非洲绿猴肺部SARS-CoV-2感染动态

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE156755

标准获取流程：

1. 访问源站链接，注册/登录账号；
2. 进入数据集详情页，按页面指引申请权限或直接下载；
3. 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。

数据说明

Detailed knowledge about dynamics of SARS-CoV-2 infection in vivo is important for unraveling the viral and host response factors that contribute to COVID-19 pathogenesis. The unknown dose and exposure timing in human infections makes the needed well-controlled time course studies impossible and thus animal models of disease are essential to fill in the gaps in our understanding of disease progression. Old-World nonhuman primate species recapitulate mild COVID-19 cases, thereby serving as important models for studying disease pathogenesis. In this study, we compare African green monkeys (AGM; Chlorocebus sabaeus) inoculated with SARS-CoV-2 to AGM inoculated with a gamma-irradiated form of the virus to study the dynamics of virus replication throughout the respiratory tract and other target tissues. RNA sequencing of single cells from the lungs and mediastinal lymph nodes allowed a high-resolution, simultaneous analysis of virus replication and the host response in these tissues over time. Viral replication was mainly localized to the lower respiratory tract, especially the pneumocyte population. However, even in the absence of active replication, viral genomic RNA is was highly stable, especially in the upper respiratory tract. Macrophages play a vital and dynamic role in initiating a pro-inflammatory state in the lungs, while also interacting with infected pneumocytes. Together, our dataset provides a detailed view of changes in host and virus replication dynamics over the course of a mild COVID-19 infection and serves as a valuable resource to identify new therapeutic targets.

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。