Understanding artificial mouse-microbiome heterogeneity and six actionable themes to increase study power

Posted by: | October 23, 2019 | Comments

Study design to understand artificial heterogeneity in mouse microbiome research.

ABSTRACT

The negative effects of data clustering due to (intra-class/spatial) correlations are well-known in statistics to interfere with interpretation and study power. Therefore, it is unclear why housing many laboratory mice (≥4), instead of one-or-two per cage, with the improper use/reporting of clustered-data statistics, abound in the literature. Among other sources of ‘artificial’ confounding, including cyclical oscillations of the ‘cage microbiome’, we quantified the heterogeneity of modern husbandry practices/perceptions. The objective was to identify actionable themes to re-launch emerging protocols and intuitive statistical strategies to increase study power. Amenable for interventions, ‘cost-vs-science’ discordance was a major aspect explaining heterogeneity and the reluctance to change. Combined, four sources of information (scoping-reviews, professional-surveys, expert-opinion, and ‘implementability-score-statistics’) indicate that a six-actionable-theme framework could minimize ‘artificial’ heterogeneity.

Read more at: bioRxiv

Abigail R Basson, Alexandria LaSalla, Gretchen Lam, Danielle Kulpins, Erika L Moen, Mark Sundrud, Jun Miyoshi, Sanja Ilic, Betty R Theriault, Fabio Cominelli, Alexander Rodriguez-Palacios. bioRxiv. DOI: https://doi.org/10.1101/778043. 25 Sept 2019.





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