Publications

Authors:

Calgaro, Matteo; Romualdi, Chiara; Waldron, Levi; Risso, Davide; Vitulo, Nicola

Title:

Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data

Year:

2020

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Referee:

No

Name of journal:

GENOME BIOLOGY

ISSN of journal:

1474-760X

N° Volume:

21

Number or Folder:

1

Page numbers:

191-221

Keyword:

Benchmark; Differential abundance; Metagenomics; Microbiome; Single-cell

Short description of contents:

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.ResultsWe 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, concordance, power, and correct identification of differentially abundant genera. 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.

Product ID:

117508

Handle IRIS:

11562/1029748

Last Modified:

January 30, 2025

Bibliographic citation:

Calgaro, Matteo; Romualdi, Chiara; Waldron, Levi; Risso, Davide; Vitulo, Nicola, Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data «GENOME BIOLOGY» , vol. 21 , n. 1 , 2020 , pp. 191-221

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