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OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

By Felix Brechtmann, Agnė Matusevičiūtė, Christian Mertes, Vicente A. Yépez, Žiga Avsec, Maximilian Herzog, Daniel M Bader, Holger Prokisch, Julien Gagneur

Posted 24 May 2018
bioRxiv DOI: 10.1101/322149 (published DOI: 10.1016/j.ajhg.2018.10.025)

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. An obvious and 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 they 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 co-variation among genes 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 correction of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of correction for co-variation and of significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a data set, for identifying outlier samples with too many aberrantly expressed genes, and for the P-value-based detection of aberrant gene expression, with false discovery rate adjustment. Overall, OUTRIDER provides a computationally fast and scalable end-to-end solution for identifying aberrantly expressed genes, suitable for use by rare disease diagnostics platforms.

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