-C119] { + color: #87FF5F +} + +div.highlight .-Color[class*=-BGC119] { + background-color: #87FF5F +} + +div.highlight .-Color[class*=-C120] { + color: #87FF87 +} + +div.highlight .-Color[class*=-BGC120] { + background-color: #87FF87 +} + +div.highlight .-Color[class*=-C121] { + color: #87FFAF +} + +div.highlight .-Color[class*=-BGC121] { + background-color: #87FFAF +} + +div.highlight .-Color[class*=-C122] { + color: #87FFD7 +} + +div.highlight .-Color[class*=-BGC122] { + background-color: #87FFD7 +} + +div.highlight .-Color[class*=-C123] { + color: #87FFFF +} + +div.highlight .-Color[class*=-BGC123] { + background-color: #87FFFF +} + +div.highlight .-Color[class*=-C124] { + color: #AF0000 +} + +div.highlight .-Color[class*=-BGC124] { + background-color: #AF0000 +} + +div.highlight .-Color[class*=-C125] { + color: #AF005F +} + +div.highlight .-Color[class*=-BGC125] { + background-color: #AF005F +} + +div.highlight .-Color[class*=-C126] { + color: #AF0087 +} + +div.highlight .-Color[class*=-BGC126] { + background-color: #AF0087 +} + +div.highlight .-Color[class*=-C127] { + color: #AF00AF +} + +div.highlight .-Color[class*=-BGC127] { + background-color: #AF00AF +} + +div.highlight .-Color[class*=-C128] { + color: #AF00D7 +} + +div.highlight .-Color[class*=-BGC128] { + background-color: #AF00D7 +} + +div.highlight .-Color[class*=-C129] { + color: #AF00FF +} + +div.highlight .-Color[class*=-BGC129] { + background-color: #AF00FF +} + +div.highlight .-Color[class*=-C130] { + color: #AF5F00 +} + +div.highlight .-Color[class*=-BGC130] { + background-color: #AF5F00 +} + +div.highlight .-Color[class*=-C131] { + color: #AF5F5F +} + +div.highlight .-Color[class*=-BGC131] { + background-color: #AF5F5F +} + +div.highlight .-Color[class*=-C132] { + color: #AF5F87 +} + +div.highlight .-Color[class*=-BGC132] { + background-color: #AF5F87 +} + +div.highlight .-Color[class*=-C133] { + color: #AF5FAF +} + +div.highlight .-Color[class*=-BGC133] { + background-color: #AF5FAF +} + +div.highlight .-Color[class*=-C134] { + color: #AF5FD7 +} + +div.highlight .-Color[class*=-BGC134] { + background-color: #AF5FD7 +} + +div.highlight .-Color[class*=-C135] { + color: #AF5FFF +} + +div.highlight .-Color[class*=-BGC135] { + background-color: #AF5FFF +} + +div.highlight .-Color[class*=-C136] { + color: #AF8700 +} + +div.highlight .-Color[class*=-BGC136] { + background-color: #AF8700 +} + +div.highlight .-Color[class*=-C137] { + color: #AF875F +} + +div.highlight .-Color[class*=-BGC137] { + background-color: #AF875F +} + +div.highlight .-Color[class*=-C138] { + color: #AF8787 +} + +div.highlight .-Color[class*=-BGC138] { + background-color: #AF8787 +} + +div.highlight .-Color[class*=-C139] { + color: #AF87AF +} + +div.highlight .-Color[class*=-BGC139] { + background-color: #AF87AF +} + +div.highlight .-Color[class*=-C140] { + color: #AF87D7 +} + +div.highlight .-Color[class*=-BGC140] { + background-color: #AF87D7 +} + +div.highlight .-Color[class*=-C141] { + color: #AF87FF +} + +div.highlight .-Color[class*=-BGC141] { + background-color: #AF87FF +} + +div.highlight .-Color[class*=-C142] { + color: #AFAF00 +} + +div.highlight .-Color[class*=-BGC142] { + background-color: #AFAF00 +} + +div.highlight .-Color[class*=-C143] { + color: #AFAF5F +} + +div.highlight .-Color[class*=-BGC143] { + background-color: #AFAF5F +} + +div.highlight .-Color[class*=-C144] { + color: #AFAF87 +} + +div.highlight .-Color[class*=-BGC144] { + background-color: #AFAF87 +} + +div.highlight .-Color[class*=-C145] { + color: #AFAFAF +} + +div.highlight .-Color[class*=-BGC145] { + background-color: #AFAFAF +} + +div.highlight .-Color[class*=-C146] { + color: #AFAFD7 +} + +div.highlight .-Color[class*=-BGC146] { + background-color: #AFAFD7 +} + +div.highlight .-Color[class*=-C147] { + color: #AFAFFF +} + +div.highlight .-Color[class*=-BGC147] { + background-color: #AFAFFF +} + +div.highlight .-Color[class*=-C148] { + color: #AFD700 +} + +div.highlight .-Color[class*=-BGC148] { + background-color: #AFD700 +} + +div.highlight .-Color[class*=-C149] { + color: #AFD75F +} + +div.highlight .-Color[class*=-BGC149] { + background-color: #AFD75F +} + +div.highlight .-Color[class*=-C150] { + color: #AFD787 +} + +div.highlight .-Color[class*=-BGC150] { + background-color: #AFD787 +} + +div.highlight .-Color[class*=-C151] { + color: #AFD7AF +} + +div.highlight .-Color[class*=-BGC151] { + background-color: #AFD7AF +} + +div.highlight .-Color[class*=-C152] { + color: #AFD7D7 +} + +div.highlight .-Color[class*=-BGC152] { + background-color: #AFD7D7 +} + +div.highlight .-Color[class*=-C153] { + color: #AFD7FF +} + +div.highlight .-Color[class*=-BGC153] { + background-color: #AFD7FF +} + +div.highlight .-Color[class*=-C154] { + color: #AFFF00 +} + +div.highlight .-Color[class*=-BGC154] { + background-color: #AFFF00 +} + +div.highlight .-Color[class*=-C155] { + color: #AFFF5F +} + +div.highlight .-Color[class*=-BGC155] { + background-color: #AFFF5F +} + +div.highlight .-Color[class*=-C156] { + color: #AFFF87 +} + +div.highlight .-Color[class*=-BGC156] { + background-color: #AFFF87 +} + +div.highlight .-Color[class*=-C157] { + color: #AFFFAF +} + +div.highlight .-Color[class*=-BGC157] { + background-color: #AFFFAF +} + +div.highlight .-Color[class*=-C158] { + color: #AFFFD7 +} + +div.highlight .-Color[class*=-BGC158] { + background-color: #AFFFD7 +} + +div.highlight .-Color[class*=-C159] { + color: #AFFFFF +} + +div.highlight .-Color[class*=-BGC159] { + background-color: #AFFFFF +} + +div.highlight .-Color[class*=-C160] { + color: #D70000 +} + +div.highlight .