Avoiding False Discoveries in Single-cell RNA-seq by Revisiting the First Alzheimer's Disease Dataset

Overview

Journal Elife

Specialty Biology

Date 2023 Dec 4

PMID 38047913

Authors

Alan E Murphy

Nurun Fancy

Nathan Skene

Affiliations

Soon will be listed here.

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.

Citing Articles

A Reproducibility Focused Meta-Analysis Method for Single-Cell Transcriptomic Case-Control Studies Uncovers Robust Differentially Expressed Genes.

Nakatsuka N, Adler D, Jiang L, Hartman A, Cheng E, Klann E bioRxiv. 2024; .

PMID: 39463993 PMC: 11507907. DOI: 10.1101/2024.10.15.618577.

Loss of immune cell identity with age inferred from large atlases of single cell transcriptomes.

Connolly E, Pan T, Aluru M, Chockalingam S, Dhere V, Gibson G Aging Cell. 2024; 23(12):e14306.

PMID: 39143696 PMC: 11634704. DOI: 10.1111/acel.14306.

Transcriptional analysis of primary ciliary dyskinesia airway cells reveals a dedicated cilia glutathione pathway.

Koenitzer J, Gupta D, Twan W, Xu H, Hadas N, Hawkins F JCI Insight. 2024; 9(17).

PMID: 39042459 PMC: 11385084. DOI: 10.1172/jci.insight.180198.

Single-nucleus sequencing reveals enriched expression of genetic risk factors in extratelencephalic neurons sensitive to degeneration in ALS.

Limone F, Mordes D, Couto A, Joseph B, Mitchell J, Therrien M Nat Aging. 2024; 4(7):984-997.

PMID: 38907103 PMC: 11257952. DOI: 10.1038/s43587-024-00640-0.

SpotSweeper: spatially-aware quality control for spatial transcriptomics.

Totty M, Hicks S, Guo B bioRxiv. 2024; .

PMID: 38895212 PMC: 11185656. DOI: 10.1101/2024.06.06.597765.

References

Wakhloo D, Scharkowski F, Curto Y, Butt U, Bansal V, Steixner-Kumar A . Functional hypoxia drives neuroplasticity and neurogenesis via brain erythropoietin. Nat Commun. 2020; 11(1):1313. PMC: 7062779. DOI: 10.1038/s41467-020-15041-1. View

Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young J . Single-cell transcriptomic analysis of Alzheimer's disease. Nature. 2019; 570(7761):332-337. PMC: 6865822. DOI: 10.1038/s41586-019-1195-2. View

Hao Y, Hao S, Andersen-Nissen E, Mauck 3rd W, Zheng S, Butler A . Integrated analysis of multimodal single-cell data. Cell. 2021; 184(13):3573-3587.e29. PMC: 8238499. DOI: 10.1016/j.cell.2021.04.048. View

Amezquita R, Lun A, Becht E, Carey V, Carpp L, Geistlinger L . Orchestrating single-cell analysis with Bioconductor. Nat Methods. 2019; 17(2):137-145. PMC: 7358058. DOI: 10.1038/s41592-019-0654-x. View

Tran H, Ang K, Chevrier M, Zhang X, Lee N, Goh M . A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020; 21(1):12. PMC: 6964114. DOI: 10.1186/s13059-019-1850-9. View

Heumos L, Schaar A, Lance C, Litinetskaya A, Drost F, Zappia L . Best practices for single-cell analysis across modalities. Nat Rev Genet. 2023; 24(8):550-572. PMC: 10066026. DOI: 10.1038/s41576-023-00586-w. View

Ilicic T, Kim J, Kolodziejczyk A, Bagger F, McCarthy D, Marioni J . Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016; 17:29. PMC: 4758103. DOI: 10.1186/s13059-016-0888-1. View

McGinnis C, Murrow L, Gartner Z . DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst. 2019; 8(4):329-337.e4. PMC: 6853612. DOI: 10.1016/j.cels.2019.03.003. View

Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M . The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32(4):381-386. PMC: 4122333. DOI: 10.1038/nbt.2859. View

10.

McQuade A, Blurton-Jones M . Microglia in Alzheimer's Disease: Exploring How Genetics and Phenotype Influence Risk. J Mol Biol. 2019; 431(9):1805-1817. PMC: 6475606. DOI: 10.1016/j.jmb.2019.01.045. View

11.

Zimmerman K, Espeland M, Langefeld C . A practical solution to pseudoreplication bias in single-cell studies. Nat Commun. 2021; 12(1):738. PMC: 7854630. DOI: 10.1038/s41467-021-21038-1. View

12.

Hodge R, Bakken T, Miller J, Smith K, Barkan E, Graybuck L . Conserved cell types with divergent features in human versus mouse cortex. Nature. 2019; 573(7772):61-68. PMC: 6919571. DOI: 10.1038/s41586-019-1506-7. View

13.

Fernandes H, Patikas N, Foskolou S, Field S, Park J, Byrne M . Single-Cell Transcriptomics of Parkinson's Disease Human In Vitro Models Reveals Dopamine Neuron-Specific Stress Responses. Cell Rep. 2020; 33(2):108263. DOI: 10.1016/j.celrep.2020.108263. View

14.

Rau A, Marot G, Jaffrezic F . Differential meta-analysis of RNA-seq data from multiple studies. BMC Bioinformatics. 2014; 15:91. PMC: 4021464. DOI: 10.1186/1471-2105-15-91. View

15.

Lun A, Riesenfeld S, Andrews T, Dao T, Gomes T, Marioni J . EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 2019; 20(1):63. PMC: 6431044. DOI: 10.1186/s13059-019-1662-y. View

16.

Xi N, Li J . Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data. Cell Syst. 2020; 12(2):176-194.e6. PMC: 7897250. DOI: 10.1016/j.cels.2020.11.008. View

17.

Murphy A, Skene N . A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis. Nat Commun. 2022; 13(1):7851. PMC: 9780232. DOI: 10.1038/s41467-022-35519-4. View

18.

Welch J, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko E . Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity. Cell. 2019; 177(7):1873-1887.e17. PMC: 6716797. DOI: 10.1016/j.cell.2019.05.006. View

19.

Brase L, You S, DOliveira Albanus R, Del-Aguila J, Dai Y, Novotny B . Single-nucleus RNA-sequencing of autosomal dominant Alzheimer disease and risk variant carriers. Nat Commun. 2023; 14(1):2314. PMC: 10121712. DOI: 10.1038/s41467-023-37437-5. View

20.

Soneson C, Robinson M . Bias, robustness and scalability in single-cell differential expression analysis. Nat Methods. 2018; 15(4):255-261. DOI: 10.1038/nmeth.4612. View