Scripts for Breast Cancer Bulk RNA-Seq Differential Expression, Survival, and Enrichment Analyses Using GEO Data

This repository contains R and Python scripts used to analyze bulk RNA-Seq datasets from the Gene Expression Omnibus (GEO) related to breast, prostate, endometrial, lung, and colorectal cancer. The workflow includes differential expression analysis, survival analysis, functional enrichment, and visualization.Included workflows:<b>R scripts:</b><b>Data processing and normalization</b> of GEO-derived bulk RNA-Seq datasets.<b>Differential expression analysis</b> (DEG) using limma.<b>Survival analysis</b> using Cox proportional hazards models<b>Functional enrichment analysis</b> (GO, Reactome) of overlapping DEGs to identify significant pathways<b>Python scripts:</b><b>Forest plot generation</b> using Matplotlib and Seaborn to visualize the survival analysis's hazard ratios and confidence intervals.<b>Data Source:</b>Publicly available bulk RNA-Seq datasets from the <b>Gene Expression Omnibus (GEO)</b> database.

本仓库包含用于分析来自基因表达综合数据库（Gene Expression Omnibus，GEO）的、与乳腺、前列腺、子宫内膜、肺及结直肠癌相关的 bulk RNA-Seq 数据集的 R 和 Python 脚本。该工作流涵盖差异表达分析、生存分析、功能富集分析及可视化。
包含的工作流：
<b>R脚本：</b>
<b>数据处理与标准化：</b>对源自GEO的bulk RNA-Seq数据集进行处理与标准化。
<b>差异表达分析：</b>使用limma进行差异表达基因（differential expression gene，DEG）分析。
<b>生存分析：</b>采用Cox比例风险模型进行生存分析。
<b>功能富集分析：</b>对重叠差异表达基因进行GO及Reactome功能富集分析，以识别显著通路。
<b>Python脚本：</b>
<b>森林图生成：</b>使用Matplotlib和Seaborn生成森林图，可视化生存分析中的风险比及置信区间。
<b>数据来源：</b>来自<b>基因表达综合数据库（Gene Expression Omnibus，GEO）</b>的公开可用bulk RNA-Seq数据集。