-Color[class*=-BGC160] { + background-color: #D70000 +} + +div.highlight .-Color[class*=-C161] { + color: #D7005F +} + +div.highlight .-Color[class*=-BGC161] { + background-color: #D7005F +} + +div.highlight .-Color[class*=-C162] { + color: #D70087 +} + +div.highlight .-Color[class*=-BGC162] { + background-color: #D70087 +} + +div.highlight .-Color[class*=-C163] { + color: #D700AF +} + +div.highlight .-Color[class*=-BGC163] { + background-color: #D700AF +} + +div.highlight .-Color[class*=-C164] { + color: #D700D7 +} + +div.highlight .-Color[class*=-BGC164] { + background-color: #D700D7 +} + +div.highlight .-Color[class*=-C165] { + color: #D700FF +} + +div.highlight .-Color[class*=-BGC165] { + background-color: #D700FF +} + +div.highlight .-Color[class*=-C166] { + color: #D75F00 +} + +div.highlight .-Color[class*=-BGC166] { + background-color: #D75F00 +} + +div.highlight .-Color[class*=-C167] { + color: #D75F5F +} + +div.highlight .-Color[class*=-BGC167] { + background-color: #D75F5F +} + +div.highlight .-Color[class*=-C168] { + color: #D75F87 +} + +div.highlight .-Color[class*=-BGC168] { + background-color: #D75F87 +} + +div.highlight .-Color[class*=-C169] { + color: #D75FAF +} + +div.highlight .-Color[class*=-BGC169] { + background-color: #D75FAF +} + +div.highlight .-Color[class*=-C170] { + color: #D75FD7 +} + +div.highlight .-Color[class*=-BGC170] { + background-color: #D75FD7 +} + +div.highlight .-Color[class*=-C171] { + color: #D75FFF +} + +div.highlight .-Color[class*=-BGC171] { + background-color: #D75FFF +} + +div.highlight .-Color[class*=-C172] { + color: #D78700 +} + +div.highlight .-Color[class*=-BGC172] { + background-color: #D78700 +} + +div.highlight .-Color[class*=-C173] { + color: #D7875F +} + +div.highlight .-Color[class*=-BGC173] { + background-color: #D7875F +} + +div.highlight .-Color[class*=-C174] { + color: #D78787 +} + +div.highlight .-Color[class*=-BGC174] { + background-color: #D78787 +} + +div.highlight .-Color[class*=-C175] { + color: #D787AF +} + +div.highlight .-Color[class*=-BGC175] { + background-color: #D787AF +} + +div.highlight .-Color[class*=-C176] { + color: #D787D7 +} + +div.highlight .-Color[class*=-BGC176] { + background-color: #D787D7 +} + +div.highlight .-Color[class*=-C177] { + color: #D787FF +} + +div.highlight .-Color[class*=-BGC177] { + background-color: #D787FF +} + +div.highlight .-Color[class*=-C178] { + color: #D7AF00 +} + +div.highlight .-Color[class*=-BGC178] { + background-color: #D7AF00 +} + +div.highlight .-Color[class*=-C179] { + color: #D7AF5F +} + +div.highlight .-Color[class*=-BGC179] { + background-color: #D7AF5F +} + +div.highlight .-Color[class*=-C180] { + color: #D7AF87 +} + +div.highlight .-Color[class*=-BGC180] { + background-color: #D7AF87 +} + +div.highlight .-Color[class*=-C181] { + color: #D7AFAF +} + +div.highlight .-Color[class*=-BGC181] { + background-color: #D7AFAF +} + +div.highlight .-Color[class*=-C182] { + color: #D7AFD7 +} + +div.highlight .-Color[class*=-BGC182] { + background-color: #D7AFD7 +} + +div.highlight .-Color[class*=-C183] { + color: #D7AFFF +} + +div.highlight .-Color[class*=-BGC183] { + background-color: #D7AFFF +} + +div.highlight .-Color[class*=-C184] { + color: #D7D700 +} + +div.highlight .-Color[class*=-BGC184] { + background-color: #D7D700 +} + +div.highlight .-Color[class*=-C185] { + color: #D7D75F +} + +div.highlight .-Color[class*=-BGC185] { + background-color: #D7D75F +} + +div.highlight .-Color[class*=-C186] { + color: #D7D787 +} + +div.highlight .-Color[class*=-BGC186] { + background-color: #D7D787 +} + +div.highlight .-Color[class*=-C187] { + color: #D7D7AF +} + +div.highlight .-Color[class*=-BGC187] { + background-color: #D7D7AF +} + +div.highlight .-Color[class*=-C188] { + color: #D7D7D7 +} + +div.highlight .-Color[class*=-BGC188] { + background-color: #D7D7D7 +} + +div.highlight .-Color[class*=-C189] { + color: #D7D7FF +} + +div.highlight .-Color[class*=-BGC189] { + background-color: #D7D7FF +} + +div.highlight .-Color[class*=-C190] { + color: #D7FF00 +} + +div.highlight .-Color[class*=-BGC190] { + background-color: #D7FF00 +} + +div.highlight .-Color[class*=-C191] { + color: #D7FF5F +} + +div.highlight .-Color[class*=-BGC191] { + background-color: #D7FF5F +} + +div.highlight .-Color[class*=-C192] { + color: #D7FF87 +} + +div.highlight .-Color[class*=-BGC192] { + background-color: #D7FF87 +} + +div.highlight .-Color[class*=-C193] { + color: #D7FFAF +} + +div.highlight .-Color[class*=-BGC193] { + background-color: #D7FFAF +} + +div.highlight .-Color[class*=-C194] { + color: #D7FFD7 +} + +div.highlight .-Color[class*=-BGC194] { + background-color: #D7FFD7 +} + +div.highlight .-Color[class*=-C195] { + color: #D7FFFF +} + +div.highlight .-Color[class*=-BGC195] { + background-color: #D7FFFF +} + +div.highlight .-Color[class*=-C196] { + color: #FF0000 +} + 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.-Color[class*=-C255] { + color: #EEEEEE +} + +div.highlight .-Color[class*=-BGC255] { + background-color: #EEEEEE +} diff --git a/api/cellarr.html b/api/cellarr.html index 2f8cf71..0a5abfc 100644 --- a/api/cellarr.html +++ b/api/cellarr.html @@ -6,9 +6,10 @@ - cellarr package - cellarr 0.0.1 documentation + cellarr package - cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@
-
cellarr 0.0.1 documentation
 +
cellarr 0.0.2 documentation
@@ -203,7 +204,7 @@
- cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
  • Overview
    • +
  • Overview
  • Contributions & Help
  • License
  • Authors
    • @@ -266,91 +267,443 @@

