ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq

By Andrew D. Fernandes, Jean M. Macklaim, T. Linn, Gregor Reid, G. Gloor. PLoS ONE, 2013

758 citations recorded by the source corpus.

Experimental variance is a major challenge when dealing with high-throughput sequencing data. This variance has several sources: sampling replication, technical replication, variability within biological conditions, and variability between biological conditions. The high per-sample cost of RNA-Seq often precludes the large number of experiments needed to partition observed variance into these categories as per standard ANOVA models. We show that the partitioning of within-condition to between-condition variation cannot reasonably be ignored, whether in single-organism RNA-Seq or in Meta-RNA-Seq experiments, and further find that commonly-used RNA-Seq analysis tools, as described in the literature, do not enforce the constraint that the sum of relative expression levels must be one, and thus report expression levels that are systematically distorted. These two factors lead to misleading inferences if not properly accommodated. As it is usually only the biological between-condition and within-condition differences that are of interest, we developed ALDEx, an ANOVA-like differential expression procedure, to identify genes with greater between- to within-condition differences. We show that the presence of differential expression and the magnitude of these comparative differences can be reasonably estimated with even very small sample sizes.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d3b32a34883c345bcb1ec74224784d860824ca49

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In Molecular Biology

1. Jean M. Macklaim
2. G. Gloor
3. Vera Pawlowsky-Glahn
4. Juan José Egozcue
5. Andrew D. Fernandes

Citations in the corpus

• Using clusterProfiler to characterize multiomics data (2024, neutral): “49).”
• Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014, methodology): “These transformations have been shown to increase resolution and allow more robust data interpretations in fields as diverse as paleontology [15], environmental sciences [14], metabolomics of wine [13], meta-transcriptomics [8] and 16S rRNA gene tag sequencing [9].”
• Microbiome Datasets Are Compositional: And This Is Not Optional (2017, methodology): “…since this accounts for the precision of the estimate of the probabilities for each feature; we refer advanced readers to the more technical literature (Jaynes and Bretthorst, 2003; Fernandes et al., 2013; Gloor et al., 2016a) and the “Probability” section of the Supplement for more information.”