Normalization and microbial differential abundance strategies depend upon data characteristics

Abstract

Background: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Results: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. Conclusions: These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

The gut microbiome is associated with behavioural task in honey bees

“…We controlled for false discovery rate using the Benjamini and Hochberg procedure (Benjamini and Hochberg 1995). The procedure based on DESeq2 shows higher sensitivity on smaller datasets (< 20 samples per group), but tends towards a higher false discovery rate with more samples, very uneven (> 10×) library sizes or compositional effects (Weiss et al 2017). Because of these potential limitations, we also performed an analysis of composition of microbiomes (ANCOM) (Mandal et al 2015).…”

One Health Relationships Between Human, Animal, and Environmental Microbiomes: A Mini-Review

“…Whatever method is chosen must account for differences in the numbers of sequences in each DNA library. This is commonly addressed by either randomly resampling each sample to an even number of sequences (rarefaction) or through statistical models designed to incorporate sequencing depth (71). …”

Effect of water and resource availability on ant feeding preferences: a field experiment on the Mediterranean ant Crematogaster scutellaris

“…We controlled for false discovery rate using the Benjamini and Hochberg procedure (Benjamini and Hochberg 1995). The procedure based on DESeq2 shows higher sensitivity on smaller datasets (< 20 samples per group), but tends towards a higher false discovery rate with more samples, very uneven (> 10×) library sizes or compositional effects (Weiss et al 2017). Because of these potential limitations, we also performed an analysis of composition of microbiomes (ANCOM) (Mandal et al 2015).…”

“…Because of these potential limitations, we also performed an analysis of composition of microbiomes (ANCOM) (Mandal et al 2015). This procedure has recently been found to appropriately control for false discovery rate (Weiss et al 2017). ANCOM compares the log ratio of the abundance of each taxon to the abundance of all the remaining taxa one at a time and the Mann-Whitney U is then calculated on each log ratio (Mandal et al 2015;Weiss et al 2017).…”

“…This procedure has recently been found to appropriately control for false discovery rate (Weiss et al 2017). ANCOM compares the log ratio of the abundance of each taxon to the abundance of all the remaining taxa one at a time and the Mann-Whitney U is then calculated on each log ratio (Mandal et al 2015;Weiss et al 2017). The R implementation of the procedure (version 1.1-3) was used here.…”