    cellarr package

    Submodules

    -
    -

    cellarr.skeleton module

    -

    This is a skeleton file that can serve as a starting point for a Python -console script. To run this script uncomment the following lines in the -[options.entry_points] section in setup.cfg:

    -
    console_scripts =
-     fibonacci = cellarr.skeleton:run
+
    
+
    cellarr.CellArrDataset module
    
+
    
+
    
+class cellarr.CellArrDataset.CellArrDataset(dataset_path, counts_tdb_uri='counts', gene_metadata_uri='gene_metadata', cell_metadata_uri='cell_metadata')[source]
    
+
    Bases: object
    
+
    A class that represent a collection of cells in TileDB.
    
+
    
+
    
+__del__()[source]
    
+
    
+
+
    
+
    
+__init__(dataset_path, counts_tdb_uri='counts', gene_metadata_uri='gene_metadata', cell_metadata_uri='cell_metadata')[source]
    
+
    Initialize a CellArr dataset.
    
+
    
+
    Parameters:
    
+
      
+
    • counts_tdb_uri (str) – Path to counts TileDB.
      • 
+
    • gene_metadata_uri (str) – Path to gene metadata TileDB.
      • 
+
    • cell_metadata_uri (str) – Path to cell metadata TileDB.
      • 
+
    
+
    
+
    
+
    
+
+
    
+
    
+get_cell_metadata_column(column_name)[source]
    
+
    
+
+
    
+
    
+get_cell_metadata_columns()[source]
    
+
    
+
+
    
+
    
+get_cell_subset(subset, columns=None)[source]
    
+
    
+
+
    
+
    
+get_gene_metadata_column(column_name)[source]
    
+
    
+
+
    
+
    
+get_gene_metadata_columns()[source]
    
+
    
+
+
    
+
    
+get_gene_subset(subset, columns=None)[source]
    
+
    
+
+
    
+
    
+get_slice(cell_subset, gene_subset)[source]
    
+
    
+
+
    
+
+
    
+
    
+
    cellarr.build_cellarrdataset module
    
+
    Build the CellArrDatset
    
+
    The CellArrDataset method is designed to store single-cell RNA-seq
+datasets but can be generalized to store any 2-dimensional experimental data.
    
+
    This method creates three TileDB files in the directory specified by output_path:
+- gene_metadata: A TileDB file containing gene metadata.
+- cell_metadata: A TileDB file containing cell metadata.
+- A matrix TileDB file named as specified by the layer_matrix_name parameter.
    
+
    The TileDB matrix file is stored in a cell X gene orientation. This orientation
+is chosen because the fastest-changing dimension as new files are added to the
+collection is usually the cells rather than genes.
    
+
    ## Process
    
+
    1. Scan the Collection: Scan the entire collection of files to create
+a unique set of gene symbols. Store this gene set as the
+gene_metadata TileDB file.
+2. Store Cell Metadata: Store cell metadata as the
+cell_metadata TileDB file.
+3. Remap and Orient Data: For each dataset in the collection,
+remap and orient the gene dimension using the gene set from Step 1.
+This step ensures consistency in gene measurement and order, even if
+some genes are unmeasured or ordered differently in the original experiments.
    
