cellarr 0.6.1
TileDB-based array storage for genomics data collections.
License: MIT
Requires Python: >=3.9

[![PyPI-Server](https://img.shields.io/pypi/v/cellarr.svg)](https://pypi.org/project/cellarr/)
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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.

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

> [!NOTE]
>
> Check out the tutorial using cellXgene datasets [here](https://cellarr.github.io/cellarr/tutorial_cellxgene.html).


## Install

To get started, install the package from [PyPI](https://pypi.org/project/cellarr/)

```sh
pip install cellarr

## to include optional dependencies
pip install cellarr[optional]
```

## Usage

### Build a `CellArrDataset`

Building a `CellArrDataset` generates 4 TileDB files in the specified output directory:

- `gene_annotation`: A TileDB file containing feature/gene annotations.
- `sample_metadata`: A TileDB file containing sample metadata.
- `cell_metadata`: A TileDB file containing cell metadata including mapping to the samples
  they are tagged with in `sample_metadata`.
- An `assay` TileDB group containing various matrices. This allows the package to store multiple different matrices, e.g. 'counts', 'normalized', 'scaled' for the same sample/cell and gene attributes.

The organization is inspired by Bioconductor's `SummarizedExperiment` data structure.

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.

![`CellArrDataset` structure](./assets/cellarr.png "CellArrDataset")

**_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, MatrixOptions

# Create a temporary directory, this is where the
# output files are created. Pick your location here.
tempdir = tempfile.mkdtemp()

# Read AnnData objects
adata1 = anndata.read_h5ad("path/to/object1.h5ad", "r")
# or just provide the path
adata2 = "path/to/object2.h5ad"

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

# Or if the objects contain multiple assays
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,
)
```

The build process usually involves 4 steps:

1. **Scan the Collection**: Scan the entire collection of files to create
   a unique set of feature ids (e.g. gene symbols). Store this set as the
   `gene_annotation` TileDB file.

2. **Sample Metadata**: Store sample metadata in `sample_metadata`
   TileDB file. Each file is typically considered a sample, and an automatic
   mapping is created between files and samples if metadata is not provided.

3. **Store Cell Metadata**: Store cell metadata in the `cell_metadata`
   TileDB file.

4. **Remap and Orient Data**: For each dataset in the collection,
   remap and orient the feature dimension using the feature 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.

**_Note: The objects to build the `CellArrDataset` are expected to be fairly consistent, especially along the feature dimension.
if these are `AnnData` or `H5AD`objects, all objects must contain an index (in the `var` slot) specifying the gene symbols._**

#### Optionally provide cell metadata columns

If the cell metadata is inconsistent across datasets, you may provide a list of
columns to standardize during extraction. Any missing columns will be filled with
the default value `'NA'`, and their data type should be specified as `'ascii'` in
`CellMetadataOptions`. For example, this build process will create a TileDB store
for cell metadata containing the columns `'cellids'` and `'tissue'`. If any dataset
lacks one of these columns, the missing values will be automatically filled with `'NA'`.

```python
dataset = build_cellarrdataset(
    output_path=tempdir,
    files=[adata1, adata2],
    matrix_options=MatrixOptions(dtype=np.float32),
    cell_metadata_options=CellMetadataOptions(
        column_types={"cellids": "ascii", "tissue": "ascii"}
    ),
)

print(dataset)
```

Check out the [documentation](https://cellarr.github.io/cellarr/tutorial.html) for more details.

### Building on HPC environments with `slurm`

To simplify building TileDB files on HPC environments that use `slurm`, there are a few steps you need to follow.

- Step 1: Construct a manifest file
  A minimal manifest file (json) must contain the following fields
- `"files"`: A list of file path to the input `h5ad` objects.
- `"python_env"`: A set of commands to activate the Python environment containing this package and its dependencies.

Here’s an example of the manifest file:

```py
manifest = {
    "files": your/list/of/files,
    "python_env": """
ml Miniforge3
conda activate cellarr

python --version
which python
    """,
    "matrix_options": [
        {
            "matrix_name": "non_zero_cells",
            "dtype": "uint32"
        },
        {
            "matrix_name": "pseudo_bulk_log_normed",
            "dtype": "float32"
        }
    ],
}

import json
json.dump(manifest, open("your/path/to/manifest.json", "w"))
```

For more options, check out the [README](./src/cellarr/slurm/README.md).

- Step 2: Submit the job
  Once your manifest file is ready, you can submit the necessary jobs using the `cellarr_build` CLI. Run the following command:

```sh
cellarr_build --input-manifest your/path/to/manifest.json --output-dir your/path/to/output --memory-per-job 8 --cpus-per-task 2
```

### Query a `CellArrDataset`

Users have the option to reuse the `dataset` object returned when building the dataset or by creating a `CellArrDataset` object by initializing 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
gene_list = ["gene_1", "gene_95", "gene_50"]
expression_data = dataset[0:10, gene_list]

print(expression_data.matrix)

print(expression_data.gene_annotation)
```

     ## output 1
     <11x3 sparse matrix of type '<class 'numpy.float32'>'
          with 9 stored elements in COOrdinate format>

     ## output 2
     	cellarr_gene_index
     0	gene_1
     446	gene_50
     945	gene_95

This returns a `CellArrDatasetSlice` object that contains the matrix and metadata `DataFrame`'s along the cell and gene axes.

Users can easily convert these to analysis-ready representations

```python
print("as anndata:")
print(expression_data.to_anndata())

print("\n\n as summarizedexperiment:")
print(expression_data.to_summarizedexperiment())
```

### A built-in dataloader for the `pytorch-lightning` framework

The package includes a dataloader in the `pytorch-lightning` framework for single cells expression profiles, training labels, and study labels. The dataloader uniformly samples across training labels and study labels to create a diverse batch of cells.

This dataloader can be used as a **template** to create custom dataloaders specific to your needs.

```python
from cellarr.ml.dataloader import DataModule

datamodule = DataModule(
    dataset_path="/path/to/cellar/dir",
    cell_metadata_uri="cell_metadata",
    gene_annotation_uri="gene_annotation",
    matrix_uri="assays/counts",
    label_column_name="label",
    study_column_name="study",
    batch_size=1000,
    lognorm=True,
    target_sum=1e4,
)
```

The package also includes a simple autoencoder in the `pytorch-lightning` which makes use of the dataloader. This can be used as a template to create custom architectures and models.

```python
import pytorch_lightning as pl
from cellarr.ml.autoencoder import AutoEncoder

autoencoder = AutoEncoder(
    n_genes=len(datamodule.gene_indices),
    latent_dim=128,
    hidden_dim=[1024, 1024, 1024],
    dropout=0.5,
    input_dropout=0.4,
    residual=False,
)

model_path = "/path/to/model/mymodel/"
params = {
    "max_epochs": 500,
    "logger": True,
    "log_every_n_steps": 1,
    "limit_train_batches": 100, # to specify number of batches per epoch
}
trainer = pl.Trainer(**params)
trainer.fit(autoencoder, datamodule=datamodule)
autoencoder.save_all(model_path=model_path)
```

Check out the [documentation](https://cellarr.github.io/cellarr/api/modules.html) for more details.

<!-- pyscaffold-notes -->

## Note

This project has been set up using PyScaffold 4.5. For details and usage
information on PyScaffold see <https://pyscaffold.org/>.