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Title: 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.  more » « less
Award ID(s):
1661331
NSF-PAR ID:
10236464
Author(s) / Creator(s):
; ;
Editor(s):
Kelso, Janet
Date Published:
Journal Name:
Bioinformatics
ISSN:
1367-4803
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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