+
    Example
    
+
    import anndata
+import numpy as np
+import tempfile
+from cellarr import build_cellarrdataset, CellArrDataset
+
+# Create a temporary directory
+tempdir = tempfile.mkdtemp()
+
+# Read AnnData objects
+adata1 = anndata.read_h5ad("path/to/object1.h5ad")
+# or just provide the path
+adata2 = "path/to/object2.h5ad"
+
+# Build CellArrDataset
+dataset = build_cellarrdataset(
+    output_path=tempdir,
+    h5ad_or_adata=[adata1, adata2],
+    matrix_dim_dtype=np.float32
+)
 
    
 
    
-
    Then run pip install . (or pip install -e . for editable mode)
-which will install the command fibonacci inside your current environment.
    
-
    Besides console scripts, the header (i.e. until _logger…) of this file can
-also be used as template for Python modules.
    
-
    
-
    Note
    
-
    This file can be renamed depending on your needs or safely removed if not needed.
    
-
    
-
    References
    
-
      
-
    • https://setuptools.pypa.io/en/latest/userguide/entry_point.html
      • 
-
    • https://pip.pypa.io/en/stable/reference/pip_install
      • 
+
      
+
      
+cellarr.build_cellarrdataset.build_cellarrdataset(files, output_path, num_cells=None, num_genes=None, cell_metadata=None, gene_metadata=None, var_gene_column='index', layer_matrix_name='counts', skip_gene_tiledb=False, skip_cell_tiledb=False, skip_matrix_tiledb=False, cell_dim_dtype=<class 'numpy.uint32'>, gene_dim_dtype=<class 'numpy.uint32'>, matrix_dim_dtype=<class 'numpy.uint32'>, optimize_tiledb=True, num_threads=1)[source]
      
+
      Generate the CellArrDataset.
      
+
      All files are expected to be consistent and any modifications
+to make them consistent is outside the scope of this function
+and package.
      
+
      There’s a few assumptions this process makes:
+- If object in files is an AnnData or H5AD object, these must
+contain an assay matrix in layer names as layer_matrix_name
+parameter.
+- Feature information must contain a column defined by
+var_gene_column that contains gene symbols or a common entity
+across all files.
+-
      
+
      
+
      Parameters:
      
+
        
+
      • files (List[Union[str, AnnData]]) – List of file paths to H5AD or AnnData objects.
+Each object in this list must contain
+- gene symbols as index or the column specified by
+var_gene_column.
+- Must contain a layers with a matrix named as
+layer_matrix_name.
        • 
+
      • output_path (str) – Path to where the output tiledb files should be stored.
        • 
+
      • num_cells (Optional[int]) – 
        Number of cells across all files.
        
+
        Defualts to None, in which case, automatically inferred by
+scanning all objects in h5ad_or_adata.
        
+
        • 
+
      • num_genes (Optional[int]) – 
        Number of genes across all cells.
        
+
        Defualts to None, in which case, automatically inferred by
+scanning all objects in h5ad_or_adata.
        
+
        • 
+
      • cell_metadata (Union[DataFrame, str, None]) – 
        Path to the file containing a concatenated cell metadata across
+all cells. In this case, the first row is expected to contain the
+column names.
        
+
        Alternatively, may also provide a dataframe containing the cell
+metadata across all objects.
        
+
        Regardless of the input type, the number of rows in the file or
+DataFrame must match the num_cells argument.
        
+
        Defaults to None, then a simple range index is created using the
+num_cells argument.
        
+
        • 
+
      • gene_metadata (Union[List[str], dict, str, DataFrame, None]) – 
        Path to the file containing a concatenated gene annotations across
+all datasets. In this case, the first row is
+expected to contain the column names and an index column
+containing the gene symbols to remap the matrix.
        
+
        Alternatively, may also provide a dataframe containing the gene
+annotations across all objects.
        
+
        Alternatively, a list or a dictionary of gene symbols.
        
+
        Regardless of the input type, the number of rows in the file or
+DataFrame must match the num_genes argument.
        
+
        Defaults to None, then a gene set is generated by scanning all
+objects in h5ad_or_adata.
        
+
        • 
+
      • var_gene_column (str) – 
        Column name from var slot that contains the gene symbols.
+Must be consistent across all objects in h5ad_or_adata.
        
+
        Defaults to “index”.
        
+
        • 
+
      • layer_matrix_name (str) – 
        Matrix name from layers slot to add to tiledb.
+Must be consistent across all objects in h5ad_or_adata.
        
+
        Defaults to “counts”.
        
+
        • 
+
      • skip_gene_tiledb (bool) – 
        Whether to skip generating gene metadata tiledb.
        
+
        Defaults to False.
        
+
        • 
+
      • skip_cell_tiledb (bool) – 
        Whether to skip generating cell metadata tiledb.
        
+
        Defaults to False.
        
+
        • 
+
      • skip_matrix_tiledb (bool) – 
        Whether to skip generating matrix tiledb.
        
+
        Defaults to False.
        
+
        • 
+
      • cell_dim_dtype (dtype) – 
        NumPy dtype for the cell dimension.
+Defaults to np.uint32.
        
+
        Note: make sure the number of cells fit
+within the range limits of unsigned-int32.
        
+
        • 
+
      • gene_dim_dtype (dtype) – 
        NumPy dtype for the gene dimension.
+Defaults to np.uint32.
        
+
        Note: make sure the number of genes fit
+within the range limits of unsigned-int32.
        
+
        • 
+
      • matrix_dim_dtype (dtype) – 
        NumPy dtype for the values in the matrix.
+Defaults to np.uint32.
        
+
        Note: make sure the matrix values fit
+within the range limits of unsigned-int32.
        
+
        • 
+
      • num_threads (int) – 
        Number of threads.
        
+
        Defaults to 1.
        
+
        • 
 
      
+
      
+
      
+
      
+
+
      
+
      
+cellarr.build_cellarrdataset.generate_metadata_tiledb_csv(output_uri, input, column_dtype=<class 'str'>, chunksize=1000)[source]
      
+
      Generate a metadata tiledb from csv.
      
+
      The difference between this and generate_metadata_tiledb_frame
+is when the csv is super large and it won’t fit into memory.
      
+
      
+
      Parameters:
      
+
        
+
      • output_uri (str) – TileDB URI or path to save the file.
        • 
+
      • input (str) – Path to the csv file. The first row is expected to
+contain the column names.
        • 
+
      • column_dtype – Dtype of the columns.
+Defaults to str.
        • 
+
      • chunksize – Chunk size to read the dataframe.
+Defaults to 1000.
        • 
+
      
+
      
+
      
+
      
+
 
      
-
      
-cellarr.skeleton.fib(n)[source]
      
-
      Fibonacci example function
      
+
      
+cellarr.build_cellarrdataset.generate_metadata_tiledb_frame(output_uri, input, column_types=None)[source]
      
+
      Generate metadata tiledb from a :pu:class:`~pandas.DataFrame`.
      
 
      
 
      Parameters:
      
-
      n (int) – integer
      
+
        
+
      • output_uri (str) – TileDB URI or path to save the file.
        • 
+
      • input (DataFrame) – Input dataframe.
        • 
+
      • column_types (Optional[dict]) – 
        You can specify type of each column name to cast into.
+“ascii” or str works best for most scenarios.
        
+
        Defaults to None.
        
+
        • 
+
      
+
      
+
      
+
      
+
+
    
+
    
+
    cellarr.dataloader module
    
+
    
+
    
+
    cellarr.utils_anndata module
    
+
    
+
    
+cellarr.utils_anndata.remap_anndata(h5ad_or_adata, gene_set_order, var_gene_column='index', layer_matrix_name='counts')[source]
    
+
    Extract and remap the count matrix to the provided gene set order from the AnnData object.
    
+
    
+
    Parameters:
    
+
      
+
    • adata – 
      Input AnnData object.
      
+
      Alternatively, may also provide a path to the H5ad file.
      
+
      The index of the var slot must contain the gene symbols
+for the columns in the matrix.
      
+
      • 
+
    • gene_set_order (dict) – A dictionary with the symbols as keys and their index as value.
+The gene symbols from the AnnData object are remapped to the
+gene order from this dictionary.
      • 
+
    • var_gene_column (str) – Column in var containing the symbols.
+Defaults to the index of the var slot.
      • 
+
    • layer_matrix_name (str) – Layer containing the matrix to add to tiledb.
+Defaults to “counts”.
      • 
+
    
 
    
-
    Returns:
    
-
    n-th Fibonacci number
    
+
    Return type:
    
+
    csr_matrix
    
 
    
-
    Return type:
    
-
    int
    
+
    Returns:
    
+
    A csr_matrix representation of the assay matrix.
    
 
    
 
    
 
    
 
 
    
-
    
-cellarr.skeleton.main(args)[source]
    
-
    Wrapper allowing fib() to be called with string arguments in a CLI fashion
    
-
    Instead of returning the value from fib(), it prints the result to the
-stdout in a nicely formatted message.
    
+
    
+cellarr.utils_anndata.scan_for_cellcounts(h5ad_or_adata, num_threads=1)[source]
    
+
    Extract cell counts across files.
    
 
    
 
    Parameters:
    
-
    args (List[str]) – command line parameters as list of strings
-(for example  ["--verbose", "42"]).
    
+
      
+
    • h5ad_or_adata (List[Union[str, AnnData]]) – List of anndata objects or path to h5ad files.
      • 
+
    • num_threads (int) – Number of threads to use.
+Defaults to 1.
      • 
+
    
+
    
+
    Return type:
    
+
    List[int]
    
+
    
+
    Returns:
    
+
    List of cell counts across files.
    
+
    
+
    
+
    
+
+
    
+
    
+cellarr.utils_anndata.scan_for_genes(h5ad_or_adata, var_gene_column='index', num_threads=1)[source]
    
+
    Extract and generate the list of unique genes identifiers across files.
    
+
    
+
    Parameters:
    
+
      
+
    • h5ad_or_adata (List[Union[str, AnnData]]) – List of anndata objects or path to h5ad files.
      • 
+
    • var_gene_column (str) – Column containing the gene symbols.
+Defaults to “index”.
      • 
+
    • num_threads (int) – Number of threads to use.
+Defaults to 1.
      • 
+
    
+
    
+
    Return type:
    
+
    List[str]
    
+
    
+
    Returns:
    
+
    List of all unique gene symbols across all files.
    
 
    
 
    
 
    
 
+
    
+
    
+
    cellarr.utils_tiledb_array module
    
 
    
-
    
-cellarr.skeleton.parse_args(args)[source]
    
-
    Parse command line parameters
    
+
    
+cellarr.utils_tiledb_array.create_tiledb_array(tiledb_uri_path, num_cells, num_genes, x_dim_name='cell_index', y_dim_name='gene_index', matrix_attr_name='counts', x_dim_dtype=<class 'numpy.uint32'>, y_dim_dtype=<class 'numpy.uint32'>, matrix_dim_dtype=<class 'numpy.uint32'>, is_sparse=True)[source]
    
+
    Create a tiledb file with the provided attributes to persistent storage.
    
+
    This will materialize the array directory and all
+related schema files.
    
 
    
 
    Parameters:
    
-
    args (List[str]) – command line parameters as list of strings
-(for example  ["--help"]).
    
+
      
+
    • tiledb_uri_path (str) – Path to create the array tiledb file.
      • 
+
    • num_cells (int) – Number of cells (x/fastest-changing dimension).
      • 
+
    • num_genes (int) – Number fo genes (y dimension).
      • 
+
    • x_dim_name (str) – Name for the x-dimension.
+Defaults to “cell_index”.
      • 
+
    • y_dim_name (str) – Name for the y-dimension.
+Defaults to “gene_index”.
      • 
+
    • matrix_attr_name (str) – Name for the attribute in the array.
+Defaults to “counts”.
      • 
+
    • x_dim_dtype (dtype) – NumPy dtype for the x-dimension.
+Defaults to np.uint32.
      • 
+
    • y_dim_dtype (dtype) – NumPy dtype for the y-dimension.
+Defaults to np.uint32.
      • 
+
    • matrix_dim_dtype (dtype) – NumPy dtype for the values in the matrix.
+Defaults to np.uint32.
      • 
+
    • is_sparse (bool) – Whether the matrix is sparse.
+Defaults to True.
      • 
+
    
 
    
-
    Returns:
    
-
    command line parameters namespace
    
+
    
+
    
+
+
    
+
    
+cellarr.utils_tiledb_array.optimize_tiledb_array(tiledb_array_uri, verbose=True)[source]
    
+
    Consolidate tiledb fragments.
    
+
    
+
+
    
+
    
+cellarr.utils_tiledb_array.write_csr_matrix_to_tiledb(tiledb_array_uri, matrix, value_dtype=<class 'numpy.uint32'>, row_offset=0, batch_size=25000)[source]
    
+
    Append and save a csr_matrix to tiledb.
    
+
    
+
    Parameters:
    
+
      
+
    • tiledb_array_uri (Union[str, SparseArray]) – Tiledb array object or path to a tiledb object.
      • 
+
    • matrix (csr_matrix) – Input matrix to write to tiledb, must be a
+csr_matrix matrix.
      • 
+
    • value_dtype (dtype) – NumPy dtype to reformat the matrix values.
+Defaults to uint32.
      • 
+
    • row_offset (int) – Offset row number to append to matrix.
+Defaults to 0.
      • 
+
    • batch_size (int) – Batch size.
+Defaults to 25000.
      • 
+
    
 
    
-
    Return type:
    
-
    argparse.Namespace
    
+
    
+
    
+
+
    
+
    
+
    cellarr.utils_tiledb_frame module
    
+
    
+
    
+cellarr.utils_tiledb_frame.append_to_tiledb_frame(tiledb_uri_path, frame, row_offset=0)[source]
    
+
    Create a tiledb file with the provided attributes to persistent storage.
    
+
    This will materialize the array directory and all
+related schema files.
    
+
    
+
    Parameters:
    
+
      
+
    • tiledb_uri_path (str) – Path to create the metadata tiledb file.
      • 
+
    • frame (DataFrame) – Pandas Dataframe to append to tiledb.
      • 
+
    • row_offset (int) – Row offset to append new rows to.
+Defaults to 0.
      • 
+
    
 
    
 
    
 
    
 
 
    
-
    
-cellarr.skeleton.run()[source]
    
-
    Calls main() passing the CLI arguments extracted from sys.argv
    
-
    This function can be used as entry point to create console scripts with setuptools.
    
+
    
+cellarr.utils_tiledb_frame.create_tiledb_frame_from_column_names(tiledb_uri_path, column_names, column_dtype=<class 'str'>)[source]
    
+
    Create a tiledb file with the provided attributes to persistent storage.
    
+
    This will materialize the array directory and all
+related schema files.
    
+
    
+
    Parameters:
    
+
      
+
    • tiledb_uri_path (str) – Path to create the metadata tiledb file.
      • 
+
    • column_names (List[str]) – Column names of the data frame.
      • 
+
    • column_dtype – Type for the columns, usually str.
+Defaults to string.
      • 
+
    
+
    
+
    
 
    
 
 
    
-
    
-cellarr.skeleton.setup_logging(loglevel)[source]
    
-
    Setup basic logging
    
+
    
+cellarr.utils_tiledb_frame.create_tiledb_frame_from_dataframe(tiledb_uri_path, frame, column_types=<class 'dict'>)[source]
    
+
    Create a tiledb file with the provided attributes to persistent storage.
    
+
    This will materialize the array directory and all
+related schema files.
    
