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article: 10.1038/s41467-021-25960-2, Nature Communications, 2021
Main content
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establish the methodological framework -> to curate a resource of ground truth datasets
the matched bulk RNA and scRNA data with the same perturbation and sequenced in the same laboaratories. - The most popular methods that were benchmarked in this paper (grey bar).
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- Evaluations
- concordance between DE results in bulk versus scRNA-seq datasets using concordance curve (AUCC)
- concordance between GO results using the DEGs.
- split the control data into two samples randomly for test
- Results
- the pseudobulk methods outperform the specialized sc-DE methods, alse in the proteomics data, because they can account for the the intrinsic variability of biological replicates to generate the biologically accurate results.
- sc-DE methods tend to identify the highly expressed genes as the DEGs, even they remain unchanged.
- negative binomial generalized linear mixed models can generate accurate results but are time-consuming.
