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Activity Number: 469
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #306397
Title: Measuring Reproducibility of RNA-Seq Data Using a Copula Mixture Model for Grouped and Truncated Data
Author(s): Qunhua Li*+ and Peter Bickel
Companies: Penn State University and University of California at Berkeley
Address: 514D Wartik Lab, University Park, PA, 16802, United States
Keywords: copula ; mixture model ; grouped and truncated data ; EM algorithm ; RNA-seq data ; clustering

Reproducibility is essential to reliable scientific discovery in high-throughput experiments. Binning and truncation of data are common in high-throughput data, due to, for example, precision or detection limit of instruments or under-sampling in the experimental procedures. Li et al (2011) developed a unified approach to measure the reproducibility of findings identified from replicate experiments and identify putative discoveries using reproducibility. It overcomes several drawbacks of traditional correlation-based measures for assessing the reproducibility of high-throughput data. However, it encounters difficulties for grouped and truncated data. Here we extend the copula mixture model to grouped and truncated data and provide an estimation procedure. We illustrate our method on RNA-seq data.

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