SARS-CoV-2肺部感染宿主反应的时间与空间异质性 [GeoMx human protein]

数据集获取

源站：https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159785

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

The relationship of SARS-CoV-2 lung infection and severity of pulmonary disease is not fully understood. We analyzed specimens from 24 autopsies from patients who succumbed to SARS-CoV-2 infection using a combination of different RNA and protein analytical platforms to characterize inter- and intra- patient heterogeneity of pulmonary virus infection. There was a spectrum of high and low virus cases that was associated with duration of disease and activation of interferon pathway genes. Using a digital spatial profiling platform, the intra-pulmonary spatial heterogeneity of the virus corresponded to focal expression of interferon response genes and association with immune checkpoint genes In this dataset, we capture the GeoMx DSP protein profiling work done in conjunction with this study. The files here represent SARS-CoV-2 infected rapid autopsy patients (n = 6) which were profiled with the following nanoString GeoMx protein panels: Immune Cell Profiling Core v1.0, IO Drug Target Module v1.0, Immune Cell Typing Module v1.0, Immune Activation Status Module v1.0, PI3K/AKT Signaling Module v1.0, MAPK Signaling Module v0.9, and Cell Death Module v0.9. These panels were then read out using Nanostring nCounter quantification following standard HybCode panel profiling protocols. For access to high resolution (single channel, composite, or ROI) images please contact the corresponding author or GeoMxSupport@nanostring.com. RNA sequencing data and results for bulk RNA profiling can be accessed in study GSE150316.

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。