性别差异研究衍生数据集

数据集获取（Figshare）

• 源站标题：Derived datasets on ‘‘sexdiff research’’
• DOI：10.6084/m9.figshare.23961336.v1
• 发布日期：2023-08-16
• 文件数：75 个
• 文件总体积：2.81 GB
• 源站页面：https://figshare.com/articles/dataset/Derived_datasets_on_sexdiff_research_/23961336

命令行下载

curl -L -o "ACC_Clinical.txt" "https://ndownloader.figshare.com/files/42014346"
curl -L -o "ACC_CNV.txt" "https://ndownloader.figshare.com/files/42014349"
curl -L -o "ACC_Mutation.txt" "https://ndownloader.figshare.com/files/42014352"
curl -L -o "BLCA_Clinical.txt" "https://ndownloader.figshare.com/files/42014355"
curl -L -o "BLCA_CNV.txt" "https://ndownloader.figshare.com/files/42014358"
curl -L -o "BLCA_Mutation.txt" "https://ndownloader.figshare.com/files/42014361"
curl -L -o "CHOL_Clinical.txt" "https://ndownloader.figshare.com/files/42014364"
curl -L -o "CHOL_CNV.txt" "https://ndownloader.figshare.com/files/42014367"

文件清单

• ACC_Clinical.txt（25.82 KB）
• ACC_CNV.txt（5.03 MB）
• ACC_Mutation.txt（12.15 MB）
• BLCA_Clinical.txt（120.23 KB）
• BLCA_CNV.txt（23.15 MB）
• BLCA_Mutation.txt（166.15 MB）
• CHOL_Clinical.txt（11.26 KB）
• CHOL_CNV.txt（2.17 MB）
• CHOL_Mutation.txt（4.69 MB）
• COADREAD_Clinical.txt（167.38 KB）
• COADREAD_CNV.txt（61.95 MB）
• COADREAD_Mutation.txt（356.37 MB）
• DLBC_Clinical.txt（14.00 KB）
• DLBC_CNV.txt（2.66 MB）
• DLBC_Mutation.txt（8.03 MB）
• ESCA_Clinical.txt（52.51 KB）
• ESCA_CNV.txt（10.59 MB）
• ESCA_Mutation.txt（49.42 MB）
• GBM_Clinical.txt（112.24 KB）
• GBM_CNV.txt（31.11 MB）

文件总体积 2.81 GB（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

To treat cancer in this age of precision medicine, identifying effective patient groups or understanding the variations among patients is essential. Several recent reports have revealed that even the same treatment for the same disease has different effects depending on the sex. However, a detailed understanding of genomic differences according sex, including mutational differences, oncogenic signaling pathways, and co-occurrence or exclusivity networks, at the pan-cancer level remains elusive. There were sex-biased mutation and tumor type dependent mutations such as significantly female-enriched EGFR mutation or male-enriched TP53 mutations. In addition, HUWE1 mutations and PHKA2 mutations showed female-specific prognostic effect in overall survival. Oncogenic signaling pathways and co-occurring networks also showed diverse differences across tumor types. Finally, we discovered associations male-enriched the chemotherapy signature and female-enriched several mutational signatures. Our findings identified genomic mutational landscape caused by different sex and confirmed that sex is an important biological variable for understanding tumor biology. Finally, our findings will offer new findings and conclusions from abundant but unfound public data.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.23961336.v1（source: https://figshare.com/articles/dataset/Derived_datasets_on_sexdiff_research_/23961336）。