LIANA+:单细胞与空间多组学数据细胞间通讯推断一体化方案
文件数 6 个,合计 13.59 GB。本数据集为肿瘤领域公开数据集,主题涵盖single-cell。本数据集为肿瘤领域的图像公开数据集,由figshare提供,主题涵盖single-cell。

基本信息
资源简介
LIANA+:单细胞与空间多组学数据细胞间通讯推断一体化方案
LIANA+ provides an all-in-one solution for cell-cell communication inference from single-cell and spatial (multi-)omics data
该数据集来自 doi.org,归类为肿瘤 / 乳腺癌,由 Daniel Dimitrov 于 2024-09-03 发布,DOI 为 10.6084/m9.figshare.26131789.v1,共 6 个文件,样例包括 reliability.tar.xz、wu_et_al.tar.xz、sma.tar.xz、slidetags.tar.xz、heart_visium.tar.xz、classification.tar.xz。源站描述显示其包含 Kuppe et al. (2022) 空间与单细胞梗死数据、Reichart et al. (2022) 心肌病数据、Carraro et al. (2021) 囊性纤维化数据、Hab。
【数据集背景】
配套论文《LIANA+ provides an all-in-one framework for cell–cell communication inference》发表于 Nature Cell Biology(2024),DOI:10.1038/s41556-024-01469-w。源站首次发布:2024-09-03。
【数据结构与技术规格】
文件数 6 个,合计 13.59 GB。数据集 DOI:10.6084/m9.figshare.26131789.v1。
【主题与分类】
源站学科分类:Computational Biology。主题标签:single-cell、Spatial analysis、cell-cell communication、multi-omics analysis。本站分类:肿瘤/乳腺癌。
【适用方向】
源站学科分类标注为 Computational Biology。
【获取与许可】
源站页面:https://springernature.figshare.com/articles/dataset/LIANA_provides_an_all-in-one_solution_for_cell-cell_communication_inference_from_single-cell_and_spatial_multi-_omics_data/26131789
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Dimitrov, Daniel (2024). LIANA+ provides an all-in-one solution for cell-cell communication inference from single-cell and spatial (multi-)omics data. figshare. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
源站:https://doi.org/10.6084/m9.figshare.26131789
标准获取流程:
- 访问源站链接,注册/登录账号;
- 进入数据集详情页,按页面指引申请权限或直接下载;
- 部分平台提供 SDK,例如:
- OpenDataLab:
pip install openxlab后openxlab.dataset.get(...) - ModelScope:
pip install modelscope后modelscope download - 天池: 登录后页面提供直链下载
以源站「数据下载/使用文档」页面为准。
- OpenDataLab:
数据集大小约 13.59 GB。
数据说明
Processed Datasets Kuppe et al. (2022): Spatial and single-cell infarction data, obtained via link (Reference: https://www.nature.com/articles/s41586-022-05060-x) Reichart et al. (2022): Cardiomyopathies, obtained via link (Reference: https://www.science.org/doi/10.1126/science.abo1984) Carraro et al. (2021): Cystic fibrosis data, obtained via link (Reference: https://www.nature.com/articles/s41591-021-01332-7) Habermann et al. (2020): Pulmonary Fibrosis data, obtained via link (Reference: https://pubmed.ncbi.nlm.nih.gov/32832598/) Velmeshev et al. (2019): Single-cell Autism Spectrum Disorder data, obtained via link and link (Reference: https://www.science.org/doi/full/10.1126/science.aav8130) Wu et al. (2021): Processed breast cancer 10× Visium slides, available at link (Reference: https://www.nature.com/articles/s41588-021-00911-1) Vicari et al. (2023): Parkinson’s disease data, obtained via link (Reference: https://www.nature.com/articles/s41587-023-01937-y) Russell et al. (2023): Slide-Seq datasets obtained via the Broad Institute Single Cell Portal: Mouse embryonic brain: SCP2170 Mouse brain: SCP2162 Human brain: SCP2167 Human tonsil: SCP2169, SCP2171 Human melanoma multiome: SCP2176 Also available under GEO GSE244355 (Reference: https://www.nature.com/articles/s41586-023-06837-4) Kuppe et al. (2022): Acute cardiac infarction, 23 samples, obtained via link (Reference: https://www.nature.com/articles/s41586-022-05060-x) Reichart et al. (2022): Cardiomyopathies, 126 samples, obtained via link (Reference: https://www.science.org/doi/10.1126/science.abo1984) Simonson et al. (2023): Ischemic cardiomyopathy, 15 samples, obtained via link (Reference: https://www.sciencedirect.com/science/article/pii/S2211124723000979?via=ihub) Koenig et al. (2022): Dilated (nonischemic) cardiomyopathy, 38 samples, obtained via link (Reference: https://www.nature.com/articles/s44161-022-00028-6) Chaffin et al. (2022): Dilated and hypertrophic cardiomyopathy, 42 samples, obtained via li
数据加载示例(图像类)
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
需要用于商业化训练或医疗器械注册(NMPA/FDA)?
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