Evaluation of read count based RNAseq analysis methods

Background RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting r...

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Published in:BMC genomics Vol. 14; no. Suppl 8; p. S2
Main Authors: Guo, Yan, Li, Chung-I, Ye, Fei, Shyr, Yu
Format: Journal Article
Language:English
Published: London BioMed Central 2013
Springer Nature B.V
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ISSN:1471-2164, 1471-2164
Online Access:Get full text
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Summary:Background RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. To date, there has not been a consensus on the best approach for conducting robust RNAseq analysis. Results In this study, we designed a thorough experiment to evaluate six read count-based RNAseq analysis methods (DESeq, DEGseq, edgeR, NBPSeq, TSPM and baySeq) using both real and simulated data. We found the six methods produce similar fold changes and reasonable overlapping of differentially expressed genes based on p-values. However, all six methods suffer from over-sensitivity. Conclusions Based on the evaluation of runtime using real data and area under the receiver operating characteristic curve (AUC-ROC) using simulated data, we found that edgeR achieves a better balance between speed and accuracy than the other methods.
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ISSN:1471-2164
1471-2164
DOI:10.1186/1471-2164-14-S8-S2