Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies

Microbiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate that some well chosen sign and rank transformations also allow for valid inference with compositional data, and we show how logistic regression and probabilistic index models can be used for testing for differential abundance, while inheriting the flexibility of a statistical modelling framework. The results of a simulation study demonstrate that the new methods perform better than most other methods, and that it is comparable with ANCOM-BC. These methods are implemented in an R-package ‘signtrans’ and can be installed from Github (https://github.com/lucp9827/signtrans)

Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.

Microbiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate that some well chosen sign and rank transformations also allow for valid inference with compositional data, and we show how logistic regression and probabilistic index models can be used for testing for differential abundance, while inheriting the flexibility of a statistical modelling framework. The results of a simulation study demonstrate that the new methods perform better than most other methods, and that it is comparable with ANCOM-BC. These methods are implemented in an R-package 'signtrans' and can be installed from Github (https://github.com/lucp9827/signtrans)

Empirical FDRs vs sensitivities of the sign methods and competitors for the various simulation scenarios with the negative Binomial distribution.

The top row contains the results of scenarios with FC = 1.5, and the bottom row contains the results of scenarios with FC = 5. Left: Setting A (high sparsity) and Right: Setting B (low sparsity).</p

Estimates of the effect size parameters <i>β</i>_<i>A</i> for the S-sign and R-sign methods (SE and two-sided <i>p</i>-values are also reported).

The S-sign estimates for βA are a result from the ML parameter estimates of the logistic regression models taking the library size, gender and FIT into account. The estimates of the R-sign methods are a direct result of fitting PIMs, also taking the library size, gender and FIT into account.</p

Concordance plot of the WMW test and the sign methods.

Concordance plot of the WMW test and the sign methods.</p

Concordance plot of the ANCOM-BC and the sign methods.

Concordance plot of the ANCOM-BC and the sign methods.</p

Summary statistics of the data for the case study (per group).

Summary statistics of the data for the case study (per group).</p

Basic properties of sign-transforms.

Microbiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate that some well chosen sign and rank transformations also allow for valid inference with compositional data, and we show how logistic regression and probabilistic index models can be used for testing for differential abundance, while inheriting the flexibility of a statistical modelling framework. The results of a simulation study demonstrate that the new methods perform better than most other methods, and that it is comparable with ANCOM-BC. These methods are implemented in an R-package ‘signtrans’ and can be installed from Github (https://github.com/lucp9827/signtrans).</div

Reference frame description for all simulation scenarios of setting A and B with SPSimSeq and the Negative Binomial distribution.

Reference frame description for all simulation scenarios of setting A and B with SPSimSeq and the Negative Binomial distribution.</p