Fundamental gene network rewiring at the second order within and across mammalian systems

Abstract

Motivation Genetic or epigenetic events can rewire molecular networks to induce extraordinary phenotypical divergences. Among the many network rewiring approaches, no model-free statistical methods can differentiate gene-gene pattern changes not attributed to marginal changes. This may obscure fundamental rewiring from superficial changes. Results Here we introduce a model-free Sharma-Song test to determine if patterns differ in the second order, meaning that the deviation of the joint distribution from the product of marginal distributions is unequal across conditions. We prove an asymptotic chi-squared null distribution for the test statistic. Simulation studies demonstrate its advantage over alternative methods in detecting second-order differential patterns. Applying the test on three independent mammalian developmental transcriptome datasets, we report a lower frequency of co-expression network rewiring between human and mouse for the same tissue group than the frequency of rewiring between tissue groups within the same species. We also find secondorder differential patterns between microRNA promoters and genes contrasting cerebellum and liver development in mice. These patterns are enriched in the spliceosome pathway regulating tissue specificity. Complementary to previous mammalian comparative studies mostly driven by first-order effects, our findings contribute an understanding of system-wide second-order gene network rewiring within and across mammalian systems. Second-order differential patterns constitute evidence for fundamentally rewired biological circuitry due to evolution, environment, or disease. Availability The generic Sharma-Song test is available from the R package ‘DiffXTables’ at https://cran.r-project.org/package=DiffXTables. Other code and data are described in Methods. Supplementary information Supplementary data are available at Bioinformatics online.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction

“…Further, comparing distributions of clinical diagnoses across clusters showed that clusters derived by GMM-baseline ( χ 2 (9) = 226.4, p < 0.001 ) have a more uniform distribution of clinical diagnoses than MTMderived clusters ( χ 2 (9) = 308.0, p < 0.001 ). Comparing between models (Table S5) showed significantly higher deviation of the joint distribution of cluster assignment and clinical diagnosis from the product of the marginals for MTM than GMM-baseline (Sharma-Song test for second-order differentials in contingency tables 33 , χ 2 (9) = 48.3, p < 0.001 ). These results suggest that MTM training on longitudinal rather than baseline data alone stratifies individuals more reliably to clusters of cognitive health based on future disease progression.…”