Journal Club
Comparing methods for differential expression
viktor.petukhov@pm.me
Viktor Petukhov,
Khodosevich group
"Ground-truth"
18 datasets
- matched bulk and scRNA-seq
- performed on the same population of purified cells
- exposed to the same perturbations
- sequenced in the same laboratories
Evaluated methods
A curated database reveals trends in single cell transcriptomics.
Valentine Svensson, Eduardo da Veiga Beltrame, & Lior Pachter
Evaluated methods
- Only DE across conditions, no DE between types
- 8 single-cell methods
- 6 pseudo-bulk methods
Recovering bulk results
DE
GO
cells stimulated with poly-I:C
What's wrong with single-cell methods?
Bias towards highly-expressed genes
Spike-in experiments
Correlation between expression and -log10(p-value)
False-positives
What causes this bias?
Shuffled aggregation
No aggregation
What causes this bias?
Shuffled aggregation
No aggregation
What causes this bias?
False-positives on simulated data
False-positives on simulated data
Human vs mouse data
∆(variance) = variance in biological replicates minus variance in pseudo-replicates
Conclusions
- Single-cell-based methods fail to capture inter-individual variability and should not be used
- edge-R is the most stable
- It's important to control for covariates in biological experiments
A practical solution to pseudoreplication bias in single-cell studies.
Nat Commun 12, 738 (2021).
Zimmerman, K.D., Espeland, M.A. & Langefeld, C.D.
Simulating false-positive rates
Simulating sensitivity
Conclusions
Single-cell-based methods fail even on a theoretical level