README.md

---
license: other
#User-Defined Tags
tags:
  - single cell
  - ST&SE
language:
  - en
  - zh
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">State Dataset</span>
  </strong>
</p>

## Dataset Description

`State_dataset` is a collection of datasets used for State single-cell expression modeling and perturbation prediction tasks. It comprises four data categories: Parse, Tahoe, Replogle-Nadig, and SE-167M-Human. The primary data is in AnnData/H5AD format, accompanied by gene embeddings (PyTorch `.pt`), dataset split configurations (TOML), and upstream license files.

## Supported Tasks

This repository corresponds to the experiment configurations in the `State` directory:
`ST-HVG-Parse` and `ST-SE-Parse` use Parse data for few-shot/zero-shot splits by cell type or donor; `ST-HVG-Tahoe` uses Tahoe data for generalization evaluation; Replogle data is used for perturbation validation; and `SE-600M/config.yaml` describes the organization of large-scale cellxgene/Tahoe training data and gene embeddings.

## Data Format and Structure

The following sizes are based on file statistics from the current directory. File sizes may vary between data versions:

| Subset | Main Files | Current File Size |
|---|---|---:|
| Parse | `parse_concat_full.h5ad` | Approximately 342.3 GiB |
| Replogle-Nadig | 5 `.h5ad` files | Approximately 49.3 GiB |
| Tahoe smoke | `c36.h5ad`, `c39.h5ad`, `c44.h5ad` | Approximately 5.0 GiB |
| SE-167M-Human smoke | 1 `.pt` file + 4 `.h5ad` files | Approximately 607 MiB |

H5AD files can be read with `scanpy`/`anndata`, while PT files can be read with PyTorch. The data paths in the configuration files are examples for the runtime environment. After migrating the data to a local environment, update the paths in the `State` configurations to the actual mount paths.

## How to Use the Dataset

After mounting this directory in the runtime environment, update the data path in the corresponding TOML file to the actual path. For example:

```toml
[datasets]
parse = "/path/to/State_dataset/State-Parse-Filtered"
```

Read an H5AD file:

```python
import anndata as ad

adata = ad.read_h5ad("State-Parse-Filtered/parse_concat_full.h5ad", backed="r")
print(adata)
```

### Sharded Archives

Because the complete directory is approximately 401 GiB, it has been split into multiple Zstandard-compressed shards of 90 GiB (binary) each. The shards are consecutive parts of the same compressed stream and cannot be decompressed independently; they must first be concatenated in order:

```bash
cat State_dataset.tar.zst.part-* > State_dataset.tar.zst
zstd -d State_dataset.tar.zst -c | tar -xf -
```

Alternatively, stream the decompression directly without materializing the merged file:

```bash
cat State_dataset.tar.zst.part-* | zstd -d -c | tar -xf -
```

For shard filenames, actual sizes, and SHA256 checksums, refer to `State_dataset.tar.zst.sha256`, which was generated in the same directory.

## Official OneScience Information

| Platform | OneScience Main Repository | Skills Repository |
|---|---|---|
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |

## Citation and License

- Parse data source: Parse Biosciences, “Performance of Evercode WT v3 in Human Immune Cells (PBMCs)”; see `State-Parse-Filtered/README.md` and `CC-NC-4.0-License.txt`.
- For Replogle-Nadig, Tahoe, and SE-167M-Human data, comply with the licenses, citation requirements, and usage restrictions of the respective upstream datasets.
- This README only describes the current directory structure and does not alter the copyright or license terms of any upstream data.