Hey @user123!
Some (delayed) thoughts:
The type of heterogeneity you expect should dictate the way you set the thresholds. Namely, if you expect compositional heterogeneity, then a fixed threshold will be more appropriate since density will ‘appear’ and ‘disappear’ throughout the sequence. For heterogeneity caused by motion/flexibility, a normalized threshold will ensure that the total density will be conserved across each ‘frame’ of the sequence. In the 3DVA paper (Punjani & Fleet, 2021), the thresholds were set for each of the target datasets given the considerations above.
Although the 3D variability components represent the variability in the dataset as a whole, a particular frame along a component axis does not necessarily correspond to individual particles observed on micrographs. We recommend that you validate the heterogeneity you deduce from the ‘simple’ 3DVA display mode in other ways, such as:
- masked 3D classification
- ‘intermediates’ mode 3DVA display, which will back-project intermediate volumes along a component using extant particles.
Finally, cryo-EM-based 3DVA models could also be supplemented with experiments independent of the cryo-EM data. For example, we’ve seen verification by hydrogen-deuterium exchange mass spectrometry (Josephs et al. (2021)).
