OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data
Brechtmann F, Mertes C, Matuseviciute A, Yepez VA, Avsec Z, Herzog M, Bader DM, Prokisch H, Gagneur J (2018)
Publication Type: Journal article
Publication year: 2018
Journal
Book Volume: 103
Pages Range: 907-917
Journal Issue: 6
DOI: 10.1016/j.ajhg.2018.10.025
Abstract
RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (Outlier in RNA-Seq Finder), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read-count expectations according to the gene covariation resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best recall of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of controlling for covariation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a dataset, for identifying outlier samples with too many aberrantly expressed genes, and for detecting aberrant gene expression on the basis of false-discovery-rate-adjusted p values. Overall, OUTRIDER provides an end-to-end solution for identifying aberrantly expressed genes and is suitable for use by rare-disease diagnostic platforms.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
Germany (DE)
Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich
Germany (DE)
How to cite
APA:
Brechtmann, F., Mertes, C., Matuseviciute, A., Yepez, V.A., Avsec, Z., Herzog, M.,... Gagneur, J. (2018). OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data. American Journal of Human Genetics, 103(6), 907-917. https://doi.org/10.1016/j.ajhg.2018.10.025
MLA:
Brechtmann, Felix, et al. "OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data." American Journal of Human Genetics 103.6 (2018): 907-917.
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