JWGray 乳腺癌细胞系面板

数据集获取

源站：https://synapse.org/Synapse:syn2346643

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

JWGray Breast Cancer Cell Line Panel ${image?fileName=circos%5FHCC1954%5Fall%2Epng&align=left&scale=20} ### Overview The data on this wiki represent one of the most comprehensive sets of profiling data available on a collection of cell lines. The data have been published across several manuscripts, but have been curated here into a single resource for the broader scientific community. Should you chose to use these data in your publications, please cite the following manuscripts: Heiser, PNAS, 2012 Daemen, Genome Biology, 2013 ### Datafiles Cell Line Classifications: Cell line classifications determined by analysis of RNA sequencing data Cell Line Classifications PNAS Exon Data: Affymetrix exon array data on breast cancer cell lines. Data were described and published in conjunction with Heiser, et al, PNAS (2012) PNAS Exon Data RPPA Data: Reverse protein lysate array data generated by the Gordon Mills lab RPPA Data SNP6 Copy Number Data: DNA copy number assessed by the Joe Gray Lab using the Affymetrix Genome-Wide Human SNP Array 6.0 analysis platform SNP6 segmented cbs data SNP6 segmented gene level data SNP6 segmented table SNP6 segmented table imputed PNAS SNP6 Copy Number Data: DNA copy number assessed by the Joe Gray Lab using the Affymetrix Genome-Wide Human SNP Array 6.0 analysis platform. Data were described and published in conjunction with Heiser, et al, PNAS (2012) SNP6 segmented PNAS cbs data Western Blot Data: Protein abundances meas

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。