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Differential expression identification and false discovery rate estimation in RNA-Sequencing (RNA-Seq) dataExport this event to calendar

Friday, January 27, 2012 — 2:00 PM EST

Jun Li, Stanford University

RNA-Seq is taking place of microarrays and becoming the primary tool for measuring genome-wide transcript expression. We discuss the identification of features (genes, isoforms, exons, etc.) that are associated with an outcome in RNA-Seq and other sequencing-based comparative genomic experiments. That is, we aim to find features that are differentially expressed in samples in different biological conditions or under different disease statuses. RNA-Seq data take the form of counts, so models based on the normal distribution are generally unsuitable. The problem is especially challenging because different sequencing experiments may generate quite different total numbers of reads, or sequencing depths. Existing methods for this problem are based on Poisson or negative-binomial models: they are useful but can be heavily influenced by outliers in the data. We introduce a simple, non-parametric method with resampling to account for the different sequencing depths. The new method is more robust than parametric methods. It can be applied to data with quantitative, survival, two-class, or multiple-class outcomes. We compare our proposed method to Poisson and negative-binomial based methods in simulated and real data sets, and find that our method discovers more consistent patterns than competing methods.

Location 
M3 - Mathematics 3
3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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