Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis

By Andrew D. Fernandes, J. N. Reid, Jean M. Macklaim, T. A. McMurrough, David R. Edgell, G. Gloor. Microbiome, 2014

1,153 citations recorded by the source corpus.

Experimental designs that take advantage of high-throughput sequencing to generate datasets include RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), sequencing of 16S rRNA gene fragments, metagenomic analysis and selective growth experiments. In each case the underlying data are similar and are composed of counts of sequencing reads mapped to a large number of features in each sample. Despite this underlying similarity, the data analysis methods used for these experimental designs are all different, and do not translate across experiments. Alternative methods have been developed in the physical and geological sciences that treat similar data as compositions. Compositional data analysis methods transform the data to relative abundances with the result that the analyses are more robust and reproducible. Data from an in vitro selective growth experiment, an RNA-seq experiment and the Human Microbiome Project 16S rRNA gene abundance dataset were examined by ALDEx2, a compositional data analysis tool that uses Bayesian methods to infer technical and statistical error. The ALDEx2 approach is shown to be suitable for all three types of data: it correctly identifies both the direction and differential abundance of features in the differential growth experiment, it identifies a substantially similar set of differentially expressed genes in the RNA-seq dataset as the leading tools and it identifies as differential the taxa that distinguish the tongue dorsum and buccal mucosa in the Human Microbiome Project dataset. The design of ALDEx2 reduces the number of false positive identifications that result from datasets composed of many features in few samples. Statistical analysis of high-throughput sequencing datasets composed of per feature counts showed that the ALDEx2 R package is a simple and robust tool, which can be applied to RNA-seq, 16S rRNA gene sequencing and differential growth datasets, and by extension to other techniques that use a similar approach.

Subfields: Artificial Intelligence, Molecular Biology, Periodontics

View Paper: https://www.semanticscholar.org/paper/437a3e209c99c7a3f90b57e4476596cc92382466

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

In Periodontics

Citations in the corpus

LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, methodology): “Numerous differential abundance analysis methods have been proposed focusing on addressing the compositional effects [13, 15–17, 19, 20, 22– 24].”
Normalization and microbial differential abundance strategies depend upon data characteristics (2017, methodology): “This agrees with prior investigation finding RNA-Seq approaches unsuitable for microbiome data [60].”
Microbiome Datasets Are Compositional: And This Is Not Optional (2017, methodology): “…extents, without restriction, to any number of taxa, and to any fixed capacity instrument (Aitchison, 1986; Lovell et al., 2011; Friedman and Alm, 2012; Fernandes et al., 2013, 2014; Lovell et al., 2015; Mandal et al., 2015; Gloor et al., 2016a,b; Gloor and Reid, 2016; Tsilimigras and Fodor, 2016).”
Soil microbial communities in cucumber monoculture and rotation systems and their feedback effects on cucumber seedling growth (2017, methodology): “For beta diversity analysis, read counts were centered log-ratio (CLR) transformed (Fernandes et al. 2014) with the 'rgr' package in 'R' (Oksanen et al. 2014).”
QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data (2020, background): “…erroneous assumptions, these methods cannot control false-positive rates (Hawinkel, Mattiello, Bijnens, & Thas, 2019; Morton et al., 2017) and contribute to irre-producibility (Fernandes et al., 2014; Gloor, Macklaim, Pawlowsky-Glahn, & Egozcue, 2017; Gloor, Wu, Pawlowsky-Glahn, & Egozcue, 2016).”