Buratin Alessia, Romualdi Chiara, Bortoluzzi Stefania, Gaffo Enrico
Department of Molecular Medicine, University of Padova, Padova, Italy.
Department of Biology, University of Padova, Padova, Italy.
Comput Struct Biotechnol J. 2022 May 20;20:2495-2502. doi: 10.1016/j.csbj.2022.05.026. eCollection 2022.
Finding differentially expressed circular RNAs (circRNAs) is instrumental to understanding the molecular basis of phenotypic variation between conditions linked to circRNA-involving mechanisms. To date, several methods have been developed to identify circRNAs, and combining multiple tools is becoming an established approach to improve the detection rate and robustness of results in circRNA studies. However, when using a consensus strategy, it is unclear how circRNA expression estimates should be considered and integrated into downstream analysis, such as differential expression assessment. This work presents a novel solution to test circRNA differential expression using quantifications of multiple algorithms simultaneously. Our approach analyzes multiple tools' circRNA abundance count data within a single framework by leveraging generalized linear mixed models (GLMM), which account for the sample correlation structure within and between the quantification tools. We compared the GLMM approach with three widely used differential expression models, showing its higher sensitivity in detecting and efficiently ranking significant differentially expressed circRNAs. Our strategy is the first to consider combined estimates of multiple circRNA quantification methods, and we propose it as a powerful model to improve circRNA differential expression analysis.
发现差异表达的环状RNA（circRNA）有助于理解与circRNA相关机制的不同条件之间表型变异的分子基础。迄今为止，已经开发了几种方法来鉴定circRNA，并且结合多种工具正成为提高circRNA研究中检测率和结果稳健性的既定方法。然而，在使用共识策略时，尚不清楚应如何考虑circRNA表达估计值并将其整合到下游分析中，例如差异表达评估。这项工作提出了一种新颖的解决方案，可同时使用多种算法的定量结果来测试circRNA的差异表达。我们的方法通过利用广义线性混合模型（GLMM）在单个框架内分析多种工具的circRNA丰度计数数据，该模型考虑了定量工具内部和之间的样本相关结构。我们将GLMM方法与三种广泛使用的差异表达模型进行了比较，结果表明其在检测和有效排名显著差异表达的circRNA方面具有更高的灵敏度。我们的策略是首次考虑多种circRNA定量方法的联合估计，并且我们将其作为一种强大的模型来改进circRNA差异表达分析。
