Testing significance relative to a fold-change threshold is a TREAT

By Davis J. McCarthy, Gordon K. Smyth. Bioinform., 2009

685 citations recorded by the source corpus.

Motivation: Statistical methods are used to test for the differential expression of genes in microarray experiments. The most widely used methods successfully test whether the true differential expression is different from zero, but give no assurance that the differences found are large enough to be biologically meaningful. Results: We present a method, t-tests relative to a threshold (TREAT), that allows researchers to test formally the hypothesis (with associated p-values) that the differential expression in a microarray experiment is greater than a given (biologically meaningful) threshold. We have evaluated the method using simulated data, a dataset from a quality control experiment for microarrays and data from a biological experiment investigating histone deacetylase inhibitors. When the magnitude of differential expression is taken into account, TREAT improves upon the false discovery rate of existing methods and identifies more biologically relevant genes. Availability: R code implementing our methods is contributed to the software package limma available at http://www.bioconductor.org. Contact: smyth@wehi.edu.au

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/daf8ebed8b037b104b8cd36a878dde8da31c6bc5

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In Molecular Biology

Citations in the corpus

Pooling across cells to normalize single-cell RNA sequencing data with many zero counts (2016, methodology): “The glmTreat function was used to detect genes with a DE log-fold change significantly greater than 1 between the groups [24].”
From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline (2016, methodology): “This function is analogous to the TREAT method for microarrays [23] but is adapted to the NB framework.”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, neutral): “We note that related approaches to generate gene lists that satisfy both statistical and biological significance criteria have been previously discussed for microarray data [22] and recently for sequencing data [18].”
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences (2018, background): “However, adjusted P-values or log odds do not capture the magnitude of LFCs, unless the standard null of LFC 1⁄4 0 is replaced by a threshold test (Love et al., 2014; McCarthy and Smyth, 2009), wherein a positive threshold of biological importance is specified by the analyst.”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, positive): “To assess differential expression relative to a threshold in a statistically rigorous way, the TREAT method was developed under the limma EB framework [ 23 , 55 ].”
Fold change and p-value cutoffs significantly alter microarray interpretations (2012, background): “Fold change suggests more meaningful insight to the organism throughout development and into adulthood [11] with 1.”
ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION (2016, methodology): “Our robustified EB procedure has been implemented in the limma software package and can be invoked by the option robust=TRUE in calls to the eBayes or treat (McCarthy and Smyth, 2009) functions.”
limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, background): “When one has a particular cut-off for log-fold-change in mind, the treat function can be used to test whether the log2fold-change is greater than a threshold rather than merely different to zero (45).”
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015, methodology): “In situations where there is a need to further refine the list of genes, we recommend the user apply a test for a given FC cut-off using the TREAT method (16).”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “To address these practical and theoretical concerns, several tools have adopted tests relative to a LFC threshold, a procedure initially proposed for microarray data [155] .”