Mean-Centering Alters Pathway-Based Transcriptomic Comparisons of Feline and Mouse Kidney Disease

Transcriptomic similarity is used to characterize disease models, but a comparison can change when the same responses are summarized differently. We examined mean-centering before pathway averaging in 16 disease states from one feline chronic kidney disease cohort and two mouse kidney injury models, using 2016 genes detected in both species’ proteomes and 422 overlapping pathways. Among 87 state pairs without shared control samples, uncentered pathway means separated same-species from cross-species pairs with an area under the curve of 0.868; removing each state’s mean across genes first reduced this to 0.511. The uncentered value was higher in 98.6% of animal-level resamples, and the difference held across nine offset and filter settings, all four gene spaces and the 83-pair subset without shared animals. Under within-state ranking or quantile normalization, which equalize each state’s overall level by construction, the contrast fell to 0.010 and 0.001. Reference-axis projections can rank states differently from cosine because they retain amplitude, and benchmark gaps were poorly calibrated. These results identify a consequential preprocessing choice rather than a preferred species or a validated measure of disease fidelity. Because all feline states came from one cohort and all mouse states from two datasets, species was confounded with dataset throughout, and the separation reported here may reflect dataset-specific technical variation rather than species biology.