ArticleBMC genomics2019
Stability of methods for differential expression analysis of RNA-seq data.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- The Impact of Proinflammatory M1 Macrophages on the Proliferation and Expression of Cyclin E2, Mitogen-Activated Protein Kinases 4 and 7 in Hepatocytes Isolated from a Diethylnitrosamine-Induced Hepatocellular Carcinoma Rat Model.Molecules (Basel, Switzerland) · 2025Article
- Associations of ANGPT2 expression and its variants (rs1868554 and rs7825407) with multiple myeloma risk and outcome.Frontiers in oncology · 2025Article
- Comparative transcriptome profiling reveals distinct regulatory responses of secondary defensive metabolism inEcology and evolution · 2024Article
- Critical Differential Expression Assessment for Individual Bulk RNA-Seq Projects.bioRxiv : the preprint server for biology · 2024Article
- Article
- Association of Chromosome 17 Aneuploidy,Cancers · 2023Article
- A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias.PLoS biology · 2023Article
- Robustness of differential gene expression analysis of RNA-seq.Computational and structural biotechnology journal · 2021Article
- consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction.PeerJ · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundAs RNA-seq becomes the assay of choice for measuring gene expression levels, differential expression analysis has received extensive attentions of researchers. To date, for the evaluation of DE methods, most attention has been paid on validity. Yet another important aspect of DE methods, stability, is overlooked and has not been studied to the best of our knowledge.
resultsIn this study, we empirically show the need of assessing stability of DE methods and propose a stability metric, called Area Under the Correlation curve (AUCOR), that generates the perturbed datasets by a mixture distribution and combines the information of similarities between sets of selected features from these perturbed datasets and the original dataset.
conclusionEmpirical results support that AUCOR can effectively rank the DE methods in terms of stability for given RNA-seq datasets. In addition, we explore how biological or technical factors from experiments and data analysis affect the stability of DE methods. AUCOR is implemented in the open-source R package AUCOR, with source code freely available at https://github.com/linbingqing/stableDE .
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