README.md

metadata

license: other
tags:
  - single cell
  - ST&SE
language:
  - en
  - zh

State Dataset

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:

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

Read an H5AD file:

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:

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:

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.