JSM 2011 Online Program

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Abstract Details

Activity Number: 507
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #302733
Title: An Integrative Approach to Comparing and Normalizing Gene Expression Data Generated from RNA-Seq, Microarray, and RT-PCR Technologies
Author(s): Zhaonan Sun*+ and Yu Zhu
Companies: Purdue University and Purdue University
Address: Department of Statistics, West Lafayette, IN, 47907-2066,
Keywords: RNA-Seq ; Microarray ; Gene expression level ; Normalization
Abstract:

DNA microarray and RNA-seq both provide genome-wise expression level measurements, in many comparison studies, data from the same biological cell line are generated from each platform. Usually the gene expression levels are calculated from each platform separately, then correlation coefficients across replicates is used to assess the reproducibility of each platform. We argue that in the presence of technical and biological variations, correlation coefficient can be biased. In the comparison of accuracy of these platforms, qRT-PCR data is often regarded as the gold standard, correlation coefficients with qRT-PCR indicate the accuracy, and in differential expression (DE) studies, error rates are assessed by comparing DE result to that from qRT-PCR data. Since qRT-PCR itself also suffers from variation, ignoring this will also lead to bias. In this study we propose an integrative method, we combine data from different platforms together to get a better estimation for the expression level, then base on this we make the between-platform comparison in a statistically sound way. Our method is also applicable to differential expression study.


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