Ensuring that fundamentals of quantitative microbiology are reflected in microbial diversity analyses based on next-generation sequencing

Abstract

Diversity analysis of amplicon sequencing data is mainly limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, read counts obtained from a sample are random data linked to source properties by a probabilistic process. Thus, diversity analysis has focused on diversity of (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Types of random error are described and clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and real). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, sample-level diversity analysis approaches are discussed. HighlightsO_LIRandom error in amplicon sequencing method should be considered in diversity analysis C_LIO_LIClustering, amplification, and differential recovery distort sample diversity C_LIO_LIThe multinomial model for compositional count data is compromised by amplification C_LIO_LIThere are three types of zeros in amplicon sequencing data, including missing zeros C_LIO_LISource alpha diversity estimates are biased by unknown number of unique variants C_LI