1. Detecting differentially expressed circular RNAs from multiple quantification methods using a generalized linear mixed model
- Author
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Alessia Buratin, Chiara Romualdi, Stefania Bortoluzzi, and Enrico Gaffo
- Subjects
Differential Expression Models ,AUC ,Biophysics ,TPR ,Biochemistry ,FDR ,Differentially Expressed circRNAs ,Differential expression ,Structural Biology ,Genetics ,AUC, Area under the ROC curve ,Circular RNAs ,DECs, Differentially Expressed circRNAs ,DEMs, Differential Expression Models ,FDR, False Discovery Rate ,GLMM, Generalized Linear Mixed Model ,Generalized linear mixed models ,RNA-seq ,RNAseq, RNA sequencing ,TPR, True Positive Rate ,circRNAs ,circRNAs, circular RNAs ,Generalized Linear Mixed Model ,False Discovery Rate ,DEMs ,Area under the ROC curve ,RNA sequencing ,DECs ,RNAseq ,Computer Science Applications ,True Positive Rate ,GLMM ,Biotechnology - Abstract
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. more...
- Published
- 2022