Estimation of statistical associations in microbial genomic survey count data is fundamental to microbiome research. Experimental limitations, including count compositionality, low sample sizes and technical variability, obstruct standard application of association measures and require data normalization prior to statistical estimation. Here, we investigate the interplay between data normalization, microbial association estimation and available sample size by leveraging the large-scale American Gut Project (AGP) survey data. We analyze the statistical properties of two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis workflows and log-ratio transformations. We show that shrinkage estimation, a standard statistical regularization technique, can universally improve the quality of taxon-taxon association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as downstream data analysis examples, we show that variance-stabilizing and log-ratio approaches enable the most taxonomically and structurally coherent estimates. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.
ASJC Scopus subject areas
- Structural Biology
- Molecular Biology
- Computer Science Applications
- Applied Mathematics