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A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory

机译：使用外部刺入和最大似然理论对RNA-seq计数进行校准的完整统计模型

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摘要

A fundamental assumption, common to the vast majority of high-throughput transcriptome analyses, is that the expression of most genes is unchanged among samples and that total cellular RNA remains constant. As the number of analyzed experimental systems increases however, different independent studies demonstrate that this assumption is often violated. We present a calibration method using RNA spike-ins that allows for the measurement of absolute cellular abundance of RNA molecules. We apply the method to pooled RNA from cell populations of known sizes. For each transcript, we compute a nominal abundance that can be converted to absolute by dividing by a scale factor determined in separate experiments: the yield coefficient of the transcript relative to that of a reference spike-in measured with the same protocol. The method is derived by maximum likelihood theory in the context of a complete statistical model for sequencing counts contributed by cellular RNA and spike-ins. The counts are based on a sample from a fixed number of cells to which a fixed population of spike-in molecules has been added. We illustrate and evaluate the method with applications to two global expression data sets, one from the model eukaryote Saccharomyces cerevisiae, proliferating at different growth rates, and differentiating cardiopharyngeal cell lineages in the chordate Ciona robusta. We tested the method in a technical replicate dilution study, and in a k-fold validation study.
机译：绝大多数高通量转录组分析所共有的基本假设是，样品中大多数基因的表达未改变，而总细胞RNA则保持不变。然而，随着所分析的实验系统数量的增加，不同的独立研究表明，这一假设经常被违反。我们提出了一种使用RNA插入的校准方法，该方法可以测量RNA分子的绝对细胞丰度。我们将这种方法应用于从已知大小的细胞群体中提取的RNA。对于每个转录本，我们计算出一个标称丰度，该丰度可以通过除以在单独实验中确定的比例因子来转换为绝对值：转录本的产量系数相对于用相同协议测得的参考刺入的产量系数。该方法是通过最大似然理论在完整的统计模型的背景下得出的，该模型用于对细胞RNA和刺突贡献的测序计数。计数基于来自固定数量细胞的样本，其中已添加固定数量的刺入分子。我们举例说明并评估了该方法在两个全局表达数据集上的应用，其中一个来自模型真核生物酿酒酵母，以不同的增长率增殖，并区分了有脉Cionarobusta的心咽细胞谱系。我们在技术重复稀释研究和k倍验证研究中测试了该方法。

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