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 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
2013
Springer Nature B.V |
| Subjects: | |
| 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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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Conference-1 ObjectType-Feature-3 content type line 23 SourceType-Conference Papers & Proceedings-2 |
| ISSN: | 1471-2164 1471-2164 |
| DOI: | 10.1186/1471-2164-14-S8-S2 |