Abstract
BackgroundTesting for differential abundance of microbes in disease is a common practice in microbiome studies. Numerous differential abundance (DA) testing methods exist and range from traditional statistical tests to methods designed for microbiome data. Comparison studies of DA testing methods have been performed, but none performed on microbiome datasets collected for the study of real, complex disease. Due to this, DA testing was performed here using various DA methods in two large, uniformly collected gut microbiome datasets on Parkinson disease (PD), and their results compared.ResultsOverall, 78–92% of taxa tested were detected as differentially abundant by at least one method, while 5–22% were called differentially abundant by the majority of methods (depending on dataset and filtering of taxonomic data prior to testing). Concordances between method results ranged from 1 to 100%. Average concordance for datasets 1 and 2 were 24% and 28% respectively, and 27% for replicated DA signatures. Concordances increased when removing rarer taxa before testing, increasing average concordances by 2–32%. Certain methods consistently resulted in higher concordances (e.g. ANCOM-BC, LEfSe), while others consistently resulted in lower (e.g. edgeR, fitZIG). Hierarchical clustering revealed three groups of DA signatures that were (1) replicated by the majority of methods on average and included taxa previously associated with PD, (2) replicated by a subset of methods and included taxa largely enriched in PD, and (3) replicated by few to one method(s).ConclusionsDifferential abundance tests yielded varied concordances, and amounts of detected DA signatures. Some methods were more concordant than others on both filtered and unfiltered data, therefore, if consistency with other study methodology is a key goal, one might choose among these methods. Even still, using one method on one dataset may find true associations, but may also detect false positives. To help lower false positives, one might analyze data with two or more DA methods to gauge concordance, and use a built-in replication dataset. This study will hopefully serve to complement previously reported DA method comparison studies by implementing and coalescing a large number of both previously and yet to be compared methods on two real gut microbiome datasets.
Highlights
Testing for differential abundance of microbes in disease is a common practice in microbiome studies
The differential abundance (DA) methods compared in this study included Analysis of compositions of microbiomes (ANCOM) [6], ANCOM with bias correction (ANCOM-BC) [7], ALDEx2 [8], baySeq [9], DESeq2 nbinomWald test [10], edgeR exactTest using relative log expression (RLE) or trimmed mean of M-values (TMM) [11], generalized linear model (GLM), Kruskal–Wallis rank-sum test [12], Linear discriminant analysis Effect Size (LEfSe) [13], limma-voom [14], metagenomeSeq’s fitFeatureModel and fitZIG [15, 16], negative binomial GLM with or without zero-inflation (GLM Negative binomial zero-inflated (NBZI)), SAMseq [17], and Welch’s t-test [18]
No data transformations were performed for negative binomial methods, or metagenomeSeq methods, to try and bring the data to normality as non-normality of data is taken into account in their statistical models
Summary
Testing for differential abundance of microbes in disease is a common practice in microbiome studies. Numerous differential abundance (DA) testing methods exist and range from traditional statistical tests to methods designed for microbiome data. DA testing was performed here using various DA methods in two large, uniformly collected gut microbiome datasets on Parkinson disease (PD), and their results compared. Microbiome research has gained immense traction in recent years driven primarily by technological advances in sequencing and exponential increase in computational resources and tools The availability of these new tools and technologies have solidified a place for microbiome research in many fields including the biomedical research community where a large portion of the research effort is targeted at the gut microbiome [1]. An example of how DA testing methods compare to one another when performed on real, complex disease gut microbiome datasets is still lacking in the literature. Not all methods included in previous comparison studies have been compared side by side as each study compared few to several methods at a time with slight differences in what methods were included in their assessments and comparisons
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