OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data
OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data
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DOI：
10.1016/j.ajhg.2018.10.025
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发表时间：
2018-12-06
影响因子：
9.8
通讯作者：
Gagneur, Julien
中科院分区：
文献类型：
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作者：
Brechtmann, Felix;Mertes, Christian;Gagneur, Julien
RNA测序（RNA-seq）作为基因组测序的补充检测方法，用于精确识别罕见疾病的分子原因，正越来越受欢迎。一种有效的方法是将异常基因表达水平鉴定为潜在的致病事件。然而，用于检测RNA-seq数据中异常读数计数的现有方法要么缺乏统计学显著性的评估，使得建立截止值是任意的，要么依赖于对混杂因素的主观手动校正。在这里，我们描述了OUTRIDER（RNA-Seq Finder中的离群值），这是一种为解决这些问题而开发的算法。该算法使用一个自动编码器，根据技术，环境或常见的遗传变异导致的基因协变来模拟读数计数预期。考虑到这些预期，假设RNA-seq读数计数遵循具有基因特异性分散的负二项分布。然后将离群值识别为显著偏离该分布的读段计数。该模型自动拟合，以实现人工损坏数据的最佳召回。使用模拟离群读数计数的精确-召回分析证明了控制协变和基于显著性的阈值的重要性。OUTRIDER是开源的，包括用于过滤数据集中未表达的基因的功能，用于识别具有太多异常表达基因的离群值样本，以及用于基于错误发现率调整的p值检测异常基因表达。总的来说，OUTRIDER为识别异常表达基因提供了端到端的解决方案，适用于罕见病诊断平台。
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.