Row data for 1042835.zip

<strong>Title: Hypoxia-dependent spatial transcriptomics predicted the prognosis and efficacy of immunotherapy in claudin-low breast cancer</strong> <strong>The raw data in this study includes four parts.</strong> <strong>Original data list</strong> <strong>Part 1 </strong> <strong>Source data (raw data, original data, individual data points) for the information presented in your tables and figures. This should be in a generally readable format and Excel files are preferred.</strong> This study peformed Spatial Transcriptomics (ST) to demonstrate their spatial distribution in human claudin-low breast cancer MDA-MB-231 engraft. 10x genomics official software Space Ranger 1.0.0 was used for data preprocessing, gene expression quantitative and point identification. The original Space Ranger files for 4 samples were also submitted as additional files in Jianguoyun website (https://www.jianguoyun.com/c/sd/15eabb7/5bdc92d86263a0b7). Software Seurat 4.0 was used to analyze and cluster the four samples. UMAP algorithm were used to reduce the dimension of data and visualize data. The differentially expressed genes and cluster type in 12 clusters listed in Supplementary Table 3. <strong>Part 2 </strong> <strong>If your raw data were obtained from publicly available datasets, please provide the working sheets used to analyse these data.</strong> RNA-Sequence data and associated clinical data for 1904 breast cancers (METABRIC) were downloaded from the cBioPortal website (http://www.cbioportal.org/datasets). We used these data to validate the relationship between the breast cancer hypoxia-dependent spatial clusters score and immune cell infiltrition, immune funtion and breast cancer subtype. Supplementary table 4 listed the clinicopathological factors and ssGSEA score of each sample. Supplementary table 5 showed the gene sets used in this study. <strong>Part 3 </strong> <strong>If statistical programs (such as R, SAS, SPSS, MATLAB) were used for the data analysis or figure creation in your manuscript, please provide all the relevant code/script files (such as .R or .SPS) and data sheets to replicate your analyses or figures.</strong> The breast cancer hypoxia-dependent spatial clusters score and immune fuction-related score was calculated based on the ssGSEA R launguage for 1904 human breast cancers. Kaplan–Meier (K-M) analysis were carried out using R language. The R script files for ssGSEA and K-M survival were submitted as additional files in Jianguoyun website（https://www.jianguoyun.com/c/sd/15eabb7/5bdc92d86263a0b7）. SPSS 20.0 software was used to perform Cox proportional hazards regression, ANOVA test, Chi-squared test, and <em>Pearson</em> correlation. The correlation heatmaps were provided by HIPLOT website (https://hiplot.com.cn/basic/cor-heatmap). <strong>Part 4</strong> This part of data are the original images in Figure 2, Figure 3 and Figure S1. There are immunofluorescence staining pictures and immunohistochemical staining pictures.

标题：缺氧依赖性空间转录组学（Spatial Transcriptomics, ST）预测低紧密连接型乳腺癌的预后与免疫治疗疗效
本研究的原始数据共包含四个部分。
原始数据列表
第一部分
用于支撑论文图表信息的源数据（原始数据、个体数据点），需采用通用可读格式，优先使用Excel文件。本研究通过空间转录组学（Spatial Transcriptomics, ST）技术，解析人类低紧密连接型乳腺癌MDA-MB-231移植瘤的空间分布特征。采用10x Genomics官方软件Space Ranger 1.0.0完成数据预处理、基因表达定量及位点识别。4个样本的原始Space Ranger文件已作为补充文件上传至坚果云（https://www.jianguoyun.com/c/sd/15eabb7/5bdc92d86263a0b7）。采用Seurat 4.0软件对4个样本进行分析与聚类，通过UMAP算法完成数据降维与可视化。补充表3列出了12个聚类中的差异表达基因及聚类类型。
第二部分
若原始数据来自公开数据集，请提供用于数据分析的工作表单。本研究从cBioPortal官网（http://www.cbioportal.org/datasets）下载了1904例乳腺癌的RNA测序数据及配套临床数据（METABRIC队列）。利用该数据集验证乳腺癌缺氧依赖性空间聚类评分与免疫细胞浸润、免疫功能及乳腺癌亚型之间的关联。补充表4列出了所有样本的临床病理特征及单样本基因集富集分析（single-sample Gene Set Enrichment Analysis, ssGSEA）评分，补充表5展示了本研究使用的基因集。
第三部分
若论文的数据分析或图表绘制使用了统计软件（如R、SAS、SPSS、MATLAB），请提供所有可复现分析或图表的相关代码/脚本文件（如.R或.SPS格式）及数据表。本研究基于ssGSEA R语言包，对1904例人类乳腺癌样本计算了缺氧依赖性空间聚类评分及免疫功能相关评分；采用R语言完成Kaplan–Meier（K-M）生存分析。用于ssGSEA与K-M生存分析的R脚本文件已作为补充文件上传至坚果云（https://www.jianguoyun.com/c/sd/15eabb7/5bdc92d86263a0b7）。采用SPSS 20.0软件完成Cox比例风险回归、方差分析、卡方检验及Pearson相关分析。相关性热图通过HIPLOT官网（https://hiplot.com.cn/basic/cor-heatmap）生成。
第四部分
本部分数据为图2、图3及补充图S1的原始图像，包含免疫荧光染色图片与免疫组化染色图片。