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Activity Number: 186
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #311412
Title: Power Calculation in Candidate Marker Detection in RNA-Seq Experiment
Author(s): Ge Liao*+ and George Tseng
Companies: University of Pittsburgh and University of Pittsburgh
Keywords: RNA-seq ; power calculation ; mixture model ; sample size ; coverage
Abstract:

Although the experimental cost of Next Generation Sequencing (NGS) technology continues to drop rapidly, the high sequencing expenses and bioinformatic complexity will continue to be an obstacle. Modelling of NGS data not only involves sample size and genome-wide inference, but also includes sequencing depth and count data statistics. The optimal design is beyond one-dimensional dual problem between sample size versus statistical power. In this work, we propose a novel strategy to predict power of detecting differential expression (DE) in RNA-seq experiment under different sample size (n) and coverage(r) with a given pilot data. Instead of using summary statistics for power computation, we direct model p-value distribution by a mixture model, so that heterogeneity across genes could be maintained. To predict power of detection in a new sample size (N), a parametric bootstrap procedure is implemented, followed by 5-parameter log logistic curve fitting for expected discovery rate (EDR, or genome-wide power). To extend power prediction to other coverage, a downward sub-sampling scheme is implemented. We compare with three existing methods for evaluation.


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