Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
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, computation...
Provenance — who produced it, who reused it
Linked to 11 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Measure transcript integrity using RNA-seq data 2016 · 274 cites
- A comparison of per sample global scaling and per gene normaliza... 2017 · 98 cites
- Comprehensive evaluation of AmpliSeq transcriptome, a novel targ... 2015 · 92 cites
- ROTS: reproducible RNA-seq biomarker detector—prognostic markers... 2015 · 85 cites
6 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently