Cellarr

- Support csc matrices in layers, although not common ran into a situation where the
anndata object was stored from R.

- Construct cellarr TileDB files on HPC environments based on slurm
(reference: [61](https://github.com/BiocPy/cellarr/pull/61))

- chore: Remove Python 3.8 (EOL).
- precommit: Replace docformatter with ruff's formatter.

- Functionality to iterate over samples and cells.
- Explicitly mention that slicing defaults to TileB's behavior, inclusive of upper bounds.

This version introduces major improvements to matrix handling, storage, and performance, including support for multiple matrices in H5AD/AnnData workflows and optimizations for ingestion and querying.

**Support for multiple matrices**:
- Both `build_cellarrdataset` and `CellArrDataset` now support multiple matrices. During ingestion, a TileDB group called `"assays"` is created to store all matrices, along with group-level metadata.

This may introduce breaking changes with the default parameters based on how these classes are used. Previously to build the TileDB files:

python
dataset = build_cellarrdataset(
output_path=tempdir,
files=[adata1, adata2],
matrix_options=MatrixOptions(matrix_name="counts", dtype=np.int16),
num_threads=2,
)


Now you may provide a list of matrix options for each layers in the files.

python
dataset = build_cellarrdataset(
output_path=tempdir,
files=[adata1, adata2],
matrix_options=[
MatrixOptions(matrix_name="counts", dtype=np.int16),
MatrixOptions(matrix_name="log-norm", dtype=np.float32),
],
num_threads=2,
)


Querying follows a similar structure:
python
cd = CellArrDataset(
dataset_path=tempdir,
assay_tiledb_group="assays",
assay_uri=["counts", "log-norm"]
)

`assay_uri` is relative to `assay_tiledb_group`. For backwards compatibility, `assay_tiledb_group` can be an empty string.

**Parallelized ingestion**:
The build process now uses `num_threads` to ingest matrices concurrently. Two new columns in the sample metadata, `cellarr_sample_start_index` and `cellarr_sample_end_index`, track sample offsets, improving matrix processing.
- Note: The process pool uses the `spawn` method on UNIX systems, which may affect usage on windows machines.

**TileDB query condition fixes**:
Fixed a few issues with fill values represented as bytes (seems to be common when ascii is used as the column type) and in general filtering operations on TileDB Dataframes.

**Index remapping**:
Improved remapping of indices from sliced TileDB arrays for both dense and sparse matrices. This is not a user facing function but an internal slicing operation.

**Get a sample**:
Added a method to access all cells for a particular sample. you can either provide an index or a sample id.

python
sample_1_slice = cd.get_cells_for_sample(0)


Other updates to documentation, tutorials, the README, and additional tests.

- Provide options to extract an expected set of cell metadata columns across datasets.
- Update documentation and tests.