Hi @vperetroukhin,

Thanks so much for the elaborated explanation as the response given by @DanielAsarnow. Your reply and also the video tutorial was also helpful to understand the paper conceptually better.

With that, I understand your statement below and agree with it.

*> Thus, it is not fair to say that the most ‘most dominant/variable motion’ (i.e., one that the structure actually undergoes) is defined by the first variation component.*

But I was trying to interpret the scatter plot (attached here again) to tell the variability, not necessarily by order of component 0 and component 1 and so on.

And the statement below is what I found from the 3DVA tutorial and sounds very similar to what I understood in the paper.

*> (3DVA tutorial) …The figure below shows the reaction coordinate distribution of particles, as scatter plots between adjacent pairs of components (0 vs 1, 1 vs 2, 2 vs 3, etc). ***Clearly, the first component (0) has the most variability**.

https://guide.cryosparc.com/processing-data/tutorials-and-case-studies/tutorial-3d-variability-analysis-part-one

If I understand correctly, **running 3DVA with K=3 will yield the top three variability components that explain the most variability in the data**, and one could think that **the component showing more (or the most) large variance explains the most variability in the data**.

I wonder if one could help me to interpret the scatter plot (attache above) and please correct me if my interpretation; *component 0 in the attached scatter plot has the most variability*, is wrong.

Best regards,

Min Woo Sung