cILR: Competitive isometric log-ratio for taxonomic enrichment analysis

Research in human associated microbiomes often involves the analysis of taxonomic count tables generated via high-throughput sequencing. It is difficult to apply statistical tools as the data is high-dimensional, sparse, and strictly compositional. An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. Set-based analysis is ubiquitous in the genomics literature, and has demonstrable impact in improving interpretability and power of downstream analysis. Unfortunately, there is a lack of sophisticated set-based analysis methods specific to microbiome taxonomic data, where current practice often employs abundance summation as a technique for aggregation. This approach prevents comparison across sets of different sizes, does not preserve inter-sample distances, and amplifies protocol bias. Here, we attempt to fill this gap with a new single sample taxon set enrichment method based on the isometric log ratio transformation and the competitive null hypothesis commonly used in the enrichment analysis literature. Our approach, titled competitive isometric log ratio (cILR), generates sample-specific enrichment scores as the scaled log ratio of the subcomposition defined by taxa within a set and the subcomposition defined by its complement. We provide sample-level significance testing by estimating an empirical null distribution of our test statistic with valid p-values. Herein we demonstrate using both real data applications and simulations that cILR controls for type I error even under high sparsity and high inter-taxa correlation scenarios. Additionally, it provides informative scores that can be inputs to downstream differential abundance and prediction tasks.Author summaryThe study of human associated microbiomes relies on genomic surveys via high-throughput sequencing. However, microbiome taxonomic data is sparse and high dimensional which prevents the application of standard statistical techniques. One approach to address this problem is to perform analyses at the level of taxon sets. Set-based analysis has a long history in the genomics literature, with demonstrable impact in improving both power and interpretability. Unfortunately, there is not a lot of research in developing new set-based tools for microbiome taxonomic data specifically, given that compared to other ‘omics data types microbiome taxonomic data is strictly compositional. We developed a new tool to generate taxon set enrichment scores at the sample level by combining the isometric log-ratio and the competitive null hypothesis. Our scores can be used for statistical inference, and as inputs to other downstream analyses such as differential abundance and prediction models. We demonstrate the performance of our method against competing approaches across both real data analyses and simulation studies.