cellarr 0.0.2

TileDB-based array storage for genomics data collections.

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 information on PyScaffold see https://pyscaffold.org/.