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

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

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

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

Comparison of the estimated environmental gradients and the model fits from three ordination methods applied to the Antarctic lakes data.

<p>Dimension 1 and Dimension 2 refer to models fitted with the environmental scores on dimensions 1 and 2, respectively. MSE gives the mean squared error calculated only among Bell-shaped species, MSE* stands for the mean squared error calculated from all species.</p

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

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

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

Concordance plot of the WMW test and the sign methods.

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

Relation to the log-fold change.

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

Simulation study set up.

This appendix provides more information on the setup of the simulations by visualizing and summarising the parameters that were taken into account. Additionally, more information on the packages(and versions) used is provided. (PDF)</p

The relative changes of average LLR (left) and average SSE (right) as a function of the penalty parameter <i>δ</i>.

<p>The relative changes of average LLR (left) and average SSE (right) as a function of the penalty parameter <i>δ</i>.</p