The Kernel Regression Tree-Exploring Aggregations Estimator for Microbiome Analysis

2221 

Contributed Abstract 

Poster 

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

(1) Texas A&M University, N/A, (2) Cornell University, N/A

Y. Samuel Wang  
Cornell University

Martin Wells  
Cornell University

Sithija Manage  
Texas A&M University

Sithija Manage  
Texas A&M University

We introduce a novel Kernel Regression estimator, Kernel Regression with Tree-Exploring Aggregations (KR TEXAS), that learns a distance metric while allowing feature aggregation along a predefined tree structure. This approach is particularly relevant for microbiome analysis, where data is often collected at multiple taxonomic levels, and determining the appropriate level of aggregation is non-trivial. Unlike traditional aggregation methods that rely on uniform taxonomic levels, KR TEXAS leverages an L1-penalized distance metric to selectively aggregate features based on their importance, leading to biologically interpretable results. Our method extends prior work on metric learning and nonparametric regression, incorporating structured feature aggregation to improve predictive accuracy and interpretability. We demonstrate the utility of KR TEXAS through both simulations and real microbiome datasets, highlighting its advantages in capturing functional relationships that may be missed by conventional aggregation techniques.

Microbiome|Compositional Data|Kernel Regression|Metric Learning| |

Biometrics Section

Genomics, Metabolomics, Microbiome and NextGen Sequencing