Improving reproducibility of differentially expressed genes in single-cell transcriptomic studies of neurodegenerative diseases through meta-analysis

Nathan Nakatsuka (), Drew Adler, Longda Jiang, Austin Hartman, Evan Cheng, Eric Klann and Rahul Satija
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Nathan Nakatsuka: New York Genome Center
Drew Adler: New York University
Longda Jiang: New York Genome Center
Austin Hartman: New York Genome Center
Evan Cheng: New York University
Eric Klann: New York University
Rahul Satija: New York Genome Center

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: Abstract False positive claims of differentially expressed genes (DEGs) in scRNA-seq studies are of substantial concern. We found that DEGs from individual Parkinson’s (PD), Huntington’s (HD), and COVID-19 datasets had moderate predictive power for case-control status of other datasets, but DEGs from Alzheimer’s (AD) and Schizophrenia (SCZ) datasets had poor predictive power. We developed a non-parametric meta-analysis method, SumRank, based on reproducibility of relative differential expression ranks across datasets, and found DEGs with improved predictive power. Specificity and sensitivity of these genes were substantially higher than those discovered by dataset merging and inverse variance weighted p-value aggregation methods. Up-regulated DEGs implicated chaperone-mediated protein processing in PD glia and lipid transport in AD and PD microglia, while down-regulated DEGs were in glutamatergic processes in AD astrocytes and excitatory neurons and synaptic functioning in HD FOXP2 neurons. Lastly, we evaluate factors influencing reproducibility of individual studies as a prospective guide for experimental design.

Date: 2025
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DOI: 10.1038/s41467-025-62579-z

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