OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data

Felix Brechtmann, Christian Mertes, Agnė Matusevičiūtė, Vicente A. Yépez, Žiga Avsec, Maximilian Herzog, Daniel M. Bader, Holger Prokisch, Julien Gagneur
2018 American Journal of Human Genetics  
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
more » ... scribe 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.
doi:10.1016/j.ajhg.2018.10.025 pmid:30503520 pmcid:PMC6288422 fatcat:7dkqmv3w5jc4xf323af3wcfqdq