队列样本特征数据集
基于泛癌基因组数据的队列样本特征,用于分层分析癌症驱动通路在不同人群和肿瘤类型中的模式。

基本信息
资源简介
队列样本特征数据集
【数据集背景】
本数据集源站标题为《Sample characteristics of cohorts.》。源站首次发布:2025-08-06。
【数据内容】
源站原始描述:Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chi…
【数据结构与技术规格】
文件数 1 个,合计 19.23 KB。pcbi.1013349.s005.xlsx:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet,19.23 KB。数据集 DOI:10.1371/journal.pcbi.1013349.s005。
【主题与分类】
源站学科分类:Microbiology、Cell Biology、Genetics、Molecular Biology、Biotechnology、Biological Sciences not elsewhere classified。主题标签:xlink "> discovery、subtype comparisons highlight、perform stratified analyses、adult comparisons emphasize、cancer genomic data、risk factor associations、help prioritize context、cancer driver pathways、bladder cancer、risk factors。
【适用方向】
组学数据适用于生物标志物发现与分子分型相关建模研究。
【获取与许可】
源站页面:https://plos.figshare.com/articles/dataset/Sample_characteristics_of_cohorts_/29847118
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Zhou, Wenjia; Zhang, Junhua (2025). Sample characteristics of cohorts.. PLOS Computational Biology. Dataset
【使用注意】
· 源站最后修改于 2025-08-13,引用时请注意版本时效。
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。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放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(Figshare)
- 源站标题:Sample characteristics of cohorts.
- DOI:10.1371/journal.pcbi.1013349.s005
- 发布日期:2025-08-06
- 文件数:1 个
- 文件总体积:19.23 KB
- 源站页面:https://plos.figshare.com/articles/dataset/Sample_characteristics_of_cohorts_/29847118
命令行下载
curl -L -o "pcbi.1013349.s005.xlsx" "https://ndownloader.figshare.com/files/56974510"
文件清单
pcbi.1013349.s005.xlsx(19.23 KB)
文件总体积 19.23 KB(源站 API 实测)。
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。
数据说明
Discovery of cancer driver pathways is essential for targeted therapies, since these pathways govern tumor progression and treatment resistance. However, their context-specific patterns across populations remain poorly understood. Leveraging pan-cancer genomic data, we apply our two models, EntCDP and ModSDP, to perform stratified analyses from four perspectives: region, tumor type, age group, and risk factors. Our results reveal the regional biases in perturbed pathways, such as PI3K-Akt in Chinese patients and GPCR in American patients with bladder cancer. Subtype comparisons highlight the mTOR signaling in lung adenocarcinoma and the FoxO signaling in lung squamous cell carcinoma. Pediatric-adult comparisons emphasize the enrichment of Ras signaling in pediatric acute myeloid leukemia and PAK signaling in pediatric glioblastoma, respectively. Risk factor associations further link Notch-mediated pathways to alcohol consumption and CDKN-regulated pathways to obesity-related cancers. Our findings demonstrate the utility of stratified driver pathway analysis in uncovering common and specific mechanisms, which can help prioritize context-aware therapeutic targets.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pcbi.1013349.s005(source: https://plos.figshare.com/articles/dataset/Sample_characteristics_of_cohorts_/29847118)。
数据缺失?
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