Infant Gut Microbiome: Time-Adaptive Causal Network Reconstruction

2953 

Contributed Abstract 

Poster 

Sithija Manage (1), Martin Wells (2), Y. Samuel Wang (3)

(1) Cornell University, Ithaca, NY, (2) Cornell University, N/A, (3) University of Washington, N/A

Martin Wells  
Cornell University

Y. Samuel Wang  
University of Washington

Sithija Manage  
Cornell University

Sithija Manage  
Texas A&M University

The composition of the intestinal microbiome has a significant impact on children's health, starting from the prenatal period. Transformations in the microbiome during infancy have been associated with the development of chronic illnesses such as asthma and inflammatory bowel disease. However, the scientific investigation of the gut microbiome is complicated by certain aspects of the data such as compositionality and zero-inflation. Furthermore, causal discovery and causal inference in this space, without resorting to transformations of the data that alter mathematical properties in the underlying geometry is a developing area of research. In this work we develop novel methodological and statistically sound tools that recover causal relations and networks among bacteria in the developing gut microbiome without distorting the underlying geometry and expand this framework to allow for variation over time

Compositional Data|Longitudinal Data | Zero-Inflation| Aitchison Geometry| Graphical Models|

Biometrics Section

High Dimensional Regression