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eLife Sciences Publications, eLife, (12), 2023

DOI: 10.7554/elife.90214.3

eLife Sciences Publications, eLife, (12), 2023

DOI: 10.7554/elife.90214

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Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer’s disease dataset

Journal article published in 2023 by Alan E. Murphy ORCID, Nurun Fancy ORCID, Nathan Skene
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

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.