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

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.

An intragenic duplication of TRPS1 leading to abnormal transcripts and causing trichorhinophalangeal syndrome type I

“…3). An expression analysis using OUTRIDER (Brechtmann et al 2018) showed only a modest increase in expression for TRPS1 (0.38 log 2 fold change), most likely owing to the presence of the third copy of exons 2–4 (∼50% of TRPS1 nucleotides) being transcribed and leading to additional reads contributing to overall gene expression. However, the nonduplicated exons in TRPS1 support normal expression levels and suggest the abnormal transcript with the duplicated exons may be escaping nonsense-mediated decay (NMD) that would be predicted from the premature termination codons.…”

“…Specifically, genes whose 0.95 quantile FPKM was <1 or whose counts were 0 in more than one-quarter of the samples were excluded from the analysis. Outlier expression analysis was then performed using OUTRIDER (Brechtmann et al 2018) with the Benjamini–Hochberg correction for multiple comparisons, which provided confounder-corrected z -scores, log 2 fold changes, P -values, and adjusted P -values.…”

Benchmarking RNA-seq differential expression analysis methods using spike-in and simulation data

“…For moderately noisy data, both DESeq2 and edgeR.rb perform better than their original methods. Recently developed methods can help detect outlier counts in RNA-seq data [42,43].…”

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

“…fInDER) method that identifies "outlier" genes that are aberrantly expressed in the entire cohort (Brechtmann et al, 2018). OUTRIDER is based on the use of auto-encoders, a deep learning approach.…”