One area of disagreement is whether read count tables should be rarefied (i.e., subsampled) to correct for differing read depths across samples 3. Indeed, there are numerous ongoing debates regarding the best practices for differential abundance (DA) testing with microbiome data 1, 2. A frequent and seemingly simple question to investigate with this type of data is: which taxa significantly differ in relative abundance between sample groupings? Newcomers to the microbiome field may be surprised to learn that there is little consensus on how best to approach this question. Marker gene sequencing, such as 16S rRNA gene sequencing, is the most common form of microbiome profiling and enables the relative abundances of taxa to be compared across different samples. Microbial communities are frequently characterized by DNA sequencing. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups.
Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Multiple methods are used interchangeably for this purpose in the literature. Identifying differentially abundant microbes is a common goal of microbiome studies.
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