| --- |
| license: other |
| |
| 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. |
|
|
