Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset.
Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset.
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DOI：
10.7554/elife.90214
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发表时间：
2023-12-04
期刊：
影响因子：
7.7
通讯作者：
Skene N
中科院分区：
文献类型：
--
作者：
Murphy AE;Fancy N;Skene N
Mathys等人。对阿尔茨海默病(AD)进行了第一次单核RNA-seq(snRNA-seq)研究(Mathys等人，2019年)。有了成批的RNA-seq，跨细胞类型的基因表达的变化可能会丢失，潜在地掩盖了跨不同细胞类型的差异表达基因(Deg)。通过使用单细胞技术，作者受益于分辨率的提高，有可能首次发现阿尔茨海默病特定细胞类型的DEGS。然而，它们在数据处理、质量控制和差异表达分析方面都存在局限性。在这里，我们纠正了这些问题，并使用最佳实践方法来进行SnRNA-seq差异表达，从而在错误发现率为0.05的情况下将DEG减少了549倍。因此，这项研究强调了质量控制和差异分析方法对发现疾病相关基因的影响，并旨在将AD研究领域的重点从虚假识别的基因转移到AD研究领域。
Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer’s disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.