arXiv subjects

Yunfan Tang

Publications and source records attributed to Yunfan Tang.

3 recordsLinked to original sources

arXiv2018

Identifying Heritable Communities of Microbiome by Root-Unifrac and Wishart Distribution

We introduce a method to identify heritable microbiome communities when the input is a pairwise dissimilarity matrix among all samples. Current methods target each taxon individually and are unable to take advantage of their phylogenetic relationships. In contrast, our approach focuses on community heritability by using the root-Unifrac to summarize the microbiome samples through their pairwise dissimilarities while taking the phylogeny into account. The resulting dissimilarity matrix is then transformed into an outer product matrix and further modeled through a Wishart distribution with the same set of variance components as in the univariate model. Directly modeling the entire dissimilarity matrix allows us to bypass any dimension reduction steps. An important contribution of our work is to prove the positive definiteness of such outer product matrix, hence the applicability of the Wishart distribution. Simulation shows that this community heritability approach has higher power than existing methods to identify heritable groups of taxa. Empirical results on the TwinsUK dataset are also provided.

stat.ME↗

arXiv2017

Mixed Effect Dirichlet-Tree Multinomial for Longitudinal Microbiome Data and Weight Prediction

Quantifying the relation between gut microbiome and body weight can provide insights into personalized strategies for improving digestive health. In this paper, we present an algorithm that predicts weight fluctuations using gut microbiome in a healthy cohort of newborns from a previously published dataset. Microbial data has been known to present unique statistical challenges that defy most conventional models. We propose a mixed effect Dirichlet-tree multinomial (DTM) model to untangle these difficulties as well as incorporate covariate information and account for species relatedness. The DTM setup allows one to easily invoke empirical Bayes shrinkage on each node for enhanced inference of microbial proportions. Using these estimates, we subsequently apply random forest for weight prediction and obtain a microbiome-inferred weight metric. Our result demonstrates that microbiome-inferred weight is significantly associated with weight changes in the future and its non-trivial effect size makes it a viable candidate to forecast weight progression.

stat.AP↗

arXiv2017

A phylogenetic scan test on Dirichlet-tree multinomial model for microbiome data

In this paper we introduce the phylogenetic scan test (PhyloScan) for investigating cross-group differences in microbiome compositions using the Dirichlet-tree multinomial (DTM) model. DTM models the microbiome data through a cascade of independent local DMs on the internal nodes of the phylogenetic tree. Each of the local DMs captures the count distributions of a certain number of operational taxonomic units at a given resolution. Since distributional differences tend to occur in clusters along evolutionary lineages, we design a scan statistic over the phylogenetic tree to allow nodes to borrow signal strength from their parents and children. We also derive a formula to bound the tail probability of the scan statistic, and verify its accuracy through simulations. The PhyloScan procedure is applied to the American Gut dataset to identify taxa associated with diet habits. Empirical studies performed on this dataset show that PhyloScan achieves higher testing power in most cases.

stat.AP↗