Journal Club

Comparing methods for differential expression

viktor.petukhov@pm.me

Viktor Petukhov,

Khodosevich group

Confronting false discoveries in single-cell differential expression

bioRxiv 2021.03.12.435024;

Jordan W. Squair, Matthieu Gautier, Claudia Kathe, Mark A. Anderson, Nicholas D. James, Thomas H. Hutson, Rémi Hudelle, Taha Qaiser, Kaya J. E. Matson, Quentin Barraud, Ariel J. Levine, Gioele La Manno, Michael A. Skinnider, Grégoire Courtine

https://doi.org/10.1101/2021.03.12.435024

"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

 

https://www.nxn.se/single-cell-studies

Evaluated methods

• Only DE across conditions, no DE between types
• 8 single-cell methods
• 6 pseudo-bulk methods

Recovering bulk results

What's wrong with single-cell methods?

Bias towards highly-expressed genes

What causes this bias?

What causes this bias?

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.

https://doi.org/10.1038/s41467-021-21038-1

Simulating false-positive rates

Simulating sensitivity

Conclusions

Single-cell-based methods fail even on a theoretical level