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Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest

View ORCID ProfilePaul F. Harrison, Andrew D. Pattison, David R. Powell, Traude H. Beilharz
doi: https://doi.org/10.1101/343145
Paul F. Harrison
1Monash Bioinformatics Platform, Monash University
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Andrew D. Pattison
2Monash Biomedicine Discovery Institute, Monash University
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David R. Powell
1Monash Bioinformatics Platform, Monash University
2Monash Biomedicine Discovery Institute, Monash University
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Traude H. Beilharz
2Monash Biomedicine Discovery Institute, Monash University
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Abstract

Background A differential gene expression analysis may produce a set of significantly differentially expressed genes that is too large to easily investigate, so that a means of ranking genes by their biological interest level is desirable. The life-sciences have grappled with the abuse of p-values to rank genes for this purpose. As an alternative, a lower confidence bound on the magnitude of Log Fold Change (LFC) could be used to rank genes, but it has been unclear how to reconcile this with the need to perform False Discovery Rate (FDR) correction. The TREAT test of McCarthy and Smyth is a step in this direction, finding genes significantly exceeding a specified LFC threshold. Here we describe the use of test inversion on TREAT to present genes ranked by a confidence bound on the LFC, while still controlling FDR.

Results Testing the Topconfects R package with simulated gene expression data shows the method outperforming current statistical approaches across a wide range of experiment sizes in the identification of genes with largest LFCs. Applying the method to a TCGA breast cancer data-set shows the method ranks some genes with large LFC higher than would traditional ranking by p-value. Importantly these two ranking methods lead to a different biological emphasis, in terms both of specific highly ranked genes and gene-set enrichment.

Conclusions The choice of ranking method in differential expression analysis can affect the biological interpretation. The common default of ranking by p-value is implicitly by an effect size in which each gene is standardized to its own variability, rather than comparing genes on a common scale, which may not be appropriate. The Topconfects approach of presenting genes ranked by confident LFC effect size is a variation on the TREAT method with improved usability, removing the need to fine-tune a threshold parameter and removing the temptation to abuse p-values as a de-facto effect size.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 11, 2018.
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Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest
Paul F. Harrison, Andrew D. Pattison, David R. Powell, Traude H. Beilharz
bioRxiv 343145; doi: https://doi.org/10.1101/343145
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Topconfects: a package for confident effect sizes in differential expression analysis provides improved usability ranking genes of interest
Paul F. Harrison, Andrew D. Pattison, David R. Powell, Traude H. Beilharz
bioRxiv 343145; doi: https://doi.org/10.1101/343145

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