Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data

This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (https://doi.org/10.1186/s13059-024-03232-8). It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4).本版本包含复现我们针对两篇通讯评论的回复中所有结果所需的代码与数据，该回复为《针对“半合成RNA测序（RNA-seq）数据模拟中忽视归一化影响会产生人工假阳性”及“温索化（Winsorization）可大幅降低主流差异表达分析方法在分析人类群体样本时的假阳性率”一文的回复》，对应DOI链接为https://doi.org/10.1186/s13059-024-03232-8。 本数据集还附带README文件，用于指导复现我们原创发表论文中的相关结果，同时提供了该原创论文中拟合优度检验所需的配套资源，该原创论文为《主流差异表达分析方法在分析人类群体样本时存在夸大假阳性的问题》，对应链接为https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4。