期刊
GENOME BIOLOGY
卷 14, 期 9, 页码 -出版社
BMC
DOI: 10.1186/gb-2013-14-9-r95
关键词
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资金
- Starr foundation
- DeGregorio Family foundation
- MSKCC Comprehensive Cancer Center [P30 CA008748]
- Susan and Peter Solomon Divisional Genomics Program
- MSKCC SPORE in Prostate Cancer [P50 CA091629]
- SPORE in Soft Tissue Sarcoma [P50 CA140146]
- [2P01CA129243-06]
- NATIONAL CANCER INSTITUTE [P01CA129243, P30CA008748, P50CA140146] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R01NS076465] Funding Source: NIH RePORTER
A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
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