报告时间：2021年10月20日

               上午10:00-11:00

报告地点：腾讯会议

              （会议ID：624308231）

报告嘉宾：Hu Yijuan

报告主题：LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

报告摘要

LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

Compositional analysis is based on the premise that a relatively small proportion of taxa are "differentially abundant", while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods of compositional analysis such as ANCOM or ANCOM-BC use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. We propose LOCOM, a logistic regression approach to compositional analysis, that does not require pseudocounts and is robust to experimental biases. We use a Firth bias-corrected estimating function to account for sparse data. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for continuous and/or discrete confounding covariates is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, ANCOM often had inflated FDR; ANCOM-BC largely controlled FDR but still had modest inflation occasionally. LOCOM and ANCOM were robust to experimental biases in every situation, while ANCOM-BC had elevated FDR when biases at causal and non-causal taxa were differentially distributed. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies.

个人简介

Hu Yijuan, Tenured Associate Professor of Emory University, Department of Biostatistics and Bioinformatics (Rollins School of Public Health), has been focusing on developing statistical methods and software packages for analysis of high-throughput microbiome data, as well as applying them to studies of various diseases. She obtained the BS in Statistics from Peking University (School of Mathematic Sciences) in 2005 and PhD in Biostatistics from University of North Carolina at Chapel Hill (School of Public Health) in 2011. Then, She joined Emory University, first as an endowed Rollins Assistant Professor and then as a tenured Associate Professor since 2017.