 
    
 
    Parameters:
    
-
    loglevel (int) – minimum loglevel for emitting messages
    
+
      
+
    • tiledb_uri_path (str) – Path to create the metadata tiledb file.
      • 
+
    • column_names – Column names of the data frame.
      • 
+
    • column_types – Dictionary specifying the column types for each
+column in the frame.
      • 
+
    
 
    
 
    
 
    
@@ -410,12 +763,44 @@ 
    Submodulescellarr package
    diff --git a/api/modules.html b/api/modules.html index 9ce1169..9fa0c17 100644 --- a/api/modules.html +++ b/api/modules.html @@ -6,9 +6,10 @@ - cellarr - cellarr 0.0.1 documentation + cellarr - cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
    +
    diff --git a/authors.html b/authors.html index 63a5a2c..95f119c 100644 --- a/authors.html +++ b/authors.html @@ -6,9 +6,10 @@ - Contributors - cellarr 0.0.1 documentation + Contributors - cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    diff --git a/changelog.html b/changelog.html index febd8b4..902ce18 100644 --- a/changelog.html +++ b/changelog.html @@ -6,9 +6,10 @@ - Changelog - cellarr 0.0.1 documentation + Changelog - cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    diff --git a/contributing.html b/contributing.html index 853807d..3b55ef0 100644 --- a/contributing.html +++ b/contributing.html @@ -3,12 +3,13 @@ - + - Contributing - cellarr 0.0.1 documentation + Contributing - cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    - +
    Previous
    -
    cellarr
    +
    Cell Arrays
     @@ -582,7 +583,7 @@

    Releases

    -
    +
    diff --git a/genindex.html b/genindex.html index 729ef55..03e6f23 100644 --- a/genindex.html +++ b/genindex.html @@ -4,9 +4,10 @@ - Index - cellarr 0.0.1 documentation + Index - cellarr 0.0.2 documentation + @@ -177,7 +178,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -201,7 +202,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
    +
    diff --git a/index.html b/index.html index a2ae5ba..b67658c 100644 --- a/index.html +++ b/index.html @@ -3,12 +3,13 @@ - + - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
    +
    diff --git a/objects.inv b/objects.inv index 08727599dab89867c5438292c8a31e7965cdde1a..c2183d35578f3e99c0b34f9de26852152e3a00b6 100644 GIT binary patch delta 526 zcmV+p0`dKj1Na1xKLIk4KstZ5mCJ71Fc3xe`U(Q=)@r-WE=V^88X!PFfEbZunJ6Cu zA8p*PFDZ%%Wvi$y%NyIeoO@<8L(&YT1Z&A~d{Bl5iR3eZL40CaZx3F{4|!$xk?fkH zKgI7yYrldAhyF(U0*y&TS|`SB?$)A$p=MoL{~Y5OXu3Cc z>=!dZd$$2Bb$bP88cBZ?;JC6qHKJt})5uS;>x*j5;Dr)@Hf8>{A{NPJ`P4djwK#2FboyTS=$2}PxcSaX{R2p^w-J^dreI5QLPKNT>uwoT@ zZm9H=BvEPN2^N~;>u8bCp-iTtX2ZAivw07j$W=snzx2PQ9oA8kl;ghVhbA^>A?tA5 zm4zJsoOgB3U#ickG%ep=I_&_ Q1jw3Ov~%)b3j6{w8FbVG9RL6T delta 292 zcmV+<0o(rg1ds!eKLIh3KstY&lFLrRFc3xe{t8R5O@VE0&x{`ts5N-c-MpXdkTmjV4o*D@;=3_U`hhClBkx$@;fxxiHJioV zN%f0-N=8JL8@=4>cfARe*(5sf0gvEi*rfZkZEV1p+)-O8m68AHP(gnl;;E{cX8^&%BY!rSu6eq(FrDpAg-l|U1)POXS?@3dg~a_&rZb5{x!|DeB| q=2z7}w8NaQ)U>uo7^WCzV?^5h2Lvz1wmmV*eNz3fy!|(<`dKj~Fprb~ diff --git a/py-modindex.html b/py-modindex.html index 7adf1ef..0590c80 100644 --- a/py-modindex.html +++ b/py-modindex.html @@ -4,9 +4,10 @@ - Python Module Index - cellarr 0.0.1 documentation + Python Module Index - cellarr 0.0.2 documentation + @@ -177,7 +178,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -201,7 +202,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
    +
    diff --git a/readme.html b/readme.html index 7a8bf5c..43365d9 100644 --- a/readme.html +++ b/readme.html @@ -3,12 +3,13 @@ - + - cellarr - cellarr 0.0.1 documentation + Cell Arrays - cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@
    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
      -
    • Overview
      • +
        +
      • Overview
      • Contributions & Help
      • License
      • Authors
        • @@ -272,14 +273,71 @@ [![Monthly Downloads](https://pepy.tech/badge/cellarr/month)](https://pepy.tech/project/cellarr) [![Twitter](https://img.shields.io/twitter/url/http/shields.io.svg?style=social&label=Twitter)](https://twitter.com/cellarr) --> -

        Project generated with PyScaffold

        -
        -

        cellarr

        -
        -

        Add a short description here!

        -
        -

        A longer description of your project goes here…

        +

        PyPI-Server +Unit tests

        +
        +

        Cell Arrays

        +

        Cell Arrays is a Python package that provides a TileDB-backed store for large collections of genomic experimental data, such as millions of cells across multiple single-cell experiment objects.

        +

        The CellArrDataset is designed to store single-cell RNA-seq +datasets but can be generalized to store any 2-dimensional experimental data.

        +
        +

        Install

        +

        To get started, install the package from PyPI

        +
        pip install cellarr
+
        +
        +
        +
        +

        Usage

        +
        +

        Create a CellArrDataset

        +

        Creating a CellArrDataset generates three TileDB files in the specified output directory:

        +
          +

        • gene_metadata: Contains feature annotations.

          • +

        • cell_metadata: Contains cell or sample metadata.

          • +

        • matrix: A TileDB-backed sparse array containing expression vectors.

          • +
        +

        The TileDB matrix file is stored in a cell X gene orientation. This orientation +is chosen because the fastest-changing dimension as new files are added to the +collection is usually the cells rather than genes.

