1. Compositionality and Sparseness in 16S rRNA data Anthony Fodor Associate Professor Bioinformatics and Genomics UNC Charlotte

2. Can we fairly compare high and low biomass samples? VS.

3. Low abundance samples are inherently challenging to survey Less abundant Hanna et al - Comparison of culture and molecular techniques for microbial community characterization in infected necrotizing pancreatitis - J. Surgical Research - 2014

4. Clearly the sequencing of negative controls should be part of all of our pipelines..

5. Can we fairly compare samples with different numbers of sequences? VS.

6. 16S rRNA experiments are always compositional and often sparse Compositional – because different samples have different numbers of sequences Sparse – because there are many zeros in the spreadsheet SAMPLESSAMPLES OTUs

7. Compositionality is a well-studied problem in statistics, but remains challenging

8. Compositionality can introduce subtle artifacts into our dataset Relative abundance Problems include: Inference may report a change in A and B even though biologically A and B have not changed. The estimate of A and B is dependent on C. If C is contaminant (or rRNA in a RNA-seq experiment), the values of A and B might not be appropriate. A and B will appear correlated, but this is a statistical artifact.

9. The correlation issue has been considered by multiple groups…

10. The compositional nature of 16S rRNA data has led to controversies over analysis pipelines…

11. Notice that in all the above examples, the ratio of B/A is always 2 irrespective of what happens with taxa C. 10 5 = 10 / 115 5 / 115 10 / 1015 5 / 1015 ==2 Normalization schemes can take advantage of working in ratio space Relative abundance

12. Cells in the spreadsheet with few counts are largely structured by sequencing depth Source: Gevers et al. - The Treatment-Naive Microbiome in New-Onset Crohn’s Disease - Cell Host Microbe 2014

13. Ordination without normalization leads to dependency of sequencing depth… log Log10 (number of sequences) Bray-Curtis distance

14. No normalization scheme eliminates the dependency of sequencing depth

15. No normalization scheme eliminates compositional dependencies Bioinformatics pipelines for 16S rRNA might consider explicitly tracking the number of sequences per samples as a potential confounder…

16. Sequencing depth can be correlated with input variables of interest… Log10 (number of sequences) NMDS 1 Theta YC distance Difference in number of sequences Source: Baxter et al. - Structure of the gut microbiome following colonization with human feces determines colonic tumor burden - Microbiome 2014

17. Log10 (number of sequences) Theta YC distance NMDS 1 Difference in number of sequences Difference in number of sequences Difference in number of sequences Difference in number of sequences Difference in number of sequences Difference in number of sequences Difference in number of sequences Different normalization schemes can have very different consequences for inference..

18. No normalization scheme eliminates compositional dependencies (although some do better than others!). Bioinformatics pipelines for 16S rRNA should explicitly track number of sequences per samples as a potential confounding variable. Just as no one statistical test is appropriate for inference, there is likely no one normalization scheme that will be appropriate for all datasets. Conclusions

19. Raad Z. Gharaibeh (We thank Dirk Gevers for providing a parsable OTU table for the Risk data)

20. Cells in the spreadsheet with few counts are largely structured by sequencing depth Source: Gevers et al. - The Treatment-Naive Microbiome in New-Onset Crohn’s Disease - Cell Host Microbe 2014

21. In any experiment confounding variables can complicate inference..