53: Nonparametric Denoising of Microbiome Metagenomics Data

Music City Center 

We propose a nonparametric method to denoise microbiome metagenomics sequencing count matrices. The goal of denoising is to recover the non-zero expected abundances of rare taxa and reduce the variance of prevalent taxa. The count matrices are dichotomized into a series of binary matrices given a sequence of thresholds. We estimate the probability of each count matrix entry being larger than each threshold by taking products of conditional probabilities. We develop a novel matrix factorization algorithm for the low-rank representation of conditional probabilities. We calculate the denoised count based on the empirical distribution formed by the estimated probabilities. Simulations show that our method is better than parametric competitors at recovering accurate microbiome compositions. Our denoising method can improve downstream analyses such as training prediction models and microbiome network analysis.

Microbiome metagenomics

Denoise

Binarization

Matrix factorization

Nonparametric 

Section on Statistics in Genomics and Genetics