Abstract:
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Identification of genes with differentially expressed profiles in follow-up RNAseq experiments is crucial for understanding the transcriptional regulatory network. Experiments may involve samples repeatedly sequenced at a couple or even more occasions, and the number of samples can vary from a handful of patients assigned to two or more experimental conditions to hundreds of patients. Depending on the experimental design, several complications may arise. Existing software for RNAseq experiments cannot be successfully used in all cases. They may be limited to the analysis of single or at most paired measurements and testing can preserve good statistical properties only in small sample designs. For longer follow-up designs, time-dependent over-dispersion and within samples serial correlation may complicate the statistical analysis. Common mixed-effects models can be computationally intensive and fail to converge. In this talk we will provide an overview of state-of-the-art methods and present a recently developed approach which pairs methods for mixed-effects models with empirical Bayes methodology to stabilize estimation of differential gene expression over time.
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