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

Ben D. Fulcher (), Aurina Arnatkeviciute and Alex Fornito
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Ben D. Fulcher: The University of Sydney
Aurina Arnatkeviciute: Monash University
Alex Fornito: Monash University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-22862-1

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