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'"). It also includes a README file to guide the reproduction of the results in our 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).本数据集版本包含复现我们针对两篇同行通讯的回复中全部结果所需的代码与数据，该回复的标题为《针对〈忽视半合成RNA测序（RNA-seq）数据模拟中的归一化影响会产生人为假阳性〉与〈温莎化法（Winsorization）可大幅降低主流差异表达分析方法在人类群体样本分析中产生的假阳性〉的回应》。 本数据集还附带一份README文件，用于指导复现我们发表于原始研究论文《主流差异表达分析方法在分析人类群体样本时存在假阳性夸大现象》中的相关结果，该论文链接为：https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4。