Accommodating Differential Read Depth in Analyses of Virome Diversity

Music City Center 

Controlling the total number of sequence reads for a given sample (read depth or library size) in virome sequencing studies is experimentally challenging. Since most alpha-diversity metrics are strongly dependent on read depth, statistical or computational normalization is necessary to ensure samples are measured on the same scale and to avoid confounding by read depth. Unfortunately, standard tools commonly used for read depth normalization in other omics fail for virome data. To address this problem, we propose the Read Depth Adjustment by Quantile-regression (ReDAQ) approach which regresses out the effect of depth from alpha-diversity metrics. To accommodate irregular distributions, we employ a quantile process regression approach and further allow for nonlinear read depth effects at each quantile. We show via real data analysis and simulations that ReDAQ works well in removing read depth effects, avoiding potential confounding, whereas extant approaches (developed primarily for bacteriome or other omics) fail.

virome

diversity

quantile-regression

microbiome

library size