Assessment of single cell RNA-seq statistical methods on microbiome data

Calgaro, M.; Romualdi, C.; Waldron, L. D.; Risso, D.; Vitulo, N.

doi:10.1101/2020.01.15.907964

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bioRxiv · 10.1101/2020.01.15.907964

Assessment of single cell RNA-seq statistical methods on microbiome data

Calgaro, M.·Romualdi, C.·Waldron, L. D.·Risso, D.·Vitulo, N.

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Abstract

BackgroundThe correct identification of differentially abundant microbial taxa between experimental conditions is a methodological and computational challenge. Recent work has produced methods to deal with the high sparsity and compositionality characteristic of microbiome data, but independent benchmarks comparing these to alternatives developed for RNA-seq data analysis are lacking. ResultsHere, we compare methods developed for single cell, bulk RNA-seq, and microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concordance, and power. We benchmark these methods using 100 manually curated datasets from 16S and whole metagenome shotgun sequencing. ConclusionsThe multivariate and compositional methods developed specifically for microbiome analysis did not outperform univariate methods developed for differential expression analysis of RNA-seq data. We recommend a careful exploratory data analysis prior to application of any inferential model and we present a framework to help scientists make an informed choice of analysis methods in a dataset-specific manner.

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Calgaro, M., Romualdi, C., Waldron, L. D., Risso, D., Vitulo, N.. 2020-01-16. Assessment of single cell RNA-seq statistical methods on microbiome data. https://doi.org/10.1101/2020.01.15.907964

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