对比两种基因检测技术，发现其结果高度一致，且关键基因在不同平台表现稳定。
Assessing Concordance between RNA-Seq and NanoString Technologies in Ebola-Infected Nonhuman Primates Using Machine Learning
- 用机器学习方法比对RNA-Seq与NanoString数据，验证其相关性。
- 12个共检出基因在两平台均显著差异表达，如OAS1、ISG15等。
- 适合研究埃博拉病毒感染机制或需跨平台验证基因表达的团队。
本研究评估了在埃博拉病毒（EBOV）感染非人灵长类动物（NHPs）中，RNA测序（RNA-Seq）与NanoString技术在基因表达分析中的一致性。对62个样本的详细比较显示，两者间存在强相关性，56个样本的Spearman相关系数在0.78至0.88之间，平均值为0.83，中位数为0.85。Bland-Altman分析进一步确认高一致性，多数测量值位于95%置信区间内。采用基于NanoString数据训练的监督幅度-海拔评分（SMAS）方法，识别出OAS1为区分RT-qPCR阳性与阴性样本的关键标志物。该模型应用于RNA-Seq数据时，使用逻辑回归同样实现100%准确率区分感染与未感染样本，证明其跨平台鲁棒性。差异表达分析共发现12个共同显著基因：ISG15、OAS1、IFI44、IFI27、IFIT2、IFIT3、IFI44L、MX1、MX2、OAS2、RSAD2、OASL，具有最高统计学显著性和生物学意义。基因本体（GO）分析表明这些基因直接参与免疫应答和病毒感染通路。此外，RNA-Seq还特异性检测到CASP5、USP18、DDX60等在免疫调控和抗病毒防御中起关键作用的基因，凸显其更广检测范围。结果表明，两种平台互补性强，可提供对埃博拉病毒感染期间基因表达变化的全面、准确评估。
原文摘要 · Abstract (English)
This study evaluates the concordance between RNA sequencing (RNA-Seq) and NanoString technologies for gene expression analysis in non-human primates (NHPs) infected with Ebola virus (EBOV). We performed a detailed comparison of both platforms, demonstrating a strong correlation between them, with Spearman coefficients for 56 out of 62 samples ranging from 0.78 to 0.88, with a mean of 0.83 and a median of 0.85. Bland-Altman analysis further confirmed high consistency, with most measurements falling within 95% confidence limits. A machine learning approach, using the Supervised Magnitude-Altitude Scoring (SMAS) method trained on NanoString data, identified OAS1 as a key marker for distinguishing RT-qPCR positive from negative samples. Remarkably, when applied to RNA-Seq data, OAS1 also achieved 100% accuracy in differentiating infected from uninfected samples using logistic regression, demonstrating its robustness across platforms. Further differential expression analysis identified 12 common genes including ISG15, OAS1, IFI44, IFI27, IFIT2, IFIT3, IFI44L, MX1, MX2, OAS2, RSAD2, and OASL which demonstrated the highest levels of statistical significance and biological relevance across both platforms. Gene Ontology (GO) analysis confirmed that these genes are directly involved in key immune and viral infection pathways, reinforcing their importance in EBOV infection. In addition, RNA-Seq uniquely identified genes such as CASP5, USP18, and DDX60, which play key roles in immune regulation and antiviral defense. This finding highlights the broader detection capabilities of RNA-Seq and underscores the complementary strengths of both platforms in providing a comprehensive and accurate assessment of gene expression changes during Ebola virus infection.
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