Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data
Matteo Calgaro; Chiara Romualdi; Levi Waldron; Davide Risso; Nicola Vitulo · 2020 · Genome biology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: The 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. RESULTS: We compare methods developed for single-cell and bulk RNA-seq, and specifically for microbiome data, in terms of suitability of distributional assumptions, ability to control false discoveries, concord
Abstract by Matteo Calgaro; Chiara Romualdi; Levi Waldron; Davide Risso; Nicola Vitulo, Genome biology (2020) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s13059-020-02104-1