        +

        Note: Currently only supports either paths to H5AD or AnnData objects

        +

        To build a CellArrDataset from a collection of H5AD or AnnData objects:

        +
        import anndata
+import numpy as np
+import tempfile
+from cellarr import build_cellarrdataset, CellArrDataset
+
+# Create a temporary directory
+tempdir = tempfile.mkdtemp()
+
+# Read AnnData objects
+adata1 = anndata.read_h5ad("path/to/object1.h5ad")
+# or just provide the path
+adata2 = "path/to/object2.h5ad"
+
+# Build CellArrDataset
+dataset = build_cellarrdataset(
+     output_path=tempdir,
+     h5ad_or_adata=[adata1, adata2],
+     matrix_dim_dtype=np.float32
+)
+
        +
        +
        +
        +

        TODO: This following section does not work yet.

        +

        Users have the option to reuse the dataset object retuned when building the dataset or by creating a CellArrDataset object by initializng it to the path where the files were created.

        +
        # Create a CellArrDataset object from the existing dataset
+dataset = CellArrDataset(dataset_path=tempdir)
+
+# Query data from the dataset
+expression_data = dataset[10, ["gene1", "gene10", "gene500"]]
+
        +
        +
        +
        +

        Note

        This project has been set up using PyScaffold 4.5. For details and usage @@ -292,26 +350,8 @@

        Note
        - -
        -
        - Next -
        -
        Contributing
        -
        - -         - - -
        -
        - Previous -
        - -
        Home
        - -
        -         + +
        @@ -342,7 +382,15 @@

        Note
        -

        +
        diff --git a/search.html b/search.html index 9164814..634eb8d 100644 --- a/search.html +++ b/search.html @@ -7,8 +7,9 @@ -Search - cellarr 0.0.1 documentation +Search - cellarr 0.0.2 documentation + @@ -179,7 +180,7 @@

    -
    cellarr 0.0.1 documentation
     +
    cellarr 0.0.2 documentation
    @@ -203,7 +204,7 @@
    - cellarr 0.0.1 documentation + cellarr 0.0.2 documentation
    -
    +
    diff --git a/searchindex.js b/searchindex.js index b5d6e8b..217138d 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"Changelog": [[3, "changelog"]], "Clone the repository": [[4, "clone-the-repository"]], "Code Contributions": [[4, "code-contributions"]], "Contents": [[5, "contents"]], "Contributing": [[4, "contributing"]], "Contributors": [[2, "contributors"]], "Create an environment": [[4, "create-an-environment"]], "Documentation Improvements": [[4, "documentation-improvements"]], "Implement your changes": [[4, "implement-your-changes"]], "Indices and tables": [[5, "indices-and-tables"]], "Issue Reports": [[4, "issue-reports"]], "License": [[6, "license"]], "Maintainer tasks": [[4, "maintainer-tasks"]], "Module contents": [[0, "module-cellarr"]], "Note": [[5, "note"], [7, "note"]], "Releases": [[4, "releases"]], "Submit an issue": [[4, "submit-an-issue"]], "Submit your contribution": [[4, "submit-your-contribution"]], "Submodules": 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"tabl": 5, "task": 4, "thi": [7, 8], "todo": [4, 7, 8], "troubleshoot": 4, "usag": [7, 8], "utils_anndata": 0, "utils_tiledb_arrai": 0, "utils_tiledb_fram": 0, "version": 3, "work": [7, 8], "yet": [7, 8], "your": 4}}) \ No newline at end of file diff --git a/tutorial.html b/tutorial.html new file mode 100644 index 0000000..57f869a --- /dev/null +++ b/tutorial.html @@ -0,0 +1,396 @@ + + + + + + + + + Cell Arrays - cellarr 0.0.2 documentation + + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark mode + + + + + + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    cellarr 0.0.2 documentation
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    Cell Arrays

    +

    Cell Arrays is a Python package that provides a TileDB-backed store for large collections of genomic experimental data, such as millions of cells across multiple single-cell experiment objects.

    +
    +

    Usage

    +
    +

    Create the CellArrDataset

    +

    Creating a CellArrDataset generates three TileDB files in the specified output directory:

    +
      +

    • gene_metadata: Contains feature annotations.

      • +

    • cell_metadata: Contains cell or sample metadata.

      • +

    • matrix: A TileDB-backed sparse array containing expression vectors.

      • +
    +

    Note: Currently only supports either paths to H5AD or AnnData objects

    +

    To build a CellArrDataset from a collection of H5AD or AnnData objects:

    +
    import anndata
+import numpy as np
+import tempfile
+from cellarr import build_cellarrdataset, CellArrDataset
+
+# Create a temporary directory
+tempdir = tempfile.mkdtemp()
+
+# Read AnnData objects
+adata1 = anndata.read_h5ad("path/to/object1.h5ad")
+# or just provide the path
+adata2 = "path/to/object2.h5ad"
+
+# Build CellArrDataset
+dataset = build_cellarrdataset(
+     output_path=tempdir,
+     h5ad_or_adata=[adata1, adata2],
+     matrix_dim_dtype=np.float32
+)
+
    +
    +
    +
    +

    TODO: This following section does not work yet.

    +

    Users have the option to reuse the dataset object retuned when building the dataset or by creating a CellArrDataset object by initializng it to the path where the files were created.

    +
    # Create a CellArrDataset object from the existing dataset
+dataset = CellArrDataset(dataset_path=tempdir)
+
+# Query data from the dataset
+expression_data = dataset[10, ["gene1", "gene10", "gene500"]]
+
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