Investigating differential abundance methods in microbiome data: A benchmark study

Abstract

The development of increasingly efficient and cost-effective high throughput DNA sequencing techniques has enhanced the possibility of studying complex microbial systems. Recently, researchers have shown great interest in studying the microorganisms that characterise different ecological niches. Differential abundance analysis aims to find the differences in the abundance of each taxa between two classes of subjects or samples, assigning a significance value to each comparison. Several bioinformatic methods have been specifically developed, taking into account the challenges of microbiome data, such as sparsity, the different sequencing depth constraint between samples and compositionality. Differential abundance analysis has led to important conclusions in different fields, from health to the environment. However, the lack of a known biological truth makes it difficult to validate the results obtained. In this work we exploit metaSPARSim, a microbial sequencing count data simulator, to simulate data with differential abundance features between experimental groups. We perform a complete comparison of recently developed and established methods on a common benchmark with great effort to the reliability of both the simulated scenarios and the evaluation metrics. The performance overview includes the investigation of numerous scenarios, studying the effect on methods’ results on the main covariates such as sample size, percentage of differentially abundant features, sequencing depth, feature variability, normalisation approach and ecological niches. Mainly, we find that methods show a good control of the type I error and, generally, also of the false discovery rate at high sample size, while recall seem to depend on the dataset and sample size.

Protocol for microbiota analysis of a murine stroke model

“… Provide the function with the ANCOM-BC results, a vector of the groups and a color palette if desired.

res_ancom <- ancom_da(ps_norm, formula = "Group", group = "Group", ord=c("Stroke", "Sham")) p <- plot_da(res_ancom, groups = c("Sham", "Stroke"), cols=colours)

Note: ANCOM-BC is preferred here based on the recommendation of Lin and Peddada 23 and Cappellato et al., 32 being one of the few compositional differential-abundance methods with a good balance between statistical power and control of the false-discovery rate. 23 , 32 …”

“…Note: ANCOM-BC is preferred here based on the recommendation of Lin and Peddada 23 and Cappellato et al., 32 being one of the few compositional differential-abundance methods with a good balance between statistical power and control of the false-discovery rate. 23 , 32 …”

Different trematode parasites in the same snail host: Species‐specific or shared microbiota?

“…input data requirements, data transformation, distribution models) and of the data (e.g. sparsity, effect size between conditions, depth of sequencing) (Cappellato et al, 2022; Nearing et al, 2022; Paulson et al, 2013). Thus, to attain a more thorough evaluation of the data, the methods used for differential abundance analyses were ALDEx2_kw (Fernandes et al, 2014), for which benchmarking studies show an above‐average performance when compared with a number of other methods (Nearing et al, 2022; Yang & Chen, 2022) and the corncob v0.3.0 package (Martin et al, 2022), which enables estimation of differential abundance and variability (i.e.…”

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

“… Source : The information for the construction of this table was taken from Cappellato et al. (2022), Fernandes et al. (2014), Hugerth and Andersson (2017), Lin and Peddada (2020a), Lin and Peddada (2020b), Love et al.…”