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Activity Number: 499
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #313220
Title: DAFS: Data-Adaptive Flag Method for RNA-Sequencing Data
Author(s): Nysia George*+ and Ching-Wei Chang
Companies: FDA/National Center for Toxicological Research and FDA/National Center for Toxicological Research
Keywords: RNA-sequencing ; low expression ; mixture distribution ; data-adaptive
Abstract:

Next-generation sequencing (NGS) has advanced the application of high-throughput sequencing technologies in genomic variation analysis. Due to the dynamic range of expression levels, RNAseq is likely to detect lowly expressed transcripts. However, to date, there is no consensus on the definition of low expression. Since random variation is high in low expressed regions and distributions of transcript expression are influenced by numerous experimental factors, methods to differentiate classes of abundance levels in a sample are critical. Thus, a data-adaptive approach was developed to estimate the lower bound of high expression in RNAseq data. The Kolmgorov-Smirnov statistic and multivariate adaptive regression splines were used to determine the optimal cutoff value for separating high and low expression. Robustness of the proposed method was demonstrated by analyzing real and simulated data. We present the drawbacks of fitting RNAseq data with a two-component mixture distribution when classes of gene expression are not well separated. We show that our methodology, which accounts for the underlying data structure, demonstrates superior performance in preserving meaningful data.


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