On the inherent challenge of eliminating compositional artifacts in sparse 16S rRNA datasets

Artifacts in Sparse 16S rRNA Datasets Raad Z. Gharaibeh1,2, Timothy J. Hamp1, Anthony A. Fodor1 Department of Bioinformatics and Genomics1, Bioinformatics Services Division2 University of North Carolina at Charlotte Abstract: The use of next-generation sequencing technologies applied to PCR products targeting the 16S rRNA gene has revolutionized the study of mixed microbial communities. Sequencing profiles of these communities involve compositional data in which different samples yield different numbers of sequences. Recently, several studies have suggested that analysis paths designed for differential gene expression analysis can be applied to 16S rRNA datasets. These analysis paths can involve normalization schemes that attempt to correct for compositional artifacts by subtracting the log of the median or geometric mean from the log of each taxa in each sample. In a sparse dataset, however, the taxa defined by the median can have a count close to or exactly equal to zero, and hence these types of normalization approaches can yield results that in some cases are similar to not normalizing the counts. Utilizing a recently published dataset on IBD patients, we show that analysis paths that utilize these strategies can result in ordination axes that have highly significant correlations with the number of sequences in each sample. While some normalization schemes are more sensitive to the number of sequences per sample than others, there does not appear to be a known normalization approach that completely eliminates associations with the number of sequences per sample. Our results suggest therefore that no matter which normalization scheme is employed, artifactual dependencies on the number of sequences per sample should be explicitly tracked by 16S rRNA analysis pipelines as the effect size associated with this artifact can be substantially larger than associations with the biological variable(s) under investigation. Further, we demonstrate that in some sequence sets the number of sequences per sample is directly confounded with experimental variables of interest, further complicating the interpretation of significant associations. Background: In a typical 16S rRNA experiment, there can be a factor of 100 or more difference in the number of sequences assigned to each sample. 16S rRNA sequencing experiments are therefore compositional in nature. Before inferential statistics can be applied, some strategy is required to normalize each sample to correct for the different number of sequences that will be assigned to each sample. 16S rRNA experiments are often analyzed with Simple Normalization Schemes: Rarefaction: Sample sequences without replacement so all samples have the same number of sequences. Relative Abundance: Divided the number of counts assigned to each sample by the total number of counts in the sample. Aitchison Inspired Normalization Schemes: In his classic book, The Statistical Analysis of Compositional Data, Aitchison noted that many compositional artifacts are eliminated when working in ratio space. For example, consider a sample which contains 3 OTUs which are observed with sequence counts of 5, 10 and 100: { 5, 10, 100 } Notice that the ratio of the second OTU to the first is 2. Using a simple relative abundance normalization scheme, each OTU can be divided by the total number of sequences to yield: { 5/115, 10/115, 100/115 } = { 0.04, 0.09, 0.87 } Note that even after such a normalization, the ratio of the second OTU to the first remains 2. Moreover, this ratio will be preserved no matter what value is used to normalize the OTU counts. Inspired by this line of argument, Aitchison Inspired Normalization Schemes work in log space and normalize by subtracting off either the log median of each sample (RLE normalization in edgeR [1]) or the geometric mean of each samples (as proposed by [2]). Recent papers ([2]-[3]) have argued that pipelines based on these normalization schemes may be more appropriate for 16S datasets than the simple normalization schemes often employed in 16S rRNA pipelines. Finally, the Variance Stabilizing Transformation (VST) as implemented in DESeq2 attempts to adjust the variance so that it is roughly constant across the entire dynamic range of sequence counts [4]. 0.9