Comments (2)
One more question, about the synergy score calculation:cinamot.synergy
seems to take one of the treatments as a baseline for AB instead of taking the same no stimulation baseline as the others. I might be missing something—does this expression simplify to the synergy score described in the paper and implemented in the tutorial?
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Thank you for using the package! Sorry I somehow lost the Github notification on my email. For the first question, if we use expr_label as "No stimulation" and ref label as "IFNb", then the logic would be right and consistent with the manuscript. The only problem here is the names "expr_label" and "ref_label" are counterintuitive because of historical issues. I am not sure whether I should flip it in next versions as it can cause obvious conflicts. This is fixed in the pertpy version.
For the next question, our implemented approach in the code indeed can be simplified into the synergy score defined in the main text. Specifically, we compute 2 quantities: effect of AB minus B; effects of A minus baseline. If we project them in the same space (by optimal transport from B space to baseline space), then by a re-combination we get: (AB-B) - (A-0) = (AB-0) - (A-0) - (B-0).
Sorry again for the late reply and I would be happy to answer further questions.
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Related Issues (10)
- Can't import cinemaot module after pip install from git HOT 1
- Individual treatment effect matrices HOT 4
- install error HOT 1
- module 'cinemaot' has no attribute 'utils' HOT 2
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