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Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

GSE49712 Homo sapiens Expression profiling by high throughput sequencing; Third-party reanalysis 10 samples Submitted 2013/08/20 Platform GPL11154
Summary
A large number of computational methods have been recently developed for analyzing differential gene expression (DE) in RNA-seq data. We report on a comprehensive evaluation of the commonly used DE methods using the SEQC benchmark data set and data from ENCODE project. We evaluated a number of key features including: normalization, accuracy of DE detection and DE analysis when one condition has no detectable expression. We found significant differences among the methods. Furthermore, computational methods designed for DE detection from expression array data perform comparably to methods customized for RNA-seq. Most importantly, our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
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Also filed as BioProject PRJNA214799 and SRA study SRP028705. Searching any of these in the dataset finder brings you back here.

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