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Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data

Fulcher, Ben D., Arnatkeviciute, Aurina, Fornito, Alex·2021·Nature Communications
DOI 10.1038/s41467-021-22862-1PMC PMC8113439

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Abstract

Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene–gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox.

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Metadata

Authors
Fulcher, B. D., Arnatkeviciute, A., & Fornito, A.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-05-11
Volume
12
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-021-22862-1
PMC
PMC8113439
Cited by
164
References
15
Language
en
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